Cuckoo Sandbox Book¶

Cuckoo Sandbox is an open source software for automating analysis of suspicious files. To do so it makes use of custom components that monitor the behavior of the malicious processes while running in an isolated environment.

This guide will explain how to set up Cuckoo, use it, and customize it.

Using the new Cuckoo Package?¶

There are various big improvements related to usability in the newly released Cuckoo Package. To get the most out of it, start reading on the different subjects related to it. Following are some of the highlights:

Having troubles?¶

If you’re having troubles you might want to check out the FAQ as it may already have the answers to your questions.

FAQ¶

Here you can find answers for various Frequently Asked Questions:

General Questions ¶

Can I analyze URLs with Cuckoo?¶

New in version 0.5: Native support for URL analysis was added to Cuckoo.

Changed in version 2.0-rc1: Cuckoo will not only start the browser (i.e., Internet Explorer) but will also attempt to actively instrument it in order to extract interesting results such as executed Javascript, iframe URLs, etc. See also our 2.0-rc1 blogpost.

Additional details on URL submissions is documented at Submit an Analysis, but it boils down to:

$ cuckoo submit --url http://www.example.com

Can I use Volatility with Cuckoo?¶

New in version 0.5: Cuckoo introduces support for optional full memory dumps, which are created at the end of the analysis process. You can use these memory dumps to perform additional memory forensic analysis with Volatility.

Please also consider that we don’t particularly encourage this: since Cuckoo employs some rootkit-like technologies to perform its operations, the results of a forensic analysis would be polluted by the sandbox’s components.

What do I need to use Cuckoo with VMware ESXi?¶

To run with VMware vSphere Hypervisor (or ESXi) Cuckoo leverages on libvirt or pyVmomi (the Python SDK for the VMware vSphere API). VMware API are used to take control over virtual machines, though these APIs are available only in the licensed version. In VMware vSphere free edition these APIs are read only, so you will be unable to use it with Cuckoo. For the minimum license needed, please have a look at VMware website.

Troubleshooting ¶

After upgrade Cuckoo stops to work ¶

Probably you upgraded it in a wrong way. It’s not a good practice to rewrite the files due to Cuckoo’s complexity and quick evolution.

Please follow the upgrade steps described in Upgrading from a previous release.

Cuckoo stumbles and produces some error I don’t understand ¶

Cuckoo is a mature but always evolving project, it’s possible that you encounter some problems while running it, but before you rush into sending emails to everyone make sure you read what follows.

Cuckoo is not meant to be a point-and-click tool: it’s designed to be a highly customizable and configurable solution for somewhat experienced users and malware analysts.

It requires you to have a decent understanding of your operating systems, Python, the concepts behind virtualization and sandboxing. We try to make it as easy to use as possible, but you have to keep in mind that it’s not a technology meant to be accessible to just anyone.

That being said, if a problem occurs you have to make sure that you did everything you could before asking for time and effort from our developers and users. We just can’t help everyone, we have limited time and it has to be dedicated to the development and fixing of actual bugs.

We have extensive documentation, read it carefully. You can’t just skip parts of it.

We have a Discussion page where you can find discussion platforms on which we’re frequently helping our users.

We have lot of users producing content on Internet, Google it.

Spend some of your own time trying fixing the issues before asking ours, you might even get to learn and understand Cuckoo better.

Long story short: use the existing resources, put some efforts into it and don’t abuse people.

If you still can’t figure out your problem, you can ask help on our online communities (see Final Remarks). Make sure when you ask for help to:

Use a clear and explicit title for your emails: “I have a problem”, “Help me” or “Cuckoo error” are NOT good titles.

Explain in details what you’re experiencing. Try to reproduce several times your issue and write down all steps to achieve that.

Use no-paste services and link your logs, configuration files and details on your setup.

Eventually provide a copy of the analysis that generated the problem.

Check and restore current snapshot with KVM ¶

If something goes wrong with virtual machine it’s best practice to check current snapshot status. You can do that with the following:

$ virsh snapshot-current "<Name of VM>"

If you got a long XML as output your current snapshot is configured and you can skip the rest of this chapter; anyway if you got an error like the following your current snapshot is broken:

$ virsh snapshot-current "<Name of VM>"
error: domain '<Name of VM>' has no current snapshot

To fix and create a current snapshot first list all machine’s snapshots:

$ virsh snapshot-list "<Name of VM>"
 Name                 Creation Time             State
 ------------------------------------------------------------
 1339506531           2012-06-12 15:08:51 +0200 running

Choose one snapshot name and set it as current:

$ snapshot-current "<Name of VM>" --snapshotname 1339506531
Snapshot 1339506531 set as current

Now the virtual machine state is fixed.

Check and restore current snapshot with VirtualBox ¶

If something goes wrong with virtual it’s best practice to check the virtual machine status and the current snapshot. First of all check the virtual machine status with the following:

$ VBoxManage showvminfo "<Name of VM>" | grep State
State:           powered off (since 2012-06-27T22:03:57.000000000)

If the state is “powered off” you can go ahead with the next check, if the state is “aborted” or something else you have to restore it to “powered off” before:

$ VBoxManage controlvm "<Name of VM>" poweroff

With the following check the current snapshots state:

$ VBoxManage snapshot "<Name of VM>" list --details
Name: s1 (UUID: 90828a77-72f4-4a5e-b9d3-bb1fdd4cef5f)
Name: s2 (UUID: 97838e37-9ca4-4194-a041-5e9a40d6c205) *

If you have a snapshot marked with a star “*” your snapshot is ready, anyway you have to restore the current snapshot:

$ VBoxManage snapshot "<Name of VM>" restorecurrent

Unable to bind result server error ¶

At Cuckoo startup if you get an error message like this one:

2014-01-07 18:42:12,686 [root] CRITICAL: CuckooCriticalError: Unable to bind result server on 192.168.56.1:2042: [Errno 99] Cannot assign requested address

It means that Cuckoo is unable to start the result server on the IP address written in cuckoo.conf (or in machinery.conf if you are using the resultserver_ip option inside). This usually happen when you start Cuckoo without bringing up the virtual interface associated with the result server IP address. You can bring it up manually, it depends from one virtualization software to another, but if you don’t know how to do, a good trick is to manually start and stop an analysis virtual machine, this will bring virtual networking up.

In the case of VirtualBox the hostonly interface vboxnet0 can be created as follows:

# If the hostonly interface vboxnet0 does not exist already.
$ VBoxManage hostonlyif create

# Configure vboxnet0.
$ VBoxManage hostonlyif ipconfig vboxnet0 --ip 192.168.56.1 --netmask 255.255.255.0

Error during template rendering ¶

Changed in version 2.0-rc1.

In our 2.0-rc1 release a bug was introduced that looks as follows in the screenshot below. In order to resolve this issue in your local setup, please open the web/analysis/urls.py file and modify the 21st line by adding an underscore as follows:

-        "/(?P<ip>[\d\.]+)?/(?P<host>[a-zA-Z0-9-\.]+)?"
+        "/(?P<ip>[\d\.]+)?/(?P<host>[ a-zA-Z0-9-_\.]+)?"

The official fixes for this issue can be found in the following commits.

501 Unsupported Method (‘GET’)¶

Changed in version 2.0-rc1.

Since 2.0-rc1 Cuckoo supports both the legacy Cuckoo Agent as well as a new, REST API-based, Cuckoo Agent for communication between the Guest and the Host machine. The new Cuckoo Agent is an improved Agent in the sense that it also allows usage outside of Cuckoo. As an example, it is used extensively by VMCloak in order to automatically create, configure, and cloak Virtual Machines.

Now in order to determine whether the Cuckoo Host is talking to the legacy or new Cuckoo Agent it does a HTTP GET request to the root path (/). The legacy Cuckoo Agent, which is based on xmlrpc, doesn’t handle that specific route and therefore returns an error, 501 Unsupported method.

Having said that, the message is not actually an error, it is simply Cuckoo trying to determine to which version of the Cuckoo Agent it is talking.

Note

It should be noted that even though there is a new Cuckoo Agent available, backwards compatibility for the legacy Cuckoo Agent is still available and working properly.

Permission denied for tcpdump ¶

Changed in version 2.0.0.

With the new Cuckoo structure in-place all storage is now, by default, located in ~/.cuckoo, including the PCAP file, which will be stored at ~/.cuckoo/storage/analyses/task_id/dump.pcap. On Ubuntu with AppArmor enabled (default configuration) tcpdump doesn’t have write permission to dot-directories in $HOME, causing the permission denied message and preventing Cuckoo from capturing PCAP files.

One of the workaround is as follows - by installing AppArmor utilities and simply disabling the tcpdump AppArmor profile altogether (more appropriate solutions are welcome of course):

sudo apt-get install apparmor-utils
sudo aa-disable /usr/sbin/tcpdump

DistributionNotFound / No distribution matching the version..¶

Changed in version 2.0.0.

Installing Cuckoo through the Python package brings its own set of problems, namely that of outdated Python package management software. This FAQ entry targets the following issue..:

$ cuckoo
Traceback (most recent call last):
File "/usr/local/bin/cuckoo", line 5, in <module>
    from pkg_resources import load_entry_point
File "/usr/lib/python2.7/dist-packages/pkg_resources.py", line 2749, in <module>
    working_set = WorkingSet._build_master()
File "/usr/lib/python2.7/dist-packages/pkg_resources.py", line 446, in _build_master
    return cls._build_from_requirements(__requires__)
File "/usr/lib/python2.7/dist-packages/pkg_resources.py", line 459, in _build_from_requirements
    dists = ws.resolve(reqs, Environment())
File "/usr/lib/python2.7/dist-packages/pkg_resources.py", line 628, in resolve
    raise DistributionNotFound(req)
pkg_resources.DistributionNotFound: tlslite-ng==0.6.0a3

Those issues - and related ones - are caused by outdated Python package management software. Fortunately their fix is fairly trivial and therefore the following command should do the trick:

pip install -U pip setuptools

IOError: [Errno 24] Too many open files ¶

It is most certainly possible running into this issue when analyzing samples that have a lot of dropped files, so many that the Processing Utility can’t allocate any new file descriptors anymore.

The easiest workaround for this issue is to bump the soft and hard file descriptor limit for the current user. This may be done as documented in the following blogpost.

In case if you using Supervisor set minfds in supervisord.conf.

Remember that you have to login in to a new shell (i.e., usually logout first) session in order for the changes to take effect.

pkg_resources.ContextualVersionConflict ¶

In case you’re installing or upgrading the Cuckoo Package, it has happened before to people that they got an error much like the following:

pkg_resources.ContextualVersionConflict: (HTTPReplay 0.1.5
(/usr/local/lib/python2.7/dist-packages),
Requirement.parse('HTTPReplay==0.1.17'), set(['Cuckoo']))

Now this is quite odd, as generally speaking we’ve specifically requested pip to install all dependencies with their exact version (and in fact, if you look at pip freeze you’ll see the correct version), but it does happen sometimes that older versions of various libraries are still around.

The easiest way to resolve this issue is by uninstalling all versions of said dependency and reinstalling Cuckoo. In the case presented above, with HTTPReplay, this may look as follows:

$ sudo pip uninstall httpreplay
Uninstalling HTTPReplay-0.1.17:
/usr/local/bin/httpreplay
/usr/local/bin/pcap2mitm
/usr/local/lib/python2.7/dist-packages/HTTPReplay-0.1.17-py2.7.egg-info
...
Proceed (y/n)? y
Successfully uninstalled HTTPReplay-0.1.17

$ sudo pip uninstall httpreplay
Uninstalling HTTPReplay-0.1.5:
/usr/local/lib/python2.7/dist-packages/HTTPReplay-0.1.5-py2.7.egg-info
Proceed (y/n)? y
Successfully uninstalled HTTPReplay-0.1.5

$ sudo pip uninstall httpreplay
Cannot uninstall requirement httpreplay, not installed

Then reinstalling Cuckoo again is simply invoking pip install -U cuckoo or similar.

ValueError: incomplete format key ¶

This issue may appear at runtime after tinkering with settings in $CWD/conf, as input is passed to the configuration parser at runtime unescaped. Double-check your configuration files with an eye out for potentially troublesome character combinations such as %(.

Troubleshooting VM network configuration ¶

In case the network configuration of your Virtual Machine isn’t working as expected, you’ll be prompted with the message to resolve this issue as Cuckoo isn’t able to use it for analyses as-is. There are numerous possibilities as to why the network configuration and/or your setup are incorrect so please read our documentation once more. However, most often the issue lies within one of the following reasons:

The IP address of the VM has been configured incorrectly. Please verify that the VM has a static IP address, that it matches the one in the Cuckoo configuration, and that the configured network interface exists and is up. Also, in case of VirtualBox, did you configure the network interface to be a Host-Only interface?
Check that there are no firewalls in-place that hinder the communication between your Host and Guest and double check that the Host and Guest can ping each other as well as connect to each other.

If connections from the Cuckoo Host to the Guest work, but the other way around don’t, then some additional problems may be at hand:

Is the network configuration equivalent on the host and in the VM? If not, e.g., if the VM sees different IP ranges, then you’ll have to configure the resultserver_ip and resultserver_port, for which we have separate documentation.
If you’ve modified the Cuckoo Analyzer (located at $CWD/analyzer) this error message may indicate that a syntax error or other exception was introduced, preventing the Analyzer from being properly started, and thus not being able to perform the analysis as expected.

If you’ve triple-checked the above and are still experiencing issues, then please contact us through one of the various communication channels.

Cuckoo says there’s a version 2.1.0?¶

If you see the message Outdated! Cuckoo Sandbox version 2.1.0 is available now. and you’ve come to this FAQ entry then you’re entirely correct. There is indeed no version 2.1.0, yet (!). However, due to the logic implemented in the version checker of our 2.0-RC1 and 2.0-RC2 releases, the only way to inform our users about our latest releases is by having a “new” major version release (i.e., 2.1.0 or later). We’ve decided that it’s better to sling a little bit of confusion regarding a non-existing version than not mentioning any new versions to our users altogether. So please bear with us and install the latest version :-)

No handlers could be found for logger X in UWSGI log ¶

If you see this message, it means Cuckoo is throwing an error before its loggers are initialized. This might happen if database migration or CWD updates are required.

Start the development web server to see the error:

$ cuckoo web

Otherwise you can ask the developers and/or other Cuckoo users, see Join the discussion.

Contents¶

Introduction¶

This is an introductory chapter to Cuckoo Sandbox. It explains some basic malware analysis concepts, what’s Cuckoo and how it can fit in malware analysis.

Sandboxing¶

As defined by Wikipedia, “in computer security, a sandbox is a security mechanism for separating running programs. It is often used to execute untested code, or untrusted programs from unverified third-parties, suppliers, untrusted users and untrusted websites.”.

This concept applies to malware analysis’ sandboxing too: our goal is to run an unknown and untrusted application or file inside an isolated environment and get information on what it does.

Malware sandboxing is a practical application of the dynamical analysis approach: instead of statically analyzing the binary file, it gets executed and monitored in real-time.

This approach obviously has pros and cons, but it’s a valuable technique to obtain additional details on the malware, such as its network behavior. Therefore it’s a good practice to perform both static and dynamic analysis while inspecting a malware, in order to gain a deeper understanding of it.

Simple as it is, Cuckoo is a tool that allows you to perform sandboxed malware analysis.

Using a Sandbox¶

Before starting to install, configure and use Cuckoo, you should take some time to think on what you want to achieve with it and how.

Some questions you should ask yourself:

What kind of files do I want to analyze?

What volume of analyses do I want to be able to handle?

Which platform do I want to use to run my analysis on?

What kind of information I want about the file?

The creation of the isolated environment (for example a virtual machine) is probably the most critical and important part of a sandbox deployment: it should be done carefully and with proper planning.

Before getting hands on the virtualization product of your choice, you should already have a design plan that defines:

Which operating system, language and patching level to use.

Which software to install and which versions (particularly important when analyzing exploits).

Consider that automated malware analysis is not deterministic and its success might depend on a trillion of factors: you are trying to make a malware run in a virtualized system as it would do on a native one, which could be tricky to achieve and may not always succeed. Your goal should be both to create a system able to handle all the requirements you need as well as try to make it as realistic as possible.

For example you could consider leaving some intentional traces of normal usage, such as browsing history, cookies, documents, images etc. If a malware is designed to operate, manipulate or steal such files you’ll be able to notice it.

Virtualized operating systems usually carry a lot of traces with them that makes them very easily detectable. Even if you shouldn’t overestimate this problem, you might want to take care of this and try to hide as many virtualization traces as possible. There is a lot of literature on Internet regarding virtualization detection techniques and countermeasures.

Once you finished designing and preparing the prototype of system you want, you can proceed creating it and deploying it. You will be always in time to change things or slightly fix them, but remember that good planning at the beginning always means less troubles in the long run.

What is Cuckoo?¶

Cuckoo is an open source automated malware analysis system.

It’s used to automatically run and analyze files and collect comprehensive analysis results that outline what the malware does while running inside an isolated operating system.

It can retrieve the following type of results:

Traces of calls performed by all processes spawned by the malware.

Files being created, deleted and downloaded by the malware during its execution.

Memory dumps of the malware processes.

Network traffic trace in PCAP format.

Screenshots taken during the execution of the malware.

Full memory dumps of the machines.

Some History¶

Cuckoo Sandbox started as a Google Summer of Code project in 2010 within The Honeynet Project. It was originally designed and developed by Claudio “nex” Guarnieri, who is still the project leader and core developer.

After initial work during the summer 2010, the first beta release was published on Feb. 5th 2011, when Cuckoo was publicly announced and distributed for the first time.

In March 2011, Cuckoo has been selected again as a supported project during Google Summer of Code 2011 with The Honeynet Project, during which Dario Fernandes joined the project and extended its functionality.

On November 2nd 2011 Cuckoo the release of its 0.2 version to the public as the first real stable release. On late November 2011 Alessandro “jekil” Tanasi joined the team expanding Cuckoo’s processing and reporting functionality.

On December 2011 Cuckoo v0.3 gets released and quickly hits release 0.3.2 in early February.

In late January 2012 we opened Malwr.com, a free and public running Cuckoo Sandbox instance provided with a full fledged interface through which people can submit files to be analysed and get results back.

In March 2012 Cuckoo Sandbox wins the first round of the Magnificent7 program organized by Rapid7.

During the Summer of 2012 Jurriaan “skier” Bremer joined the development team, refactoring the Windows analysis component sensibly improving the analysis’ quality.

On 24th July 2012, Cuckoo Sandbox 0.4 is released.

On 20th December 2012, Cuckoo Sandbox 0.5 “To The End Of The World” is released.

On 15th April 2013 we released Cuckoo Sandbox 0.6, shortly after having launched the second version of Malwr.com.

On 1st August 2013 Claudio “nex” Guarnieri, Jurriaan “skier” Bremer and Mark “rep” Schloesser presented Mo’ Malware Mo’ Problems - Cuckoo Sandbox to the rescue at Black Hat Las Vegas.

On 9th January 2014, Cuckoo Sandbox 1.0 is released.

In March 2014 Cuckoo Foundation born as non-profit organization dedicated to growth of Cuckoo Sandbox and the surrounding projects and initiatives.

On 7th April 2014, Cuckoo Sandbox 1.1 is released.

On the 7th of October 2014, Cuckoo Sandbox 1.1.1 is released after a Critical Vulnerability had been disclosed by Robert Michel.

On the 4th of March 2015, Cuckoo Sandbox 1.2 has been released featuring a wide array of improvements regarding the usability of Cuckoo.

During summer 2015 Cuckoo Sandbox started the development of Mac OS X malware analysis as a Google Summer of Code project within The Honeynet Project. Dmitry Rodionov qualified for the project and developed a working analyzer for Mac OS X.

On the 21st of February 2016 version 2.0 Release Candidate 1 is released. This version ships with almost two years of combined effort into making Cuckoo Sandbox a better project for daily usage.

Use Cases¶

Cuckoo is designed to be used both as a standalone application as well as to be integrated in larger frameworks, thanks to its extremely modular design.

It can be used to analyze:

Generic Windows executables

DLL files

PDF documents

Microsoft Office documents

URLs and HTML files

PHP scripts

CPL files

Visual Basic (VB) scripts

ZIP files

Java JAR

Python files

Almost anything else

Thanks to its modularity and powerful scripting capabilities, there’s no limit to what you can achieve with Cuckoo.

For more information on customizing Cuckoo, see the Customization chapter.

Architecture¶

Cuckoo Sandbox consists of a central management software which handles sample execution and analysis.

Each analysis is launched in a fresh and isolated virtual or physical machine. The main components of Cuckoo’s infrastructure are an Host machine (the management software) and a number of Guest machines (virtual or physical machines for analysis).

The Host runs the core component of the sandbox that manages the whole analysis process, while the Guests are the isolated environments where the malware samples get actually safely executed and analyzed.

The following picture explains Cuckoo’s main architecture:

Obtaining Cuckoo¶

Deprecated since version 2.0-rc2: Although Cuckoo can still be downloaded from the website we discourage from doing so, given that simply installing it through pip is the preferred way to get Cuckoo. Please refer to Installing Cuckoo.

Cuckoo can be downloaded from the official website, where the stable and packaged releases are distributed, or can be cloned from our official git repository.

Warning

While being more updated, including new features and bugfixes, the version available in the git repository should be considered an under development stage. Therefore its stability is not guaranteed and it most likely lacks updated documentation.

License¶

Cuckoo Sandbox license is shipped with Cuckoo and contained in the “LICENSE” file inside the “docs” folder.

Disclaimer¶

Cuckoo is distributed as it is, in the hope that it will be useful, but without any warranty neither the implied merchantability or fitness for a particular purpose.

Whatever you do with this tool is uniquely your own responsibility.

Cuckoo Foundation¶

The Cuckoo Foundation is a non-profit organization incorporated as a Stichting in the Netherlands and it’s mainly dedicated to support of the development and growth of Cuckoo Sandbox, an open source malware analysis system, and the surrounding projects and initiatives.

The Foundation operates to secure financial and infrastructure support to our software projects and coordinates the development and contributions from the community.

Community guidelines¶

Cuckoo Sandbox is an open source project and we appreciate any form of contribution. These guidelines are meant to help you and us to answer questions, solve issues, and merge code as soon as we can. So, it is great that you are reading these guidelines! We will try to keep this as short as possible.

Introduction¶

These guidelines contain information on

What to include when creating issues for
- Reporting bugs/errors/unexpected behavior
- Feature suggestions/requests
Contributing code/documentation

We obviously want to fix, help with, and merge issues and contributions as fast as possible. To do this, we will likely ask some questions/post comments on your issue or pull request. We ask that you keep an eye on your issue/PR and try to answer questions we ask. Realise that it may take a while before we fix your issue or answer your question.

If after 60 days there is no progress in an issue or PR because of missing information, we may consider closing the issue. You are, of course, always welcome to re-open it in case additional information can be provided!

Creating issues¶

Issues.. Useful for many things. Bug/error/unexpected behavior reporting, asking questions, making suggestions/feature requests etc. When making any of these, it is very useful for us and you if you include the information listed here.

Reporting bugs, errors, and unexpected behavior¶

You notice a bug, see an error or behavior you did not expect and want to report it to us? That is great, thanks in advance! Before you report it, please see our FAQ. Common issues and their solutions are already mentioned here. You may also find a solution by searching existing issues.

You can also contact us using any of the methods mentioned at cuckoosandbox.org/discussion.

Now, if you do create an issue, it is very useful if you do and include the following information if you can and if it applies:

Use a descriptive issue title
Try to reproduce your issue
- How can we reproduce it?
What was the intended goal of your usage of Cuckoo Sandbox?
- Submitting a task, waiting for a result, adding a module etc.
Any information on your environment?
- Your Cuckoo Sandbox version
- The operating system the Cuckoo host is running on
- Parts of the configuration related to the error
- If you customized code, can you tell us what was customized?
What happened?
- Try to explain what happened in detail - this makes it possible for us to reproduce, confirm, and fix the issue.
- For errors etc, please include the log with this error. Preferably with a link to an online paste service.
- If you can, include a hash of the file being analyzed by Cuckoo.
What did you try to do so far?
- If you tried to do anything to fix it, please include what you have tried so far.

Feature requests/suggestions¶

You have thought of or would like to see a new feature in Cuckoo Sandbox. Maybe you have a suggestion to change something? Great! We would love to hear about it.

When creating a feature request/suggestion, include the following if it applies:

A descriptive issue title
What is your suggestion?
- What do you want to change/add?
What is the goal of this change/addition?
Do you have suggestions for the implementation?
- For example: using a specific library/package

Asking questions¶

Have a question about Cuckoo Sandbox? Maybe it has already been asked. Please see our FAQ and documentation first.

Did not find your answer? Feel free to contact us using any of the methods mentioned here, or by creating an issue.

Code and documentation contributions¶

You want to contribute by writing code or documentation? That is great, all help is appreciated! It is very easy to get started:

Fork our repository
Take a look at our development documentation for guidelines and tips
Make the changes that you want to contribute
Create a pull request

Testing¶

It is very important for us to keep Cuckoo Sandbox operational. This is why we only merge a contribution after we know it was tested and does not break anything. To unit test Cuckoo, we use Pytest. All existing tests for Cuckoo are located in the tests/ folder.

It would be appreciated if you did add a test to your contribution. This way, the correct operation of your contribution can be tested in the future.

Pull requests¶

When creating a pull request, please include the following:

What did you create/change?
What is the goal of this addition/change?
Did you test your addition/change?

Installation¶

This chapter explains how to install Cuckoo.

Although the recommended setup is GNU/Linux (Debian or Ubuntu preferably), Cuckoo has proved to work smoothly on Mac OS X and Microsoft Windows 7 as host as well. The recommended and tested setup for guests are Windows XP and 64-bit Windows 7 for Windows analysis, Mac OS X Yosemite for Mac OS X analysis, and Debian for Linux Analysis, although Cuckoo should work with other releases of guest Operating Systems as well.

Note

This documentation refers to Host as the underlying operating systems on which you are running Cuckoo (generally being a GNU/Linux distribution) and to Guest as the Windows virtual machine used to run the isolated analysis.

Preparing the Host¶

To run Cuckoo we suggest a GNU/Linux operating system. We’ll be using the latest Ubuntu LTS (16.04 at the time of writing) throughout our documentation.

Requirements¶

Before proceeding to installing and configuring Cuckoo, you’ll need to install some required software packages and libraries.

Installing Python libraries (on Ubuntu/Debian-based distributions)¶

The Cuckoo host components is completely written in Python, therefore it is required to have an appropriate version of Python installed. At this point we only fully support Python 2.7. Older version of Python and Python 3 versions are not supported by us (although Python 3 support is on our TODO list with a low priority).

The following software packages from the apt repositories are required to get Cuckoo to install and run properly:

$ sudo apt-get install python python-pip python-dev libffi-dev libssl-dev
$ sudo apt-get install python-virtualenv python-setuptools
$ sudo apt-get install libjpeg-dev zlib1g-dev swig

In order to use the Django-based Web Interface, MongoDB is required:

$ sudo apt-get install mongodb

In order to use PostgreSQL as database (our recommendation), PostgreSQL will have to be installed as well:

$ sudo apt-get install postgresql libpq-dev

Pydeep is an optional plugin that can be installed manually. A Link is provided for convenience: * pydeep install - note: the libfuzzy-dev package is required for

pydeep but at the time of writing, was not listed in the official documentation.

If you want to use KVM as machinery module you will have to install KVM:

$ sudo apt-get install qemu-kvm libvirt-bin ubuntu-vm-builder bridge-utils python-libvirt

If you want to use XenServer you’ll have to install the XenAPI Python package:

$ sudo pip install XenAPI

If you want to use the mitm auxiliary module (to intercept SSL/TLS generated traffic), you need to install mitmproxy. Please refer to its website for installation instructions. Please note that the latest version of mitmproxy requires Python 3.6 or higher and therefore it’s required to install it within a separate virtualenv to isolate it and its requirements from Cuckoo’s Python 2.7 environment. After installing mitmproxy in a separate virtualenv, include its binary path in the Cuckoo configuration, e.g., /tmp/mitmproxy3/bin/mitmdump if the virtualenv is /tmp/mitmproxy3.

Installing Python libraries (on Mac OS X)¶

This is mostly the same as the installation on Ubuntu/Debian, except that we’ll be using the brew package manager. Install all the required dependencies as follows (this list is WIP):

$ brew install libmagic cairo pango openssl

In addition to that you’ll also want to expose the openssl header files in the standard GCC/Clang include directory, so that yara-python may compile successfully. This can be done as follows:

$ cd /usr/local/include
$ ln -s ../opt/openssl/include/openssl .

Installing Python libraries (on Windows 7)¶

To be documented.

Virtualization Software¶

Cuckoo Sandbox supports most Virtualization Software solutions. As you will see throughout the documentation, Cuckoo has been setup to remain as modular as possible and in case integration with a piece of software is missing this could be easily added.

For the sake of this guide we will assume that you have VirtualBox installed (which is the default), but this does not affect the execution and general configuration of the sandbox.

You are completely responsible for the choice, configuration, and execution of your virtualization software. Please read our extensive documentation and FAQ before reaching out to us with questions on how to set Cuckoo up.

Assuming you decide to go for VirtualBox, you can get the proper package for your distribution at the official download page. Please find following the commands to install the latest version of VirtualBox on your Ubuntu LTS machine. Note that Cuckoo supports VirtualBox 4.3, 5.0, 5.1, and 5.2:

$ echo deb http://download.virtualbox.org/virtualbox/debian xenial contrib | sudo tee -a /etc/apt/sources.list.d/virtualbox.list
$ wget -q https://www.virtualbox.org/download/oracle_vbox_2016.asc -O- | sudo apt-key add -
$ sudo apt-get update
$ sudo apt-get install virtualbox-5.2

For more information on VirtualBox, please refer to the official documentation.

Installing tcpdump¶

In order to dump the network activity performed by the malware during execution, you’ll need a network sniffer properly configured to capture the traffic and dump it to a file.

By default Cuckoo adopts tcpdump, the prominent open source solution.

Install it on Ubuntu:

$ sudo apt-get install tcpdump apparmor-utils
$ sudo aa-disable /usr/sbin/tcpdump

Note that the AppArmor profile disabling (the aa-disable command) is only required when using the default CWD directory as AppArmor would otherwise prevent the creation of the actual PCAP files (see also Permission denied for tcpdump).

For Linux platforms with AppArmor disabled (e.g., Debian) the following command will suffice to install tcpdump:

$ sudo apt-get install tcpdump

Tcpdump requires root privileges, but since you don’t want Cuckoo to run as root you’ll have to set specific Linux capabilities to the binary:

$ sudo groupadd pcap
$ sudo usermod -a -G pcap cuckoo
$ sudo chgrp pcap /usr/sbin/tcpdump
$ sudo setcap cap_net_raw,cap_net_admin=eip /usr/sbin/tcpdump

You can verify the results of the last command with:

$ getcap /usr/sbin/tcpdump
/usr/sbin/tcpdump = cap_net_admin,cap_net_raw+eip

If you don’t have setcap installed you can get it with:

$ sudo apt-get install libcap2-bin

Or otherwise (not recommended) do:

$ sudo chmod +s /usr/sbin/tcpdump

Please keep in mind that even the setcap method is not perfectly safe (due to potential security vulnerabilities) if the system has other users which are potentially untrusted. We recommend to run Cuckoo on a dedicated system or a trusted environment where the privileged tcpdump execution is contained otherwise.

Installing Volatility¶

Volatility is an optional tool to do forensic analysis on memory dumps. In combination with Cuckoo, it can automatically provide additional visibility into deep modifications in the operating system as well as detect the presence of rootkit technology that escaped the monitoring domain of Cuckoo’s analyzer.

In order to function properly, Cuckoo requires at least version 2.3 of Volatility, but recommends the latest version, Volatility 2.5. You can download it from their official repository.

See the volatility documentation for detailed instructions on how to install it.

Installing M2Crypto¶

Currently the M2Crypto library is only supported when SWIG has been installed. On Ubuntu/Debian-like systems this may be done as follows:

$ sudo apt-get install swig

If SWIG is present on the system one may install M2Crypto as follows:

$ sudo pip install m2crypto==0.24.0

Installing guacd¶

guacd is an optional service that provides the translation layer for RDP, VNC, and SSH for the remote control functionality in the Cuckoo web interface.

Without it, remote control won’t work. Versions 0.9.9 and up will work, but we recommend installing the latest version. On an Ubuntu 17.04 machine the following command will install version 0.9.9-2:

$ sudo apt install libguac-client-rdp0 libguac-client-vnc0 libguac-client-ssh0 guacd

If you only want RDP support you can skip the installation of the libguac-client-vnc0 and libguac-client-ssh0 packages.

If you are using an older distribution or you just want to use the latest version (our recommendation), the following will build the latest version (0.9.14) from source:

$ sudo apt -y install libcairo2-dev libjpeg-turbo8-dev libpng-dev libossp-uuid-dev libfreerdp-dev
$ mkdir /tmp/guac-build && cd /tmp/guac-build
$ wget https://www.apache.org/dist/guacamole/0.9.14/source/guacamole-server-0.9.14.tar.gz
$ tar xvf guacamole-server-0.9.14.tar.gz && cd guacamole-server-0.9.14
$ ./configure --with-init-dir=/etc/init.d
$ make && sudo make install && cd ..
$ sudo ldconfig
$ sudo /etc/init.d/guacd start

When installing from source, make sure you don’t have another version of any of the libguac- libraries installed from your package manager or you might experience issues due to incompatibilities which can crash guacd.

Note that the VirtualBox Extension Pack must also be installed to take advantage of the Cuckoo Control functionality exposed by Guacamole.

Installing Cuckoo¶

Create a user¶

You can either run Cuckoo from your own user or create a new one dedicated just for your sandbox setup. Make sure that the user that runs Cuckoo is the same user that you will use to create and run the virtual machines (at least in the case of VirtualBox), otherwise Cuckoo won’t be able to identify and launch these Virtual Machines.

Create a new user:

$ sudo adduser cuckoo

If you’re using VirtualBox, make sure the new user belongs to the “vboxusers” group (or the group you used to run VirtualBox):

$ sudo usermod -a -G vboxusers cuckoo

If you’re using KVM or any other libvirt based module, make sure the new user belongs to the “libvirtd” group (or the group your Linux distribution uses to run libvirt):

$ sudo usermod -a -G libvirtd cuckoo

Raising file limits¶

As outlined in the FAQ entry IOError: [Errno 24] Too many open files one may want to bump the file count limits before starting Cuckoo as otherwise some samples will fail to properly process the report (due to opening more files than allowed by the Operating System).

Install Cuckoo¶

Installing the latest version of Cuckoo is as simple as follows. Note that it is recommended to first upgrade the pip and setuptools libraries as they’re often outdated, leading to issues when trying to install Cuckoo (see also DistributionNotFound / No distribution matching the version..).

Warning

It is not unlikely that you’ll be missing one or more system packages required to build various Python dependencies. Please read and re-read Requirements to resolve these sorts of issues.

$ sudo pip install -U pip setuptools
$ sudo pip install -U cuckoo

Although the above, a global installation of Cuckoo in your OS works mostly fine, we highly recommend installing Cuckoo in a virtualenv, which looks roughly as follows:

$ virtualenv venv
$ . venv/bin/activate
(venv)$ pip install -U pip setuptools
(venv)$ pip install -U cuckoo

Note

Depending on how you have set up your environment (virtualenvs etc.) you may need to specify the version of pip to use. Just replace pip in the commands above with pip2.

Some reasons for using a virtualenv:

Cuckoo’s dependencies may not be entirely up-to-date, but instead pin to a known-to-work-properly version.
The dependencies of other software installed on your system may conflict with those required by Cuckoo, due to incompatible version requirements (and yes, this is also possible when Cuckoo supports the latest version, simply because the other software may have pinned to an older version).
Using a virtualenv allows non-root users to install additional packages or upgrade Cuckoo at a later point in time.
And simply put, virtualenv is considered a best practice.

Please refer to Cuckoo Working Directory and Cuckoo Working Directory Usage to learn more about the Cuckoo Working Directory and how to operate it.

Install Cuckoo from file¶

By downloading a hard copy of the Cuckoo Package and installing it offline, one may set up Cuckoo using a cached copy and/or have a backup copy of current Cuckoo versions in the future. We also feature the option to download such a tarball on our website.

Obtaining the tarball of Cuckoo and all of its dependencies manually may be done as follows:

$ pip download cuckoo

You will end up with a file Cuckoo-2.0.0.tar.gz (or a higher number, depending on the latest released stable version) as well as all of its dependencies (e.g., alembic-0.8.8.tar.gz).

Installing that exact version of Cuckoo may be done as you’re familiar with from installing it using pip directly, except now using the filename of the tarball:

$ pip install Cuckoo-2.0.0.tar.gz

On systems where no internet connection is available, the $ pip download cuckoo command may be used to fetch all of the required dependencies and as such one should be able to - in theory - install Cuckoo completely offline using those files, i.e., by executing something like the following:

$ pip install *.tar.gz

Build/Install Cuckoo from source¶

By cloning Cuckoo Sandbox from our official repository, you can install it from source. After cloning, follow the steps mentioned in Development with the Python Package to start the installation.

Cuckoo Working Directory¶

New in version 2.0.0.

A new concept is the Cuckoo Working Directory. From this point forward all configurable components, generated data, and results of Cuckoo will be stored in this directory. These files include but are not limited to the following:

Configuration
Cuckoo Signatures
Cuckoo Analyzer
Cuckoo Agent
Yara rules
Cuckoo Storage (where analysis results go)
And much more..

The Cuckoo Working Directory comes with a couple of advantages over the legacy approach that Cuckoo used. Following we will study how the Cuckoo Working Directory (CWD from now on) overcomes various every-day hurdles.

Note

This document merely shows the installation part of the CWD, for its actual usage, please refer to the Cuckoo Working Directory Usage document.

Configuration¶

If you have ever updated your Cuckoo setup to a later version, you have run into the issue where you had to make a backup of your configuration, update your Cuckoo instance, and either restore your configuration or re-apply it completely.

With the introduction of the CWD we have gotten rid of this update nightmare.

The first time you run Cuckoo a CWD checkout will be created for you automatically, this pretty much goes as follows:

$ cuckoo -d

        _       _                   _             _              _            _
        /\ \     /\_\               /\ \           /\_\           /\ \         /\ \
        /  \ \   / / /         _    /  \ \         / / /  _       /  \ \       /  \ \
        / /\ \ \  \ \ \__      /\_\ / /\ \ \       / / /  /\_\    / /\ \ \     / /\ \ \
    / / /\ \ \  \ \___\    / / // / /\ \ \     / / /__/ / /   / / /\ \ \   / / /\ \ \
    / / /  \ \_\  \__  /   / / // / /  \ \_\   / /\_____/ /   / / /  \ \_\ / / /  \ \_\
    / / /    \/_/  / / /   / / // / /    \/_/  / /\_______/   / / /   / / // / /   / / /
    / / /          / / /   / / // / /          / / /\ \ \     / / /   / / // / /   / / /
/ / /________  / / /___/ / // / /________  / / /  \ \ \   / / /___/ / // / /___/ / /
/ / /_________\/ / /____\/ // / /_________\/ / /    \ \ \ / / /____\/ // / /____\/ /
\/____________/\/_________/ \/____________/\/_/      \_\_\\/_________/ \/_________/

Cuckoo Sandbox 2.0.0
www.cuckoosandbox.org
Copyright (c) 2010-2017

=======================================================================
    Welcome to Cuckoo Sandbox, this appears to be your first run!
    We will now set you up with our default configuration.
    You will be able to modify the configuration to your likings
    by exploring the /home/cuckoo/.cuckoo directory.

    Among other configurable things of most interest is the
    new location for your Cuckoo configuration:
            /home/cuckoo/.cuckoo/conf
=======================================================================

Cuckoo has finished setting up the default configuration.
Please modify the default settings where required and
start Cuckoo again (by running `cuckoo` or `cuckoo -d`).

As pointed out by the info messages you will now be able to find your CWD at /home/cuckoo/.cuckoo as it defaults to ~/.cuckoo. All configuration files as you know them can be found in the $CWD/conf directory. I.e., $CWD/conf/cuckoo.conf, $CWD/conf/virtualbox.conf, etc.

Now because the CWD directory is not part of Cuckoo itself, that is, the Git repository or as part of one of the latest releases, one will be able to upgrade Cuckoo without having to touch the CWD. (Of course if an update is installed that requires an updated Configuration then Cuckoo will guide the user through it - instead of overwriting the Configuration files itself).

CWD path¶

Even though the CWD defaults to ~/.cuckoo this path is completely configurable. The following lists the order of precedence for Cuckoo to determine the CWD.

Through the --cwd command-line option (e.g., --cwd ~/.cuckoo).
Through the CUCKOO environment variable (e.g., export CUCKOO=~/.cuckoo).
Through the CUCKOO_CWD environment variable.
If the current directory is a CWD (e.g., cd ~/.cuckoo assuming that a CWD has been created in that directory).
The default, ~/.cuckoo.

By using alternative CWD paths it is possible to run multiple Cuckoo instances with different configurations using the same Cuckoo setup. If for some reason one requires two or three separate Cuckoo setups, e.g., in the case that you want to run Windows analysis and Android analysis in parallel, then not having to upgrade each instance one-by-one every time there is an update surely is a great step forward.

Following some examples to show how to configure the CWD.

# Places the CWD in /opt/cuckoo. Note that Cuckoo will normally create the
# CWD itself, but in order to create a directory in /opt root capabilities
# are usually required.
$ sudo mkdir /opt/cuckoo
$ sudo chown cuckoo:cuckoo /opt/cuckoo
$ cuckoo --cwd /opt/cuckoo

# You could place this line in your .bashrc, for example.
$ export CUCKOO=/opt/cuckoo
$ cuckoo

Experimenting with multiple Cuckoo setups is now as simple as creating multiple CWD’s and configuring them accordingly.

Configuration¶

Cuckoo relies on a couple of main configuration files:

cuckoo.conf: for configuring general behavior and analysis options.
auxiliary.conf: for enabling and configuring auxiliary modules.
<machinery>.conf: for defining the options for your virtualization software (the file has the same name of the machinery module you choose in cuckoo.conf).
memory.conf: Volatility configuration.
processing.conf: for enabling and configuring processing modules.
reporting.conf: for enabling or disabling report formats.

To get Cuckoo working you should at the very least edit cuckoo.conf and <machinery>.conf.

cuckoo.conf¶

The first file to edit is $CWD/conf/cuckoo.conf. Note that we’ll be referring to the Cuckoo Working Directory when we talk about $CWD. The cuckoo.conf file contains generic configuration options that you will want to verify or at least familiarize yourself with before launching Cuckoo.

The file is largely commented and self-explanatory, but some of the options may be of special interest to you:

machinery in [cuckoo]:

This option defines which Machinery module you want Cuckoo to use to interact with your analysis machines. The value must be the name of the module without extension (e.g., virtualbox or vmware).
ip and port in [resultserver]:

These define the local IP address and port that Cuckoo is going to try to bind the result server on. Make sure this matches the network configuration of your analysis machines or they won’t be able to return any results.
connection in [database]:

The database connection string defines how Cuckoo will connect to the internal database. You can use any DBMS supported by SQLAlchemy using a valid Database Urls syntax.

Warning

Check your interface for resultserver IP! Some virtualization software (for example Virtualbox) don’t bring up the virtual networking interfaces until a virtual machine is started. Cuckoo needs to have the interface where you bind the resultserver up before the start, so please check your network setup. If you are not sure about how to get the interface up, a good trick is to manually start and stop an analysis virtual machine, this will bring virtual networking up. If you are using NAT/PAT in your network, you can set up the resultserver IP to 0.0.0.0 to listen on all interfaces, then use the specific options resultserver_ip and resultserver_port in <machinery>.conf to specify the address and port as every machine sees them. Note that if you set resultserver IP to 0.0.0.0 in cuckoo.conf you have to set resultserver_ip for all your virtual machines.

auxiliary.conf¶

Auxiliary modules are scripts that run concurrently with malware analysis, this file defines their options.

Following is the default $CWD/conf/auxiliary.conf file.

[sniffer]
# Enable or disable the use of an external sniffer (tcpdump) [yes/no].
enabled = yes

# Specify the path to your local installation of tcpdump. Make sure this
# path is correct.
tcpdump = /usr/sbin/tcpdump

# We used to define the network interface to capture on in auxiliary.conf, but
# this has been moved to the "interface" field of each Virtual Machinery
# configuration.

# Specify a Berkeley packet filter to pass to tcpdump.
# Note: packer filtering is not possible when using "nictrace" functionality
# from VirtualBox (for example dumping inter-VM traffic).
bpf = 

[mitm]
# Enable man in the middle proxying (mitmdump) [yes/no].
enabled = no

# Specify the path to your local installation of mitmdump. Make sure this
# path is correct.
mitmdump = /usr/local/bin/mitmdump

# Listen port base. Each virtual machine will use its own port to be
# able to make a good distinction between the various running analyses.
# Generally port 50000 should be fine, in this case port 50001, 50002, etc
# will also be used - again, one port per analyses.
port_base = 50000

# Script file to interact with the network traffic. Please refer to the
# documentation of mitmproxy/mitmdump to get an understand of their internal
# workings. (https://mitmproxy.org/doc/scripting/inlinescripts.html)
script = stuff/mitm.py

# Path to the certificate to be used by mitmdump. This file will be
# automatically generated for you if you run mitmdump once. It's just that
# you have to copy it from ~/.mitmproxy/mitmproxy-ca-cert.p12 to somewhere
# in the analyzer/windows/ directory. Recommended is to write the certificate
# to analyzer/windows/bin/cert.p12, in that case the following option should
# be set to bin/cert.p12.
certificate = bin/cert.p12

[replay]
# Enable PCAP replay capabilities.
enabled = yes

# Specify the path to your local installation of mitmdump. Make sure this
# path is correct. Note that this should be mitmproxy 3.0.5 or higher,
# installed in a separate virtualenv (or similar).
mitmdump = /usr/local/bin/mitmdump

# Listen port base. Each virtual machine will use its own port to be
# able to make a good distinction between the various running analyses.
# Generally port 51000 should be fine, in this case port 51001, 51002, etc
# will also be used - again, one port per analyses.
port_base = 51000

# Path to the certificate to be used by mitmdump. This file will be
# automatically generated for you if you run mitmdump once. It's just that
# you have to copy it from ~/.mitmproxy/mitmproxy-ca-cert.p12 to somewhere
# in the analyzer/windows/ directory. Recommended is to write the certificate
# to analyzer/windows/bin/cert.p12, in that case the following option should
# be set to bin/cert.p12.
certificate = bin/cert.p12

[services]
# Provide extra services accessible through the network of the analysis VM
# provided in separate, standalone, Virtual Machines [yes/no].
enabled = no

# Comma-separated list with each Virtual Machine containing said service(s).
services = honeyd

# Time in seconds required to boot these virtual machines. E.g., some services
# will only get online after a minute because initialization takes a while.
timeout = 0

[reboot]
# This auxiliary module should be enabled for reboot analysis support.
enabled = yes

<machinery>.conf¶

Machinery modules are scripts that define how Cuckoo should interact with your virtualization software of choice.

Every module has a dedicated configuration file which defines the details on the available machines. For example, Cuckoo comes with a VMWware machinery module. In order to use it one has to specify vmware as machinery option in $CWD/conf/cuckoo.conf and populate the $CWD/conf/vmware.conf file with the available Virtual Machines.

Cuckoo provides some modules by default and for the sake of this guide, we’ll assume you’re going to use VirtualBox.

Following is the default $CWD/conf/virtualbox.conf file.

[virtualbox]
# Specify which VirtualBox mode you want to run your machines on.
# Can be "gui" or "headless". Please refer to VirtualBox's official
# documentation to understand the differences.
mode = headless

# Path to the local installation of the VBoxManage utility.
path = /usr/bin/VBoxManage
# If you are running Cuckoo on Mac OS X you have to change the path as follows:
# path = /Applications/VirtualBox.app/Contents/MacOS/VBoxManage

# Default network interface.
interface = vboxnet0

# Specify a comma-separated list of available machines to be used. For each
# specified ID you have to define a dedicated section containing the details
# on the respective machine. (E.g. cuckoo1,cuckoo2,cuckoo3)
machines = cuckoo1

# If remote control is enabled in cuckoo.conf, specify a port range to use.
# Virtualbox will bind the VRDP interface to the first available port.
controlports = 5000-5050


[cuckoo1]
# Specify the label name of the current machine as specified in your
# VirtualBox configuration.
label = cuckoo1

# Specify the operating system platform used by current machine
# [windows/darwin/linux].
platform = windows

# Specify the IP address of the current virtual machine. Make sure that the
# IP address is valid and that the host machine is able to reach it. If not,
# the analysis will fail.
ip = 192.168.56.101

# (Optional) Specify the snapshot name to use. If you do not specify a snapshot
# name, the VirtualBox MachineManager will use the current snapshot.
# Example (Snapshot1 is the snapshot name):
snapshot = 

# (Optional) Specify the name of the network interface that should be used
# when dumping network traffic from this machine with tcpdump. If specified,
# overrides the default interface specified in auxiliary.conf
# Example (vboxnet0 is the interface name):
interface = 

# (Optional) Specify the IP of the Result Server, as your virtual machine sees it.
# The Result Server will always bind to the address and port specified in cuckoo.conf,
# however you could set up your virtual network to use NAT/PAT, so you can specify here
# the IP address for the Result Server as your machine sees it. If you don't specify an
# address here, the machine will use the default value from cuckoo.conf.
# NOTE: if you set this option you have to set result server IP to 0.0.0.0 in cuckoo.conf.
# Example:
resultserver_ip = 

# (Optional) Specify the port for the Result Server, as your virtual machine sees it.
# The Result Server will always bind to the address and port specified in cuckoo.conf,
# however you could set up your virtual network to use NAT/PAT, so you can specify here
# the port for the Result Server as your machine sees it. If you don't specify a port
# here, the machine will use the default value from cuckoo.conf.
# Example:
resultserver_port = 

# (Optional) Set your own tags. These are comma separated and help to identify
# specific VMs. You can run samples on VMs with tag you require.
tags = 

# Mostly unused for now. Please don't fill it out.
options = 

# (Optional) Specify the OS profile to be used by volatility for this
# virtual machine. This will override the guest_profile variable in
# memory.conf which solves the problem of having multiple types of VMs
# and properly determining which profile to use.
osprofile = 


[honeyd]
# For more information on this VM please refer to the "services" section of
# the conf/auxiliary.conf configuration file. This machine is a bit special
# in the way that its used as an additional VM for an analysis.
# *NOTE* that if this functionality is used, the VM should be registered in
# the "machines" list in the beginning of this file.
label = honeyd
platform = linux
ip = 192.168.56.102
# The tags should at least contain "service" and the name of this service.
# This way the services auxiliary module knows how to find this particular VM.
tags = service, honeyd
# Not all services actually have a Cuckoo Agent running in the VM, for those
# services one can specify the "noagent" option so Cuckoo will just wait until
# the end of the analysis instead of trying to connect to the non-existing
# Cuckoo Agent. We can't really intercept any inter-VM communication from the
# host / gateway so in order to dump traffic between VMs we have to use a
# different network dumping approach. For this machine we use the "nictrace"
# functionality from VirtualBox (which is basically their internal tcpdump)
# and thus properly dumps inter-VM traffic.
options = nictrace noagent

The configuration for the other machinery modules look mostly the same with some variations where required. E.g., XenServer operates through an API, so to access it a URL and credentials are required.

The comments for the options are self-explanatory.

Following is the default $CWD/conf/kvm.conf file.

[kvm]
# Specify a libvirt URI connection string
dsn = qemu:///system

# Specify a comma-separated list of available machines to be used. For each
# specified ID you have to define a dedicated section containing the details
# on the respective machine. (E.g. cuckoo1,cuckoo2,cuckoo3)
machines = cuckoo1

# Specify the name of the default network interface that will be used
# when dumping network traffic with tcpdump.
# Example (virbr0 is the interface name):
interface = virbr0


[cuckoo1]
# Specify the label name of the current machine as specified in your
# libvirt configuration.
label = cuckoo1

# Specify the operating system platform used by current machine
# [windows/darwin/linux].
platform = windows

# Specify the IP address of the current virtual machine. Make sure that the
# IP address is valid and that the host machine is able to reach it. If not,
# the analysis will fail. You may want to configure your network settings in
# /etc/libvirt/<hypervisor>/networks/
ip = 192.168.122.101

# (Optional) Specify the snapshot name to use. If you do not specify a snapshot
# name, the KVM MachineManager will use the current snapshot.
# Example (Snapshot1 is the snapshot name):
snapshot = 

# (Optional) Specify the name of the network interface that should be used
# when dumping network traffic from this machine with tcpdump.
# Example (virbr0 is the interface name):
interface = 

# (Optional) Specify the IP of the Result Server, as your virtual machine sees it.
# The Result Server will always bind to the address and port specified in cuckoo.conf,
# however you could set up your virtual network to use NAT/PAT, so you can specify here
# the IP address for the Result Server as your machine sees it. If you don't specify an
# address here, the machine will use the default value from cuckoo.conf.
# NOTE: if you set this option you have to set result server IP to 0.0.0.0 in cuckoo.conf.
# Example:
resultserver_ip = 

# (Optional) Specify the port for the Result Server, as your virtual machine sees it.
# The Result Server will always bind to the address and port specified in cuckoo.conf,
# however you could set up your virtual network to use NAT/PAT, so you can specify here
# the port for the Result Server as your machine sees it. If you don't specify a port
# here, the machine will use the default value from cuckoo.conf.
# Example:
resultserver_port = 

# (Optional) Set your own tags. These are comma separated and help to identify
# specific VMs. You can run samples on VMs with tag you require.
tags = 

# (Optional) Specify the OS profile to be used by volatility for this
# virtual machine. This will override the guest_profile variable in
# memory.conf which solves the problem of having multiple types of VMs
# and properly determining which profile to use.
osprofile =

memory.conf¶

The Volatility tool offers a large set of plugins for memory dump analysis. Some of them are quite slow. The $CWD/conf/volatility.conf file let’s you enable or disable plugins of your choice. To use Volatility you have to follow two steps:

Enable volatility in $CWD/conf/processing.conf

Enable memory_dump in $CWD/conf/cuckoo.conf

In $CWD/conf/memory.conf’s basic section you can configure the Volatility profile and whether memory dumps should be deleted after having been processed (this saves a lot of diskspace):

# Basic settings
[basic]
# Profile to avoid wasting time identifying it
guest_profile = WinXPSP2x86
# Delete memory dump after volatility processing.
delete_memdump = no

After that every plugin has its own section for configuration:

# Scans for hidden/injected code and dlls
# http://code.google.com/p/volatility/wiki/CommandReference#malfind
[malfind]
enabled = on
filter = on

# Lists hooked api in user mode and kernel space
# Expect it to be very slow when enabled
# http://code.google.com/p/volatility/wiki/CommandReference#apihooks
[apihooks]
enabled = off
filter = on

The filter configuration helps you to remove known clean data from the resulting report. It can be configured separately for every plugin.

The filter itself is configured in the [mask] section. You can enter a list of pids in pid_generic to filter out processes:

# Masks. Data that should not be logged
# Just get this information from your plain VM Snapshot (without running malware)
# This will filter out unwanted information in the logs
[mask]
# pid_generic: a list of process ids that already existed on the machine before the malware was started.
pid_generic = 4, 680, 752, 776, 828, 840, 1000, 1052, 1168, 1364, 1428, 1476, 1808, 452, 580, 652, 248, 1992, 1696, 1260, 1656, 1156

processing.conf¶

This file allows you to enable, disable and configure all processing modules. These modules are located under the cuckoo.processing module and define how to digest the raw data collected during the analysis.

You will find a section for each processing module in $CWD/conf/processing.conf.

# Enable or disable the available processing modules [yes/no].
# If you add a custom processing module to your Cuckoo setup, you have to add
# a dedicated entry in this file, or it won't be executed.
# You can also add additional options under the section of your module and
# they will be available in your Python class.

[analysisinfo]
enabled = yes

[apkinfo]
enabled = no
# Decompiling dex files with androguard in a heavy operation. For large dex
# files it can really take quite a while - it is recommended to limit to a
# certain filesize.
decompilation_threshold = 5000000

[baseline]
enabled = no

[behavior]
enabled = yes

[buffer]
enabled = yes

[debug]
enabled = yes

[droidmon]
enabled = no

[dropped]
enabled = yes

[dumptls]
enabled = yes

[extracted]
enabled = yes

[googleplay]
enabled = no
android_id = 
google_login = 
google_password = 

[memory]
# Create a memory dump of the entire Virtual Machine. This memory dump will
# then be analyzed using Volatility to locate interesting events that can be
# extracted from memory.
enabled = no

[misp]
enabled = no
url = 
apikey = 

# Maximum amount of IOCs to look up (hard limit).
maxioc = 100

[network]
enabled = yes

# Allow domain whitelisting
whitelist_dns = no

# Allow DNS responses from your configured DNS server for whitelisting to
# deactivate when responses come from some other DNS
# Can be also multiple like : 8.8.8.8,8.8.4.4
allowed_dns = 

[procmemory]
# Enables the creation of process memory dumps for each analyzed process right
# before they terminate themselves or right before the analysis finishes.
enabled = yes
# It is possible to load these process memory dumps in IDA Pro through the
# generation of IDA Python-based script files. Although currently symbols and
# such are not properly recovered, it is still nice to get a quick look at
# specific memory addresses of a process.
idapro = no
# Extract executable images from this process memory dump. This allows us to
# relatively easily extract injected executables.
extract_img = yes
# Also extract DLL files from the process memory dump.
extract_dll = no
# Delete process memory dumps after analysis to save disk space.
dump_delete = no

[procmon]
# Enable procmon processing. This only takes place when the "procmon=1" option
# is set for an analysis.
enabled = yes

[screenshots]
enabled = yes
# Set to the actual tesseract path (i.e., /usr/bin/tesseract or similar)
# rather than "no" to enable OCR analysis of screenshots.
# Note: doing OCR on the screenshots is a rather slow process.
tesseract = no

[snort]
enabled = no
# Following are various configurable settings. When in use of a recent 2.9.x.y
# version of Snort there is no need to change any of the following settings as
# they represent the defaults.
#
snort = /usr/local/bin/snort
conf = /etc/snort/snort.conf

[static]
enabled = yes
# On bigger PDF files PeePDF may take a substantial amount of time to perform
# static analysis of PDF files, with times of over an hour per file estimated
# in production. This option will by default limit the maximum processing time
# to one minute, but this may be adjusted accordingly. Note that if the timeout
# is hit, no static analysis results through PeePDF will be available.
pdf_timeout = 60

[strings]
enabled = yes

[suricata]
enabled = no

# Following are various configurable settings. When in use of a recent version
# of Suricata there is no need to change any of the following settings as they
# represent the defaults.
suricata = /usr/bin/suricata
conf = /etc/suricata/suricata.yaml
eve_log =  eve.json
files_log = files-json.log
files_dir = files

# By specifying the following line our processing module can use the socket
# mode in Suricata. This is quite the performance improvement as instead of
# having to load all the Suricata rules for each time the processing module is
# ran (i.e., for every task), the rules are only loaded once and then we talk
# to its API. This does require running Suricata as follows or similar;
# "suricata --unix-socket -D".
# (Please find more information in utils/suricata.sh for now).
# socket = /var/run/suricata/cuckoo.socket
socket = 

[targetinfo]
enabled = yes

[virustotal]
enabled = no
# How much time we can wait to establish VirusTotal connection and get the
# report.
timeout = 60
# Enable this option if you want to submit files to VirusTotal not yet available
# in their database.
# NOTE: if you are dealing with sensitive stuff, enabling this option you could
# leak some files to VirusTotal.
scan = no
# Add your VirusTotal API key here. The default API key, kindly provided
# by the VirusTotal team, should enable you with a sufficient throughput
# and while being shared with all our users, it shouldn't affect your use.
key = a0283a2c3d55728300d064874239b5346fb991317e8449fe43c902879d758088

[irma]
enabled = no
# IRMA @ github : https://github.com/quarkslab/irma
# How much time we can wait to establish IRMA connection and get the report.
timeout = 60
# Enable this option if you want to submit files to IRMA not yet available.
scan = no
# Force scan of submitted files
force = no
# URL to your IRMA installation
# For example : https://your.irma.host
url = 
# Probes to use on your IRMA instance
# If not specified, will default to using all available probes
# Expects comma separated list
# For example : ClamAV,F-Secure,Avast,ESET,eScan,Avira,Sophos,McAfee,Kaspersky,GData,Comodo,Bitdefender
probes =

You might want to configure the VirusTotal key if you have an account of your own.

reporting.conf¶

The $CWD/conf/reporting.conf file contains information on the reports generation.

It contains the following sections.

# Enable or disable the available reporting modules [on/off].
# If you add a custom reporting module to your Cuckoo setup, you have to add
# a dedicated entry in this file, or it won't be executed.
# You can also add additional options under the section of your module and
# they will be available in your Python class.

[feedback]
# Automatically report errors that occurred during an analysis. Requires the
# Cuckoo Feedback settings in cuckoo.conf to have been filled out properly.
enabled = no

[jsondump]
enabled = yes
indent = 4
calls = yes

[singlefile]
# Enable creation of report.html and/or report.pdf?
enabled = no
# Enable creation of report.html?
html = no
# Enable creation of report.pdf?
pdf = no

[misp]
enabled = no
url = 
apikey = 

# The various modes describe which information should be submitted to MISP,
# separated by whitespace. Available modes: maldoc ipaddr hashes url.
mode = maldoc ipaddr hashes url

distribution = 0
analysis = 0
threat_level = 4

# The minimum Cuckoo score for a MISP event to be created
min_malscore = 0

tag = Cuckoo
upload_sample = no

[mongodb]
enabled = no
host = 127.0.0.1
port = 27017
db = cuckoo
store_memdump = yes
paginate = 100
# MongoDB authentication (optional).
username = 
password = 

[elasticsearch]
enabled = no
# Comma-separated list of ElasticSearch hosts. Format is IP:PORT, if port is
# missing the default port is used.
# Example: hosts = 127.0.0.1:9200, 192.168.1.1:80
hosts = 127.0.0.1
# Increase default timeout from 10 seconds, required when indexing larger
# analysis documents.
timeout = 300
# Set to yes if we want to be able to search every API call instead of just
# through the behavioral summary.
calls = no
# Index of this Cuckoo instance. If multiple Cuckoo instances connect to the
# same ElasticSearch host then this index (in Moloch called "instance") should
# be unique for each Cuckoo instance.
index = cuckoo

# Logging time pattern.  This sets how elasticsearch creates indexes
# by default it is yearly in most instances this will be sufficient
# valid options: yearly, monthly, daily
index_time_pattern = yearly

# Cuckoo node name in Elasticsearch to identify reporting host. Can be useful
# for automation and while referring back to correct Cuckoo host.
cuckoo_node = 

[moloch]
enabled = no
# If the Moloch web interface is hosted on a different IP address than the
# Cuckoo Web Interface then you'll want to override the IP address here.
host = 
# If you wish to run Moloch in http (insecure) versus https (secure) mode,
# set insecure to yes.
insecure = no

# Following are various configurable settings. When in use of a recent version
# of Moloch there is no need to change any of the following settings as they
# represent the defaults.
moloch_capture = /data/moloch/bin/moloch-capture
conf = /data/moloch/etc/config.ini
instance = cuckoo

[notification]
# Notification module to inform external systems that analysis is finished.
# You should consider keeping this as very last reporting module.
enabled = no

# External service URL where info will be POSTed.
# example : https://my.example.host/some/destination/url
url = 

# Cuckoo host identifier - can be hostname.
# for example : my.cuckoo.host
identifier = 

[mattermost]
enabled = no

# Mattermost webhook URL.
# example : https://my.mattermost.host/hooks/yourveryrandomkey
url = 

# Cuckoo host URL to make analysis ID clickable.
# example : https://my.cuckoo.host/
myurl = 

# Username to show when posting message
username = cuckoo

# What kind of data to show apart from default.
# Show virustotal hits.
show_virustotal = no

# Show matched cuckoo signatures.
show_signatures = no

# Show collected URL-s by signature "network_http".
show_urls = no

# Hide filename and create hash of it
hash_filename = no
# Hide URL and create hash of it
hash_url = no

By setting those option to on or off you enable or disable the generation of such reports.

Per-Analysis Network Routing¶

Since Cuckoo 2.0-rc1 it is possible to feature per-analysis network routing. In other words, if you have one VM and three samples to analyze, it is possible to deny internet access for the first analysis, route the second analysis through a VPN, and pull the third analysis through the Tor network.

However, aside from the more advanced per-analysis routing, it is naturally also possible to have one default route - a setup that used to be popular before, when the more luxurious routing was not yet available.

In our examples we’ll be focusing on VirtualBox as it is our default machinery choice.

Simple Global Routing¶

Before delving into the more complex and feature-rich per-analysis network routing we’ll first cover the older approach, which is based on global iptables rules that are, once set, not changed anymore.

In the following setup we’re assuming that the interface assigned to our VirtualBox VM is vboxnet0, the IP address of our VM is 192.168.56.101 (in a /24 subnet), and that the outgoing interface connected to the internet is eth0. With such a setup, the following iptables rules will allow the VMs access to the Cuckoo host machine (192.168.56.1 in this setup) as well as the entire internet as you would expect from any application connecting to the internet.

$ sudo iptables -t nat -A POSTROUTING -o eth0 -s 192.168.56.0/24 -j MASQUERADE

# Default drop.
$ sudo iptables -P FORWARD DROP

# Existing connections.
$ sudo iptables -A FORWARD -m state --state RELATED,ESTABLISHED -j ACCEPT

# Accept connections from vboxnet to the whole internet.
$ sudo iptables -A FORWARD -s 192.168.56.0/24 -j ACCEPT

# Internal traffic.
$ sudo iptables -A FORWARD -s 192.168.56.0/24 -d 192.168.56.0/24 -j ACCEPT

# Log stuff that reaches this point (could be noisy).
$ sudo iptables -A FORWARD -j LOG

And that’s pretty much it, with these rules set we’re almost good to go. However, these rules won’t be doing any packet forwarding unless IP forwarding is explicitly enabled in the kernel. To do so, there is a temporary method that survives until a shutdown or reboot, and a permanent method that is taken into account when booting the machine. Simply put, generally speaking you’ll want to run both commands:

$ echo 1 | sudo tee -a /proc/sys/net/ipv4/ip_forward
$ sudo sysctl -w net.ipv4.ip_forward=1

Iptables rules are not persistent between reboots, so if want to keep them you should use a script or just install iptables-persistent.

Newer Linux distributions have adopted udev’s interface-naming scheme. It’s important to note that this means eth0 may no longer be your primary interface. Possible interface names include ensXX, enp0sXX, and emXX, where the XX part identifies a number. This is particularly important to note for the NAT statement above.

Per-Analysis Network Routing Options¶

Having discussed the old school method for routing analyses through a network interface we will now walk through the dynamic network routing components that allow for much more granular network routing.

As outlined in the introduction for this chapter of the documentation it has been possible since Cuckoo 2.0-rc1, when we introduced the Cuckoo Rooter, to do per-analysis network routing. Since then various bugs have been resolved and more network routing options have been added.

Following is the list of available routing options.

Routing Option	Description
None Routing	No routing whatsoever, the only option that does not require the Cuckoo Rooter to be run (and therefore also the default routing option).
Drop Routing	Completely drops all non-Cuckoo traffic, including traffic within the VMs’ subnet.
Internet Routing	Full internet access as provided by the given network interface (similar to the Simple Global Routing setup).
InetSim Routing	Routes all traffic to an InetSim instance - which provides fake services - running on the host machine.
Tor Routing	Routes all traffic through Tor.
VPN Routing	Routes all traffic through one of perhaps multiple pre-defined VPN endpoints.

Using Per-Analysis Network Routing¶

Having knowledge about the available network routing options it is time to actually use it in practice. Assuming Cuckoo has been configured properly taking advantage of its features is really as simple as starting the Cuckoo Rooter and choosing a network routing option for your analysis.

Documentation on starting the Cuckoo Rooter may be found in the Cuckoo Rooter Usage document.

Both global routing and per-analysis routing require ip forwarding to be enabled:

$ echo 1 | sudo tee -a /proc/sys/net/ipv4/ip_forward $ sudo sysctl -w net.ipv4.ip_forward=1

Configuring iproute2¶

For Linux kernel TCP/IP source routing reasons it is required to register each of the network interfaces that we use with iproute2. This is trivial, but necessary.

As an example we’ll be configuring Internet Routing (aka the dirty line) for which we’ll be using the eth0 network interface - reverting back to Ubuntu 14.04 and older terminology here for a second (Ubuntu 16.04 uses network interface names based on the hardware manufacturer, as you will likely have seen happen on BSD-based systems since forever).

To configure iproute2 with eth0 we’re going to open the /etc/iproute2/rt_tables file which will look roughly as follows:

#
# reserved values
#
255     local
254     main
253     default
0       unspec
#
# local
#

Now roll a random number that is not yet present in this file with your dice of choice and use it to craft a new line at the end of the file. As an example, registering eth0 with iproute2 could look as follows:

#
# reserved values
#
255     local
254     main
253     default
0       unspec
#
# local
#

400     eth0

And that’s really all there is to it. You will have to do this for each network interface you intend to use for network routing.

None Routing¶

The default routing mechanism in the sense that Cuckoo allows the analysis to route as defined by a third party. As in, it literally doesn’t do anything. One may use the none routing in conjunction with the Simple Global Routing.

Drop Routing¶

The drop routing option is somewhat like a default None Routing setup (as in, in a machine where no global iptables rules have been created providing full internet access to VMs or so), except that it is much more aggressive in actively locking down the internet access provided to the VM.

With drop routing the only traffic possible is internal Cuckoo traffic and hence any DNS requests or outgoing TCP/IP connections are blocked.

Internet Routing¶

By using the internet routing one may provide full internet access to VMs through one of the connected network interfaces. We also refer to this option as the dirty line due to its nature of allowing all potentially malicious samples to connect to the internet through the same uplink.

Note

It is required to register the dirty line network interface with iproute2 as described in the Configuring iproute2 section.

InetSim Routing¶

For those that have not heard of InetSim, it’s a project that provides fake services for malware to talk to. In order to use InetSim routing one will have to setup InetSim on the host machine (or in a separate VM) and configure Cuckoo so that it knows where to find the InetSim server.

The configuration for InetSim is self-explanatory and can be found as part of the $CWD/conf/routing.conf configuration file:

[inetsim]
enabled = yes
server = 192.168.56.1

In order to quickly get started with InetSim it is possible to download the latest version of the REMnux distribution which features - among many other tools - the latest version of InetSim. Naturally this VM will require its own static IP address which should then be configured in the routing.conf configuration file.

Tor Routing¶

Note

Although we highly discourage the use of Tor for malware analysis - the maintainers of Tor exit nodes already have a hard enough time keeping up their servers - it is in fact a well-supported feature.

First of all Tor will have to be installed. Please find instructions on installing the latest stable version of Tor here.

We’ll then have to modify the Tor configuration file (not talking about Cuckoo’s configuration for Tor yet!) In order to do so, we will have to provide Tor with the listening address and port for TCP/IP connections and UDP requests. For a default VirtualBox setup, where the host machine has IP address 192.168.56.1, the following lines will have to be configured in the /etc/tor/torrc file:

TransPort 192.168.56.1:9040
DNSPort 192.168.56.1:5353

Don’t forget to restart Tor (/etc/init.d/tor restart). That leaves us with the Tor configuration for Cuckoo, which may be found in the $CWD/conf/routing.conf file. The configuration is pretty self-explanatory so we’ll leave filling it out as an exercise to the reader (in fact, toggling the enabled field goes a long way):

[tor]
enabled = yes
dnsport = 5353
proxyport = 9040

Note that the port numbers in the /etc/tor/torrc and $CWD/conf/routing.conf files must match in order for the two to interact correctly.

VPN Routing¶

Last but not least, it is possible to route analyses through a number of VPNs. By defining a couple of VPNs, perhaps ending up in different countries, it may be possible to see if potentially malicious samples behave differently depending on the country of origin of its IP address.

The configuration for a VPN is much like the configuration of a VM. For each VPN you will need one section in the $CWD/conf/routing.conf configuration file detailing the relevant information for the VPN. In the configuration the VPN will also have to be registered in the list of available VPNs (exactly the same as you’d do for registering more VMs).

Configuration for a single VPN looks roughly as follows:

[vpn]
# Are VPNs enabled?
enabled = yes

# Comma-separated list of the available VPNs.
vpns = vpn0

[vpn0]
# Name of this VPN. The name is represented by the filepath to the
# configuration file, e.g., cuckoo would represent /etc/openvpn/cuckoo.conf
# Note that you can't assign the names "none" and "internet" as those would
# conflict with the routing section in cuckoo.conf.
name = vpn0

# The description of this VPN which will be displayed in the web interface.
# Can be used to for example describe the country where this VPN ends up.
description = Spain, Europe

# The tun device hardcoded for this VPN. Each VPN *must* be configured to use
# a hardcoded/persistent tun device by explicitly adding the line "dev tunX"
# to its configuration (e.g., /etc/openvpn/vpn1.conf) where X in tunX is a
# unique number between 0 and your lucky number of choice.
interface = tun0

# Routing table name/id for this VPN. If table name is used it *must* be
# added to /etc/iproute2/rt_tables as "<id> <name>" line (e.g., "201 tun0").
# ID and name must be unique across the system (refer /etc/iproute2/rt_tables
# for existing names and IDs).
rt_table = tun0

Note

It is required to register each VPN network interface with iproute2 as described in the Configuring iproute2 section.

Configuration (Android Analysis)¶

Deprecated since version 2.0-rc2: Android Analysis may not work as expected due to the changes to becoming a Cuckoo Package. Proper Android integration will be picked up as a Cuckoo update in the future.

To get Cuckoo running Android analysis you should download the Android SDK and extract it in a folder Cuckoo can access. You should also configure avd.conf with the settings of your setup.

avd.conf¶

The main file for Android environment settings is $CWD/conf/avd.conf, it contains all the generic configuration used to launch the Android emulator and run the analysis.

The file is largely commented and self-explanatory, but some important options are as follows:

emulator_path:

The path to the Android emulator (it is located inside Android SDK).

adb_path:

The path to the Android Debug Bridge utility (it is located inside Android SDK).

avd_path:

The path where the AVD images are located.

Preparing the Guest¶

At this point you should have configured the Cuckoo host component and you should have designed and defined the number and the names of the virtual machines you are going to use for malware execution.

Now it’s time to create such machines and to configure them properly. The following documentation details the entire setup guide for Windows-based Guests. For Linux Guests, please read Installing the Linux host.

Creation of the Virtual Machine¶

Once you have properly installed your virtualization software, you can proceed on creating all the virtual machines you need.

Using and configuring your virtualization software is out of the scope of this guide, so please refer to the official documentation.

Note

You can find some hints and considerations on how to design and create your virtualized environment in the Sandboxing chapter.

Note

We recommend either 64-bit Windows 7 or Windows XP virtual machines. For Windows 7 you will have to disable User Access Control.

Changed in version 2.0-rc2: We used to suggest Windows XP as a guest VM but nowadays a 64-bit Windows 7 machine yields much better results.

Note

KVM Users - Be sure to choose a hard drive image format that supports snapshots. See Saving the Virtual Machine for more information.

When creating the virtual machine, Cuckoo doesn’t require any specific configuration. You can choose the options that best fit your needs.

Requirements¶

In order to make Cuckoo run properly in your virtualized Windows system, you will have to install some required software and libraries.

Install Python¶

Python is a strict requirement for the Cuckoo guest component (analyzer) in order to run properly.

You can download the proper Windows installer from the official website. Also in this case Python 2.7 is preferred.

Some Python libraries are optional and provide some additional features to Cuckoo guest component. They include:

Python Pillow: it’s used for taking screenshots of the Windows desktop during the analysis.

They are not strictly required by Cuckoo to work properly, but you are encouraged to install them if you want to have access to all available features. Make sure to download and install the proper packages according to your Python version.

Additional Software¶

At this point you should have installed everything needed by Cuckoo to run properly.

Depending on what kind of files you want to analyze and what kind of sandboxed Windows environment you want to run the malware samples in, you might want to install additional software such as browsers, PDF readers, office suites etc. Remember to disable the “auto update” or “check for updates” feature of any additional software.

This is completely up to you and to what your needs are. You can get some hints by reading the Sandboxing chapter.

Network Configuration¶

Now it’s time to setup the network for your virtual machine.

Windows Settings¶

Before configuring the underlying networking of the virtual machine, you might want to tweak some settings inside Windows itself.

One of the most important things to do is disabling Windows Firewall and the Automatic Updates. The reason behind this is that they can affect the behavior of the malware under normal circumstances and that they can pollute the network analysis performed by Cuckoo, by dropping connections or including irrelevant requests.

You can do so from Windows’ Control Panel as shown in the picture:

Virtual Networking¶

Now you need to decide how to make your virtual machine able to access Internet or your local network.

While in previous releases Cuckoo used shared folders to exchange data between the Host and Guests, from release 0.4 it adopts a custom agent that works over the network using a simple XMLRPC protocol.

In order to make it work properly you’ll have to configure your machine’s network so that the Host and the Guest can communicate. Testing the network access by pinging a guest is a good practice, to make sure the virtual network was set up correctly. Use only static IP addresses for your guest, as Cuckoo doesn’t support DHCP and using it will break your setup.

This stage is very much up to your own requirements and to the characteristics of your virtualization software.

Warning

Virtual networking errors! Virtual networking is a vital component for Cuckoo, you must be really sure to get connectivity between host and guest. Most of the issues reported by users are related to a wrong setup of their networking. If you aren’t sure about that check your virtualization software documentation and test connectivity with ping and telnet.

The recommended setup is using a Host-Only networking layout with proper forwarding. More on such network routing can be found in Per-Analysis Network Routing, which is part of the host machine setup.

Installing the Agent¶

From release 0.4 Cuckoo adopts a custom agent that runs inside the Guest and that handles the communication and the exchange of data with the Host. This agent is designed to be cross-platform, therefore you should be able to use it on Windows, Android, Linux, and Mac OS X. In order to make Cuckoo work properly, you’ll have to install and start this agent.

It’s quite simple.

In the $CWD/agent/ directory you will find the agent.py file. Copy this file to the Guest operating system (in whatever way you want, perhaps a temporary shared folder or by downloading it from a webserver on the host, we recommend the latter) and run it. The Agent will launch a small API server that the host will be able to talk to.

On Windows simply launching the script will also spawn a Python window, if you want to hide it you can rename the file from agent.py to agent.pyw which will prevent the console window from spawning.

If you want the script to be launched at Windows’ boot, just place the file in the Startup folder.

Saving the Virtual Machine¶

Now you should be ready to save the virtual machine to a snapshot state.

Before doing this make sure you rebooted it softly and that it’s currently running, with Cuckoo’s agent running and with Windows fully booted.

Now you can proceed saving the machine. The way to do it obviously depends on the virtualization software you decided to use.

If you follow all the below steps properly, your virtual machine should be ready to be used by Cuckoo.

VirtualBox¶

If you are going for VirtualBox you can take the snapshot from the graphical user interface or from the command line:

$ VBoxManage snapshot "<Name of VM>" take "<Name of snapshot>" --pause

After the snapshot creation is completed, you can power off the machine and restore it:

$ VBoxManage controlvm "<Name of VM>" poweroff
$ VBoxManage snapshot "<Name of VM>" restorecurrent

KVM¶

If decided to adopt KVM, you must first of all be sure to use a disk format for your virtual machines which supports snapshots. By default libvirt tools create RAW virtual disks, and since we need snapshots you’ll either have to use QCOW2 or LVM. For the scope of this guide we adopt QCOW2, which is easier to setup than LVM.

The easiest way to create such a virtual disk correctly is using the tools provided by the libvirt suite. You can either use virsh if you prefer command-line interfaces or virt-manager for a nice GUI. You should be able to directly create it in QCOW2 format, but in case you have a RAW disk you can convert it like this:

$ cd /your/disk/image/path
$ qemu-img convert -O qcow2 your_disk.raw your_disk.qcow2

Now you have to edit your VM definition as follows:

$ virsh edit "<Name of VM>"

Find the disk section, it looks like this:

<disk type='file' device='disk'>
    <driver name='qemu' type='raw'/>
    <source file='/your/disk/image/path/your_disk.raw'/>
    <target dev='hda' bus='ide'/>
    <address type='drive' controller='0' bus='0' unit='0'/>
</disk>

And change “type” to qcow2 and “source file” to your qcow2 disk image, like this:

<disk type='file' device='disk'>
    <driver name='qemu' type='qcow2'/>
    <source file='/your/disk/image/path/your_disk.qcow2'/>
    <target dev='hda' bus='ide'/>
    <address type='drive' controller='0' bus='0' unit='0'/>
</disk>

Now test your virtual machine, if everything works prepare it for snapshotting while running Cuckoo’s agent. This means the virtual machine needs to be running while you are taking the snapshot. Then you can shut it down. You can finally take a snapshot with the following command:

$ virsh snapshot-create "<Name of VM>"

Having multiple snapshots can cause errors:

ERROR: No snapshot found for virtual machine VM-Name

VM snapshots can be managed using the following commands:

$ virsh snapshot-list "VM-Name"
$ virsh snapshot-delete "VM-Name" 1234567890

VMware Workstation¶

If you decided to adopt VMware Workstation, you can take the snapshot from the graphical user interface or from the command line:

$ vmrun snapshot "/your/disk/image/path/wmware_image_name.vmx" your_snapshot_name

Where your_snapshot_name is the name you choose for the snapshot. After that power off the machine from the GUI or from the command line:

$ vmrun stop "/your/disk/image/path/wmware_image_name.vmx" hard

XenServer¶

If you decided to adopt XenServer, the XenServer machinery supports starting virtual machines from either disk or a memory snapshot. Creating and reverting memory snapshots require that the Xen guest tools be installed in the virtual machine. The recommended method of booting XenServer virtual machines is through memory snapshots because they can greatly reduce the boot time of virtual machines during analysis. If, however, the option of installing the guest tools is not available, the virtual machine can be configured to have its disks reset on boot. Resetting the disk ensures that malware samples cannot permanently modify the virtual machine.

Memory Snapshots¶

The Xen guest tools can be installed from the XenCenter application that ships with XenServer. Once installed, restart the virtual machine and ensure that the Cuckoo agent is running.

Snapshots can be taken through the XenCenter application and the command line interface on the control domain (Dom0). When creating the snapshot from XenCenter, ensure that the “Snapshot disk and memory” is checked. Once created, right-click on the snapshot and note the snapshot UUID.

To snapshot from the command line interface, run the following command:

$ xe vm-checkpoint vm="vm_uuid_or_name" new-name-label="Snapshot Name/Description"

The snapshot UUID is printed to the screen once the command completes.

Regardless of how the snapshot was created, save the UUID in the virtual machine’s configuration section. Once the snapshot has been created, you can shutdown the virtual machine.

Booting from Disk¶

If you can’t install the Xen guest tools or if you don’t need to use memory snapshots, you will need to ensure that the virtual machine’s disks are reset on boot and that the Cuckoo agent is set to run at boot time.

Running the agent at boot time can be configured in Windows by adding a startup item for the agent.

The following commands must be run while the virtual machine is powered off.

To set the virtual machine’s disks to reset on boot, you’ll first need to list all the attached disks for the virtual machine. To list all attached disks, run the following command:

$ xe vm-disk-list vm="vm_name_or_uuid"

Ignoring all CD-ROM and read-only disks, run the following command for each remaining disk to change it’s behavior to reset on boot:

$ xe vdi-param-set uuid="vdi_uuid" on-boot=reset

After the disk is set to reset on boot, no permanent changes can be made to the virtual machine’s disk. Modifications that occur while a virtual machine is running will not persist past shutdown.

Cloning the Virtual Machine¶

In case you planned to use more than one virtual machine, there’s no need to repeat all the steps done so far: you can clone it. In this way you’ll have a copy of the original virtualized Windows with all requirements already installed.

The new virtual machine will also contain all the settings of the original one, which is not good. Now you need to proceed repeating the steps explained in Network Configuration, Installing the Agent and Saving the Virtual Machine for this new machine.

Installing the Linux host¶

First prepare the networking for your machinery platform on the host side. If you use VirtualBox with e.g. host-only interfaces and you have a vboxnet0 interface, you do not need to install additional dependencies.

If you use QEMU, you may need to install additional dependencies on the host:

$ sudo apt install uml-utilities bridge-utils

Next, get the list of virtual machines for which to configure the interface from conf/qemu.conf. For example, ubuntu_x32, ubuntu_x64, ubuntu_arm, ubuntu_mips, ubuntu_mipsel, et cetera. For each VM, preconfigure a network tap interfaces on the host, required to avoid have to start as root, e.g.:

$ sudo tunctl -b -u cuckoo -t tap_ubuntu_x32
$ sudo ip link set tap_ubuntu_x32 master br0
$ sudo ip link set dev tap_ubuntu_x32 up
$ sudo ip link set dev br0 up

$ sudo tunctl -b -u cuckoo -t tap_ubuntu_x64
$ sudo ip link set tap_ubuntu_x64 master br0
$ sudo ip link set dev tap_ubuntu_x64 up
$ sudo ip link set dev br0 up

Note that if you run Cuckoo as a different user, replace ``cuckoo`` after -u with your user.

Preparing x32/x64 Ubuntu 18.04 Linux guests¶

Ensure the agent automatically starts. The easiest way is to add it to crontab:

$ sudo crontab -e
@reboot python /path/to/agent.py

Install dependencies inside of the virtual machine:

$ sudo apt-get install systemtap gcc patch linux-headers-$(uname -r)

Install kernel debugging symbols:

$ sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys C8CAB6595FDFF622

$ codename=$(lsb_release -cs)
$ sudo tee /etc/apt/sources.list.d/ddebs.list << EOF
  deb http://ddebs.ubuntu.com/ ${codename}          main restricted universe multiverse
  #deb http://ddebs.ubuntu.com/ ${codename}-security main restricted universe multiverse
  deb http://ddebs.ubuntu.com/ ${codename}-updates  main restricted universe multiverse
  deb http://ddebs.ubuntu.com/ ${codename}-proposed main restricted universe multiverse
EOF

$ sudo apt-get update
$ sudo apt-get install linux-image-$(uname -r)-dbgsym

(For Debian 9 amd64) Install kernel debugging symbols:

$ sudo apt-get install linux-image-$(uname -r)-dbg

Patch the SystemTap tapset, so that the Cuckoo analyzer can properly parse the output:

$ wget https://raw.githubusercontent.com/cuckoosandbox/cuckoo/master/stuff/systemtap/expand_execve_envp.patch
$ wget https://raw.githubusercontent.com/cuckoosandbox/cuckoo/master/stuff/systemtap/escape_delimiters.patch
$ sudo patch /usr/share/systemtap/tapset/linux/sysc_execve.stp < expand_execve_envp.patch
$ sudo patch /usr/share/systemtap/tapset/uconversions.stp < escape_delimiters.patch

Compile the kernel extension:

$ wget https://raw.githubusercontent.com/cuckoosandbox/cuckoo/master/stuff/systemtap/strace.stp
$ sudo stap -p4 -r $(uname -r) strace.stp -m stap_ -v

Once the compilation finishes you should see the file stap_.ko in the same folder. You will now be able to test the STAP kernel extension as follows:

$ sudo staprun -v ./stap_.ko

Output should be something like as follows:

staprun:insert_module:x Module stap_ inserted from file path_to_stap_.ko

The stap_.ko file should be placed in /root/.cuckoo:

$ sudo mkdir /root/.cuckoo
$ sudo mv stap_.ko /root/.cuckoo/

Disable the firewall inside of the VM, if it exists:

$ sudo ufw disable

Disable NTP inside of the VM:

$ sudo timedatectl set-ntp off

Optional - preinstalled remove software and configurations:

$ sudo apt-get purge update-notifier update-manager update-manager-core ubuntu-release-upgrader-core
$ sudo apt-get purge whoopsie ntpdate cups-daemon avahi-autoipd avahi-daemon avahi-utils
$ sudo apt-get purge account-plugin-salut libnss-mdns telepathy-salut

It is recommended to configure the Linux guest with a static IP addresses. Make sure the machine entry in the configuration has the correct IP address and has the platform variable set to linux. Create a snapshot once the VM has been configured. It is now ready for analysis!

Preparing the Guest (Physical Machine)¶

Warning

This chapter only applies for a Physical Machine setup! For normal Cuckoo usage please ignore it.

At this point you should have configured the Cuckoo host component and you should have designed and defined the number and the names of the physical machines you are going to use for malware execution.

Now it’s time to create such machines and to configure them properly.

Creation of the Physical Machine¶

Once you have properly installed your imaging software, you can proceed on creating all the physical machines you need.

Using and configuring your imaging software is out of the scope of this guide, so please refer to the official documentation.

Note

You can find some hints and considerations on how to design and create your virtualized environment in the Sandboxing chapter.

Note

For analysis purposes you are recommended to use Windows XP Service Pack 3, but Cuckoo Sandbox also proved to work with Windows 7 with User Access Control disabled.

When creating the physical machine, Cuckoo doesn’t require any specific configuration. You can choose the options that best fit your needs.

Requirements¶

In order to make Cuckoo run properly in your physical Windows system, you will have to install some required software and libraries.

Install Python¶

Python is a strict requirement for the Cuckoo guest component (analyzer) in order to run properly.

You can download the proper Windows installer from the official website. Also in this case Python 2.7 is preferred.

Some Python libraries are optional and provide some additional features to Cuckoo guest component. They include:

Python Pillow: it’s used for taking screenshots of the Windows desktop during the analysis.

They are not strictly required by Cuckoo to work properly, but you are encouraged to install them if you want to have access to all available features. Make sure to download and install the proper packages according to your Python version.

NOTE: Physical machinery is currently not supported by the new cuckoo agent. Please use the old cuckoo agent for physical machinery in the meantime.

Additional Software¶

At this point you should have installed everything needed by Cuckoo to run properly.

Depending on what kind of files you want to analyze and what kind of sandboxed Windows environment you want to run the malware samples in, you might want to install additional software such as browsers, PDF readers, office suites etc. Remember to disable the “auto update” or “check for updates” feature of any additional software.

This is completely up to you and to what your needs are. You can get some hints by reading the Sandboxing chapter.

Additional Host Requirements¶

The physical machine manager uses RPC requests to reboot physical machines. The net command is required for this to be accomplished, and is available from the samba-common-bin package.

On Debian/Ubuntu you can install it with:

$ sudo apt-get install samba-common-bin

In order for the physical machine manager to work, you must have a way for physical machines to be returned to a clean state. In development/testing Fog was used as a platform to handle re-imaging the physical machines. However, any re-imaging platform can be used (Clonezilla, Deepfreeze, etc) to accomplish this.

Cuckoo Configuration Requirements¶

Since we are using physical machines to perform our analysis, we must account for the reboot/rebuild time of our physical machines in our Cuckoo configuration. Specifically, we must modify the vm_state timeout as specified in conf/cuckoo.conf:

vm_state = 60

By default, this value is set to 60 (seconds). We need to update it so that it reflects the amount of time required to reboot and rebuild the physical guest. In testing 10 minutes (i.e., vm_state = 600) has proven sufficient, provided a Windows 7 setup with a 1 gbit connection. However, it is recommended that you analyze the time it takes to reboot/rebuild the phyical machine in your environment before setting this value.

Network Configuration¶

Now it’s time to setup the network for your physical machine.

Windows Settings¶

Before configuring the underlying networking of the sandbox, you might want to tweak some settings inside Windows itself.

One of the most important things to do is disabling Windows Firewall and the Automatic Updates. The reason behind this is that they can affect the behavior of the malware under normal circumstances and that they can pollute the network analysis performed by Cuckoo, by dropping connections or including irrelevant requests.

You can do so from Windows’ Control Panel as shown in the picture:

Using a physical machine manager requires a few more configuration options than the virtual machine managers in order to run properly. In addition to the steps laid out in the regular Preparing the Guest section, some settings need to be changed for physical machines to work properly.

Enable auto-logon (Allows for the agent to start upon reboot)

Enable Remote RPC (Allows for Cuckoo to reboot the sandbox using RPC)

Turn off paging (Optional)

Disable Screen Saver (Optional)

In Windows 7 the following commands can be entered into an Administrative command prompt to enable auto-logon and Remote RPC.

reg add "hklm\software\Microsoft\Windows NT\CurrentVersion\WinLogon" /v DefaultUserName /d <USERNAME> /t REG_SZ /f
reg add "hklm\software\Microsoft\Windows NT\CurrentVersion\WinLogon" /v DefaultPassword /d <PASSWORD> /t REG_SZ /f
reg add "hklm\software\Microsoft\Windows NT\CurrentVersion\WinLogon" /v AutoAdminLogon /d 1 /t REG_SZ /f
reg add "hklm\system\CurrentControlSet\Control\TerminalServer" /v AllowRemoteRPC /d 0x01 /t REG_DWORD /f
reg add "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System" /v LocalAccountTokenFilterPolicy /d 0x01 /t REG_DWORD /f

Networking¶

Now you need to decide how to make your physical machine able to access Internet or your local network.

While in previous releases Cuckoo used shared folders to exchange data between the Host and Guests, from release 0.4 it adopts a custom agent that works over the network using a simple XMLRPC protocol.

In order to make it work properly you’ll have to configure your machine’s network so that the Host and the Guest can communicate. Testing the network access by pinging a guest is a good practice, to make sure the virtual network was set up correctly. Use only static IP addresses for your guest, as today Cuckoo doesn’t support DHCP and using it will break your setup.

This stage is very much up to your own requirements and to the characteristics of your virtualization software.

For physical machines, make sure when setting the IP address of the guest to also set the Gateway and DNS server to be the IP address of the Cuckoo server on the physical network. For example, if your Cuckoo server has the IP address of 192.168.1.1, then you would set the Gateway and DNS server in Windows Settings to be 192.168.1.1 as well.

Installing the Agent¶

Installing the Agent on a Physical machine is the same as installing it in a Virtual Machine, therefore please refer to Installing the Agent.

Saving the Guest¶

Now you should be ready to save the physical machine to a clean state. In order for the physical machine manager to work, you must have a way for physical machines to be returned to a clean state.

Before doing this make sure you rebooted it softly and that it’s currently running, with Cuckoo’s agent running and with Windows fully booted.

Now you can proceed saving the machine. The way to do it obviously depends on the imaging software you decided to use.

In development/testing Fog (http://www.fogproject.org/) was used as a platform to handle re-imaging the physical machines. However, any re-imaging platform can be used (Clonezilla, Deepfreeze, etc.) to accomplish this.

If you follow all the below steps properly, your virtual machine should be ready to be used by Cuckoo.

Fog¶

After installing Fog, you will need to create an image and add an image and a host to the Fog server.

To add an image to the fog server, open the Image Management window (http://<your_fog_server>/fog/management/index.php?node=images) and click “Create New Image.” Provide the proper inputs for your OS configuration and click “Add”

Next you will need to add the host you plan to re-image to Fog. To add a host, open a web browser and navigate to the Host Management page of Fog (http://<your_fog_server>/fog/management/index.php?node=host). Click “Create New Host.” Provide the proper inputs for your host configuration. Be sure to select the image you created above from the “Host Image” option, when finished click the “Add” button.

At this point you should be ready to take an image from the guest machine. In order to take an image you will need to navigate to the Task Management page and list all hosts (http://<your_fog_server>/fog/management/index.php?node=tasks&sub=listhosts). From here you should be able to click the Capture icon, which should instantly add a task to the queue to take an image. Now you should reboot your Cuckoo guest image and it should PXE boot into Fog and capture the base image from the cuckoo guest.

Now that you have created and capture an image in FOG, Cuckoo will use this image to rebuild the guest machine after each analysis task. If you have provided Cuckoo with valid FOG credentials and enabled Remote RPC (as shown in the Network Configuration section), Cuckoo will automatically schedule the Deploy Task in FOG and will also reboot the guest machine for you.

Setup using VMWare (Bonus!)¶

Traditionally Cuckoo requires to be running some sort of virtualization software (e.g. VMware, Virtualbox, etc). The physical machine manager will also work with other virtual machines, so long as they are configured to revert to a snapshot on shutdown/reboot, and running the agent.py script. A use case for this functionality would be to run the cuckoo server and the guest sandboxes each in their own virtual machine on a single host, allowing for development/testing of Cuckoo without requiring a dedicated Linux host.

Upgrading from a previous release¶

Upgrading post version 2.0.0¶

When updating your Cuckoo from a >=2.0.0 version, Cuckoo will automatically try to upgrade your Cuckoo Working Directory (CWD). If any actions are required, such as running automatically database migrations or backing up an edited file in your CWD, Cuckoo will notify you.

To start an upgrade after updating Cuckoo, simply start Cuckoo as you normally would.

Upgrading to Cuckoo from legacy Cuckoo¶

Legacy Cuckoo is any Cuckoo version older than 2.0.0. These versions use a working directory inside of the Cuckoo installation path. Newer Cuckoo versions use a Cuckoo Working Directory.

This document describes the process of importing an older legacy Cuckoo setup in order to upgrade your Cuckoo to the latest and greatest version. This importing process is possible for Cuckoo 0.6 and upwards. Naturally it doesn’t re-apply any custom code changes that you applied to your old setup, but it does migrate your database, configuration, and analyses to the new version (in a best-effort manner).

Now, in order to upgrade your setup, you’ll simply have to perform the following steps:

Come up with a Cuckoo Working Directory for the new setup (although the default one should work just fine, assuming it doesn’t exist yet).
Optionally create a backup of your data (Cuckoo will also offer to do this for you before doing the actual setup import).
Run the cuckoo import command.

The cuckoo import command¶

The cuckoo import App performs a number of steps in order to import an older setup. Previously we had manual steps for performing a database migration, these have been integrated in the import process as well.

Note

Only use the import command if you are upgrading from a Cuckoo version pre 2.0.0.

The usage of cuckoo import is as follows:

$ cuckoo import --help
Usage: cuckoo import [OPTIONS] PATH

  Imports an older Cuckoo setup into a new CWD. The old setup should be
  identified by PATH and the new CWD may be specified with the --cwd
  parameter, e.g., "cuckoo --cwd /tmp/cwd import old-cuckoo".

Options:
  --copy     Copy all existing analyses to the new CWD (default)
  --move     Move all existing analyses to the new CWD
  --symlink  Symlink all existing analyses to the new CWD
  --help  Show this message and exit.

As per the limited usage documentation of this command, there is an input and an output directory and a couple of different modes. The rest is done by cuckoo import according to best-practice manners.

The three different modes are best described as follows. Keep in mind that these modes only inform the importing process on what to do with the existing analyses and, in the case of sqlite3 usage, the database file. These modes do not apply to any other used databases or data not mentioned in this document.

copy: Copies all the analyses from the old setup to the new CWD. In this mode the old storage/ folder will be copied to $CWD/storage/. The copy mode is useful if you want to maintain a backup of the old setup and its analyses, allowing one to restore it with the appropriate SQL backup. Note that this mode will double the size of your existing analyses directory as it does a full copy.
move: Moves all the analyses from the old setup to the new CWD. In this mode the old storage/ folder is moved to $CWD/storage/. After the import process you won’t have a backup of your old data anymore, but you will be able to reference to it in the new CWD / setup.
symlink: Creates a symbolic link from each analysis in the old setup, i.e., storage/analyses/XYZ, to the new CWD, i.e., $CWD/storage/XYZ. This method is the most desired (as you’ll be able to access the existing analyses in both the old setup as well as the new CWD), but doesn’t work on Windows.

The default mode is copy due to its feature of remaining available on both the old setup as well as the new CWD as well as being cross-platform (i.e., symlink mode isn’t supported on Windows). After reading this documentation one may opt to go for symlink or move mode on non-Windows systems and move mode on Windows systems, though.

Following are the steps taken by Cuckoo when performing an import:

The user has to accept a non-binding EULA-like agreement that (just kidding) attempts to inform him or her regarding the implications of importing an older setup.
The version of the old Cuckoo setup is identified.
It is ensured that the new CWD does not already exist.
The old Cuckoo Configuration is read, migrated, and then validated to be fit for usage with the new Cuckoo version, i.e., you can configure a Cuckoo 0.6 setup and migrate it all the way to the latest version and it will simply work.
The new CWD is created and it is configured with the migrated configuration.
The user is prompted to optionally create a SQL database backup. On Linux-based systems this should work out of the box (and you’ll get a hard error otherwise), but due to issues with $PATH this may require manually fixing up the command on Windows & Mac OS X systems.
After the ability to create a SQL database backup, the database schema is migrated to the latest version in-place, i.e., you will not be able to use your old Cuckoo setup with this database anymore (hence the backup).
Any and all existing analyses are imported to the new CWD using the mode as specified, or if it has not been specified, the default copy method.

You are now the happy owner of an up-to-date Cuckoo setup. Please inform us of any feedback that you may have through one of the various communication channels that we’ve put in-place.

Warning

One should not clean the old Cuckoo setup after the import. By attempting to do so you may lose the existing analyses (if symlink mode is used) and the SQL, MongoDB, and ElasticSearch databases.

Usage¶

This chapter explains how to use Cuckoo.

Starting Cuckoo¶

To start Cuckoo use the command:

$ cuckoo

You will get an output similar to this:

  eeee e   e eeee e   e  eeeee eeeee
  8  8 8   8 8  8 8   8  8  88 8  88
  8e   8e  8 8e   8eee8e 8   8 8   8
  88   88  8 88   88   8 8   8 8   8
  88e8 88ee8 88e8 88   8 8eee8 8eee8

 Cuckoo Sandbox 2.0.0
 www.cuckoosandbox.org
 Copyright (c) 2010-2017

 Checking for updates...
 Good! You have the latest version available.

2017-03-31 17:08:53,527 [cuckoo.core.scheduler] INFO: Using "virtualbox" as machine manager
2017-03-31 17:08:53,935 [cuckoo.core.scheduler] INFO: Loaded 1 machine/s
2017-03-31 17:08:53,964 [cuckoo.core.scheduler] INFO: Waiting for analysis tasks.

Note that Cuckoo checks for updates on a remote API located at api.cuckoosandbox.org. You can avoid this by disabling the version_check option in the configuration file.

Now Cuckoo is ready to run and it’s waiting for submissions.

cuckoo accepts some command line options as shown by the help:

$ cuckoo --help
Usage: cuckoo [OPTIONS] COMMAND [ARGS]...

Invokes the Cuckoo daemon or one of its subcommands.

To be able to use different Cuckoo configurations on the same
machine with the same Cuckoo installation, we use the so-called
Cuckoo Working Directory (aka "CWD"). A default CWD is
available, but may be overridden through the following options -
listed in order of precedence.

* Command-line option (--cwd)
* Environment option ("CUCKOO")
* Environment option ("CUCKOO_CWD")
* Current directory (if the ".cwd" file exists)
* Default value ("~/.cuckoo")

Options:
  -d, --debug             Enable verbose logging
  -q, --quiet             Only log warnings and critical messages
  -m, --maxcount INTEGER  Maximum number of analyses to process
  --user TEXT             Drop privileges to this user
  --cwd TEXT              Cuckoo Working Directory
  --help                  Show this message and exit.

Commands:
  api          Operate the Cuckoo REST API.
  clean        Clean the CWD and associated databases.
  community    Fetch supplies from the Cuckoo Community.
  distributed  Distributed Cuckoo helper utilities.
  dnsserve     Custom DNS server.
  import       Imports an older Cuckoo setup into a new CWD.
  init         Initializes Cuckoo and its configuration.
  machine      Dynamically add/remove machines.
  migrate      Perform database migrations.
  process      Process raw task data into reports.
  rooter       Instantiates the Cuckoo Rooter.
  submit       Submit one or more files or URLs to Cuckoo.
  web          Operate the Cuckoo Web Interface.

The --debug and --quiet flags increase and decrease the logging verbosity for the cuckoo command or any of its subcommands.

Cuckoo in the background¶

Running Cuckoo manually is useful the first few times you start using it, but if you’re running multiple machines with Cuckoo on it, you will want the process of running Cuckoo to be automated.

Fortunately Cuckoo will automatically provide one with a supervisord.conf file in the Cuckoo Working Directory (this topic will be explained on the next page) which may be started either by running supervisord from the CWD directory, or by providing the configuration directly to supervisord as follows:

$ supervisord -c $CWD/supervisord.conf

It should be noted that, by default, supervisord will also start four Processing Utility instances, which means that, as per its documentation, the process_results configuration in $CWD/conf/cuckoo.conf should be disabled (i.e., change the value from on to off).

From there on, one may start and stop the various cuckoo processes (i.e., the main cuckoo process and the four processing instances) by running commands such as the following (assuming that they’re run from the CWD):

# Stop the Cuckoo daemon and the processing utilities.
$ supervisorctl stop cuckoo:

# Start the Cuckoo daemon and the processing utilities.
$ supervisorctl start cuckoo:

Note that you’ll need the trailing colon (i.e., cuckoo:) so to denote the Cuckoo supervisor group, containing the Cuckoo daemon process as well as the various processing utilities.

Cuckoo Working Directory Usage¶

Note

Before reading this page, please read on installing Cuckoo and the Cuckoo Working Directory.

Before we go into the subject of using the CWD we’re first going to walk you through the many improvements on your Quality of Life during your daily usage of Cuckoo Sandbox with the introduction of the Cuckoo Package and CWD and some of the new features that come along with this.

So simply put, the CWD is a per-Cuckoo instance configuration directory. While people generally speaking only run one Cuckoo instance per server, this still yields a lot of maintenance-related improvements:

As outlined by Installing Cuckoo installing Cuckoo and updating it will now be pretty much pip install -U cuckoo.
Due to Cuckoo now being an official Python Package we have a much tighter control on how its installed on users’ systems. No longer will users have incorrect versions of third party libraries installed breaking their setup.
Because updating is much easier (again, pip install -U cuckoo) we will be able to put out new versions more often. E.g., when one or more users run into a bug, we’ll be able to put out a fix quickly - this has happened a few times in the past in a way that we weren’t able to properly mitigate such issues (leaving users high & dry for months).
The Cuckoo Configuration is no longer part of the Git repository. Users who have updated Cuckoo in the past will have seen the effort involved in making a backup of their configuration, pulling a new version of Cuckoo, and either restoring their old configuration or applying the configuration against the new Cuckoo version by hand.
With the new CWD all configurable files will be in one centralized place in logically structured subdirectories.
Given that a CWD denotes one Cuckoo instance, it is possible to have multiple Cuckoo instances through multiple CWD’s while having installed/deployed Cuckoo only once.
With the addition of the cuckoo executable and its associated Cuckoo Apps (subcommands) the various Cuckoo commands are now centralized into one command.

Usage¶

After having installed the Cuckoo Package (Installing Cuckoo) and setup the initial Cuckoo Working Directory (Cuckoo Working Directory) it is time to actually get started with Cuckoo. Just to reiterate, installing the latest version of Cuckoo in a virtualenv environment may look roughly as follows (note the pip install -U pip setuptools, for more information see also DistributionNotFound / No distribution matching the version..).

$ virtualenv venv
$ . venv/bin/activate
(venv)$ pip install -U pip setuptools
(venv)$ pip install -U cuckoo
(venv)$ cuckoo --cwd ~/.cuckoo

First of all you’ll probably want to update the default Cuckoo configuration in the $CWD/conf/ directory. If just to switch from the default SQLite3 database to, e.g., PostgreSQL, or to register some virtual machines (more information on setting up Virtual Machines can be found in Preparing the Guest). Note that in order to view the results of analyses in the Web Interface later on it is necessary to enable the mongodb reporting module in $CWD/conf/reporting.conf (see also Web interface).

We then proceed by downloading the Cuckoo Community which includes over 300 Cuckoo Signatures which summarize a wide array of malicious behavior in a digestible way, simplifying the final results of an analysis. Downloading the Cuckoo Community into our CWD may be done as follows:

(venv)$ cuckoo community

Alternatively, if you have a local copy of the community .tar.gz file (e.g., after running wget https://github.com/cuckoosandbox/community/archive/master.tar.gz) this can be imported as follows:

(venv)$ cuckoo community --file master.tar.gz

Now we’re good to go let’s submit some samples and URLs using the command-line Submission Utility. Note that multiple tasks may be submitted at once:

(venv)$ cuckoo submit /tmp/sample1.exe /tmp/sample2.exe /tmp/sample3.exe
Success: File "/tmp/sample1.exe" added as task with ID #1
Success: File "/tmp/sample2.exe" added as task with ID #2
Success: File "/tmp/sample3.exe" added as task with ID #3
(venv)$ cuckoo submit --url google.com bing.com
Success: URL "google.com" added as task with ID #4
Success: URL "bing.com" added as task with ID #5

For the actual analysis of these samples, one will have to run the Cuckoo daemon. Which is equally straightforward. Do keep in mind that, by default, the command will run indefinitely (unless a maximum analysis count was provided through the -m parameter, e.g., -m 5).

# This command is equal to what used to be "./cuckoo.py -d".
(venv)$ cuckoo -d

Now in order to inspect the analyses that have run we start the Web Interface. For small and/or home setups this may be done using the built-in Django web server as follows, although we recommend a proper Web Deployment for any bigger setup.

(venv)$ cuckoo web
Performing system checks...

System check identified no issues (0 silenced).
March 31, 2017 - 12:10:46
Django version 1.8.4, using settings 'cuckoo.web.web.settings'
Starting development server at http://localhost:8000/
Quit the server with CONTROL-C.

There are some additional Cuckoo Apps such as cuckoo clean (Clean all Tasks and Samples), the Cuckoo Rooter, and various other utilities listed in Cuckoo Apps, but other than that there’s not much more to learn about installing and running Cuckoo Sandbox - so, happy analyzing.

Submission Utility¶

The easiest way to submit an analysis is to use the cuckoo submit utility. It currently has the following options available:

$ cuckoo submit --help
Usage: cuckoo submit [OPTIONS] [TARGET]...

  Submit one or more files or URLs to Cuckoo.

Options:
  -u, --url           Submitting URLs instead of samples
  -o, --options TEXT  Options for these tasks
  --package TEXT      Analysis package to use
  --custom TEXT       Custom information to pass along this task
  --owner TEXT        Owner of this task
  --timeout INTEGER   Analysis time in seconds
  --priority INTEGER  Priority of this task
  --machine TEXT      Machine to analyze these tasks on
  --platform TEXT     Analysis platform
  --memory            Enable memory dumping
  --enforce-timeout   Don't terminate the analysis early
  --clock TEXT        Set the system clock
  --tags TEXT         Analysis tags
  --baseline          Create baseline task
  --remote TEXT       Submit to a remote Cuckoo instance
  --shuffle           Shuffle the submitted tasks
  --pattern TEXT      Provide a glob-pattern when submitting a
                      directory
  --max INTEGER       Submit up to X tasks at once
  --unique            Only submit samples that have not been
                      analyzed before
  -d, --debug         Enable verbose logging
  -q, --quiet         Only log warnings and critical messages
  --help              Show this message and exit.

You may specify multiple files or directories at once. For directories cuckoo submit will enumerate all its files and submit them one by one.

The concept of analysis packages will be dealt later in this documentation (at Analysis Packages). Following are some usage examples:

Example: submit a local binary:

$ cuckoo submit /path/to/binary

Example: submit an URL:

$ cuckoo submit --url http://www.example.com

Example: submit a local binary and specify an higher priority:

$ cuckoo submit --priority 5 /path/to/binary

Example: submit a local binary and specify a custom analysis timeout of 60 seconds:

$ cuckoo submit --timeout 60 /path/to/binary

Example: submit a local binary and specify a custom analysis package:

$ cuckoo submit --package <name of package> /path/to/binary

Example: submit a local binary and specify a custom route:

$ cuckoo submit -o route=tor /path/to/binary

Example: submit a local binary and specify a custom analysis package and some options (in this case a command line argument for the malware):

$ cuckoo submit --package exe --options arguments=--dosomething /path/to/binary.exe

Example: submit a local binary to be run on virtual machine cuckoo1:

$ cuckoo submit --machine cuckoo1 /path/to/binary

Example: submit a local binary to be run on a Windows machine:

$ cuckoo submit --platform windows /path/to/binary

Example: submit a local binary and take a full memory dump of the analysis machine:

$ cuckoo submit --memory /path/to/binary

Example: submit a local binary and force the analysis to be executed for the full timeout (disregarding the internal mechanism that Cuckoo uses to decide when to terminate the analysis):

$ cuckoo submit --enforce-timeout /path/to/binary

Example: submit a local binary and set virtual machine clock. Format is %m-%d-%Y %H:%M:%S. If not specified, the current time is used. For example if we want run a sample the 24 january 2001 at 14:41:20:

$ cuckoo submit --clock "01-24-2001 14:41:20" /path/to/binary

Example: submit a sample for Volatility analysis (to reduce side effects of the cuckoo hooking, switch it off with options free=True):

$ cuckoo submit --memory --options free=yes /path/to/binary

API¶

Detailed usage of the REST API interface is described in REST API.

Distributed Cuckoo¶

Detailed usage of the Distributed Cuckoo API interface is described in Distributed Cuckoo.

Python Functions¶

In order to keep track of submissions, samples and overall execution, Cuckoo uses a popular Python ORM called SQLAlchemy that allows you to make the sandbox use SQLite, MySQL or MariaDB, PostgreSQL and several other SQL database systems.

Cuckoo is designed to be easily integrated in larger solutions and to be fully automated. In order to automate analysis submission we suggest to use the REST API interface described in REST API, but in case you want to write your own Python submission script, you can also use the add_path() and add_url() functions.

add_path(file_path[, timeout=0[, package=None[, options=None[, priority=1[, custom=None[, owner=""[, machine=None[, platform=None[, tags=None[, memory=False[, enforce_timeout=False], clock=None[]]]]]]]]]]]]])¶

Add a local file to the list of pending analysis tasks. Returns the ID of the newly generated task.

Parameters:

file_path (string) – path to the file to submit
timeout (integer) – maximum amount of seconds to run the analysis for
package (string or None) – analysis package you want to use for the specified file
options (string or None) – list of options to be passed to the analysis package (in the format key=value,key=value)
priority (integer) – numeric representation of the priority to assign to the specified file (1 being low, 2 medium, 3 high)
custom (string or None) – custom value to be passed over and possibly reused at processing or reporting
owner (string or None) – task owner
machine (string or None) – Cuckoo identifier of the virtual machine you want to use, if none is specified one will be selected automatically
platform (string or None) – operating system platform you want to run the analysis one (currently only Windows)
tags (string or None) – tags for machine selection
memory (True or False) – set to True to generate a full memory dump of the analysis machine
enforce_timeout (True or False) – set to True to force the execution for the full timeout
clock (string or None) – provide a custom clock time to set in the analysis machine

Return type:

integer

Example usage:

>>> from cuckoo.core.database import Database
>>> db = Database()
>>> db.add_path("/tmp/malware.exe")
1
>>>

add_url(url[, timeout=0[, package=None[, options=None[, priority=1[, custom=None[, owner=""[, machine=None[, platform=None[, tags=None[, memory=False[, enforce_timeout=False], clock=None[]]]]]]]]]]]]])¶

Add a local file to the list of pending analysis tasks. Returns the ID of the newly generated task.

Parameters:

url (string) – URL to analyze
timeout (integer) – maximum amount of seconds to run the analysis for
package (string or None) – analysis package you want to use for the specified URL
options (string or None) – list of options to be passed to the analysis package (in the format key=value,key=value)
priority (integer) – numeric representation of the priority to assign to the specified URL (1 being low, 2 medium, 3 high)
custom (string or None) – custom value to be passed over and possibly reused at processing or reporting
owner (string or None) – task owner
machine (string or None) – Cuckoo identifier of the virtual machine you want to use, if none is specified one will be selected automatically
platform (string or None) – operating system platform you want to run the analysis one (currently only Windows)
tags (string or None) – tags for machine selection
memory (True or False) – set to True to generate a full memory dump of the analysis machine
enforce_timeout (True or False) – set to True to force the execution for the full timeout
clock (string or None) – provide a custom clock time to set in the analysis machine

Return type:

integer

Example Usage:

>>> from cuckoo.core.database import Database
>>> db = Database()
>>> db.connect()
>>> db.add_url("http://www.cuckoosandbox.org")
2
>>>

Web interface¶

Cuckoo provides a full-fledged web interface in the form of a Django application. This interface will allow you to submit files, browse through the reports, and search across all the analysis results.

Configuration¶

The web interface pulls data from a Mongo database, so having the Mongo reporting module enabled in reporting.conf is mandatory for the Web Interface to function. If that’s not the case, the Web Interface won’t be able to start and will instead raise an exception.

Some additional configuration options exist in the $CWD/web/local_settings.py configuration file.

# Copyright (C) 2013 Claudio Guarnieri.
# Copyright (C) 2014-2017 Cuckoo Foundation.
# This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org
# See the file 'docs/LICENSE' for copying permission.

import web.errors

# Maximum upload size (10GB, so there's basically no limit).
MAX_UPLOAD_SIZE = 10 * 1024 * 1024 * 1024

# Override default secret key stored in $CWD/web/.secret_key
# Make this unique, and don't share it with anybody.
# SECRET_KEY = "YOUR_RANDOM_KEY"

# Language code for this installation. All choices can be found here:
# http://www.i18nguy.com/unicode/language-identifiers.html
LANGUAGE_CODE = "en-us"

ADMINS = (
    # ("Your Name", "your_email@example.com"),
)

MANAGERS = ADMINS

# Allow verbose debug error message in case of application fault.
# It's strongly suggested to set it to False if you are serving the
# web application from a web server front-end (i.e. Apache).
DEBUG = False
DEBUG404 = False

# A list of strings representing the host/domain names that this Django site
# can serve.
# Values in this list can be fully qualified names (e.g. 'www.example.com').
# When DEBUG is True or when running tests, host validation is disabled; any
# host will be accepted. Thus it's usually only necessary to set it in production.
ALLOWED_HOSTS = ["*"]

handler404 = web.errors.handler404
handler500 = web.errors.handler500

#A list of strings representing the subnets or ipaddresses that can download 
#samples and dropped files 
#Values in this list can be ipv4 or ipv6 separated by "," 
#(e.g. '127.0.0.0/8,10.0.0.0/8,fd00::/8').
ALLOWED_FILEDOWNLOAD_SUBNETS = '127.0.0.0/8,10.0.0.0/8,fd00::/8'

It is recommended to keep the DEBUG variable at False in production setups and to configure at least one ADMIN entry to enable error notification by email.

Changed in version 2.0.0: The default maximum upload size has been bumped from 25 MB to 10 GB so that virtually any file should be accepted.

Starting the Web Interface¶

In order to start the web interface, you can simply run the following command from the web/ directory:

$ cuckoo web runserver

If you want to configure the web interface as listening for any IP on a specified port, you can start it with the following command (replace PORT with the desired port number):

$ cuckoo web runserver 0.0.0.0:PORT

Or directly without the runserver part as follows while also specifying the host to listen on:

$ cuckoo web -H 0

Web Deployment¶

While the default method of starting the Web Interface server works fine for many cases, some users may wish to deploy the server in a more robust manner. This can be done by exposing the Web Interface as a WSGI application to a web server. This section shows a simple example of deploying the Web Interface via uWSGI and nginx. These instructions are written with Ubuntu GNU/Linux in mind, but may be adapted to other platforms.

This solution requires uWSGI, the uWSGI Python plugin, and nginx. All are available as packages:

$ sudo apt-get install uwsgi uwsgi-plugin-python nginx

uWSGI setup¶

First, use uWSGI to run the Web Interface server as an application.

To begin, create a uWSGI configuration file at /etc/uwsgi/apps-available/cuckoo-web.ini that contains the actual configuration as reported by the cuckoo web --uwsgi command, e.g.:

$ cuckoo web --uwsgi
[uwsgi]
plugins = python
virtualenv = /home/cuckoo/cuckoo
module = cuckoo.web.web.wsgi
uid = cuckoo
gid = cuckoo
static-map = /static=/home/..somepath..
# If you're getting errors about the PYTHON_EGG_CACHE, then
# uncomment the following line and add some path that is
# writable from the defined user.
# env = PYTHON_EGG_CACHE=
env = CUCKOO_APP=web
env = CUCKOO_CWD=/home/..somepath..

This configuration inherits a number of settings from the distribution’s default uWSGI configuration and imports cuckoo.web.web.wsgi from the Cuckoo package to do the actual work. In this example we installed Cuckoo in a virtualenv located at /home/cuckoo/cuckoo. If Cuckoo is installed globally no virtualenv option is required (and cuckoo web --uwsgi would not report one).

Enable the app configuration and start the server.

$ sudo ln -s /etc/uwsgi/apps-available/cuckoo-web.ini /etc/uwsgi/apps-enabled/
$ sudo service uwsgi start cuckoo-web    # or reload, if already running

Note

Logs for the application may be found in the standard directory for distribution app instances, i.e., /var/log/uwsgi/app/cuckoo-web.log. The UNIX socket is created in a conventional location as well, /run/uwsgi/app/cuckoo-web/socket.

nginx setup¶

With the Web Interface server running in uWSGI, nginx can now be set up to run as a web server/reverse proxy, backending HTTP requests to it.

To begin, create a nginx configuration file at /etc/nginx/sites-available/cuckoo-web that contains the actual configuration as reported by the cuckoo web --nginx command:

$ cuckoo web --nginx
upstream _uwsgi_cuckoo_web {
    server unix:/run/uwsgi/app/cuckoo-web/socket;
}

server {
    listen localhost:8000;

    # Cuckoo Web Interface
    location / {
        client_max_body_size 1G;
        uwsgi_pass  _uwsgi_cuckoo_web;
        include     uwsgi_params;
    }
}

Make sure that nginx can connect to the uWSGI socket by placing its user in the cuckoo group:

$ sudo adduser www-data cuckoo

Enable the server configuration and start the server.

$ sudo ln -s /etc/nginx/sites-available/cuckoo-web /etc/nginx/sites-enabled/
$ sudo service nginx start    # or reload, if already running

At this point, the Web Interface server should be available at port 8000 on the server. Various configurations may be applied to extend this configuration, such as to tune server performance, add authentication, or to secure communications using HTTPS. However, we leave this as an exercise for the user.

REST API¶

As mentioned in Submit an Analysis, Cuckoo provides a simple and lightweight REST API server that is under the hood implemented using Flask.

Starting the API server¶

In order to start the API server you can simply do:

$ cuckoo api

By default it will bind the service on localhost:8090. If you want to change those values, you can use the following syntax:

$ cuckoo api --host 0.0.0.0 --port 1337
$ cuckoo api -H 0.0.0.0 -p 1337

To allow only authenticated access to the API, the api_token in cuckoo.conf must be set to a secret value. In new Cuckoo installations, a random token is automatically generated for you. To access the API, you must send the Authorization: Bearer <token> header with all your requests using the token defined in the configuration. Note that if you want to access the API over an insecure network such as the Internet, you should run the API server behind nginx described in the next section and enable HTTPS.

Web deployment¶

While the default method of starting the API server works fine for many cases, some users may wish to deploy the server in a robust manner. This can be done by exposing the API as a WSGI application through a web server. This section shows a simple example of deploying the API via uWSGI and nginx. These instructions are written with Ubuntu GNU/Linux in mind, but may be adapted for other platforms.

This solution requires uWSGI, the uWSGI Python plugin, and nginx. All are available as packages:

$ sudo apt-get install uwsgi uwsgi-plugin-python nginx

uWSGI setup¶

First, use uWSGI to run the API server as an application.

To begin, create a uWSGI configuration file at /etc/uwsgi/apps-available/cuckoo-api.ini that contains the actual configuration as reported by the cuckoo api --uwsgi command:

$ cuckoo api --uwsgi
[uwsgi]
plugins = python
virtualenv = /home/cuckoo/cuckoo
module = cuckoo.apps.api
callable = app
uid = cuckoo
gid = cuckoo
env = CUCKOO_APP=api
env = CUCKOO_CWD=/home/..somepath..

This configuration inherits a number of settings from the distribution’s default uWSGI configuration and imports cuckoo.apps.api from the Cuckoo package to do the actual work. In this example we installed Cuckoo in a virtualenv located at /home/cuckoo/cuckoo. If Cuckoo is installed globally no virtualenv option is required.

Enable the app configuration and start the server.

$ sudo ln -s /etc/uwsgi/apps-available/cuckoo-api.ini /etc/uwsgi/apps-enabled/
$ sudo service uwsgi start cuckoo-api    # or reload, if already running

Note

Logs for the application may be found in the standard directory for distribution app instances, i.e., /var/log/uwsgi/app/cuckoo-api.log. The UNIX socket is created in a conventional location as well, /run/uwsgi/app/cuckoo-api/socket.

nginx setup¶

With the API server running in uWSGI, nginx can now be set up to run as a web server/reverse proxy, backending HTTP requests to it.

To begin, create a nginx configuration file at /etc/nginx/sites-available/cuckoo-api that contains the actual configuration as reportd by the cuckoo api --nginx command:

$ cuckoo api --nginx
upstream _uwsgi_cuckoo_api {
    server unix:/run/uwsgi/app/cuckoo-api/socket;
}

server {
    listen localhost:8090;

    # REST API app
    location / {
        client_max_body_size 1G;
        uwsgi_pass  _uwsgi_cuckoo_api;
        include     uwsgi_params;
    }
}

Make sure that nginx can connect to the uWSGI socket by placing its user in the cuckoo group:

$ sudo adduser www-data cuckoo

Enable the server configuration and start the server.

$ sudo ln -s /etc/nginx/sites-available/cuckoo-api /etc/nginx/sites-enabled/
$ sudo service nginx start    # or reload, if already running

At this point, the API server should be available at port 8090 on the server. Various configurations may be applied to extend this configuration, such as to tune server performance, add authentication, or to secure communications using HTTPS.

Resources¶

Following is a list of currently available resources and a brief description of each one. For details click on the resource name.

Resource	Description
`POST` /tasks/create/file	Adds a file to the list of pending tasks to be processed and analyzed.
`POST` /tasks/create/url	Adds an URL to the list of pending tasks to be processed and analyzed.
`POST` /tasks/create/submit	Adds one or more files and/or files embedded in archives to the list of pending tasks.
`GET` /tasks/list	Returns the list of tasks stored in the internal Cuckoo database. You can optionally specify a limit of entries to return.
`GET` /tasks/sample	Returns the list of tasks stored in the internal Cuckoo database for a given sample.
`GET` /tasks/view	Returns the details on the task assigned to the specified ID.
`GET` /tasks/reschedule	Reschedule a task assigned to the specified ID.
`GET` /tasks/delete	Removes the given task from the database and deletes the results.
`GET` /tasks/report	Returns the report generated out of the analysis of the task associated with the specified ID. You can optionally specify which report format to return, if none is specified the JSON report will be returned.
`GET` /tasks/summary	Returns a condensed report in JSON format.
`GET` /tasks/screenshots	Retrieves one or all screenshots associated with a given analysis task ID.
`GET` /tasks/rereport	Re-run reporting for task associated with a given analysis task ID.
`GET` /tasks/reboot	Reboot a given analysis task ID.
`GET` /memory/list	Returns a list of memory dump files associated with a given analysis task ID.
`GET` /memory/get	Retrieves one memory dump file associated with a given analysis task ID.
`GET` /files/view	Search the analyzed binaries by MD5 hash, SHA256 hash or internal ID (referenced by the tasks details).
`GET` /files/get	Returns the content of the binary with the specified SHA256 hash.
`GET` /pcap/get	Returns the content of the PCAP associated with the given task.
`GET` /machines/list	Returns the list of analysis machines available to Cuckoo.
`GET` /machines/view	Returns details on the analysis machine associated with the specified name.
`GET` /cuckoo/status	Returns the basic cuckoo status, including version and tasks overview.
`GET` /vpn/status	Returns VPN status.
`GET` /exit	Shuts down the API server.

/tasks/create/file¶

POST /tasks/create/file

Adds a file to the list of pending tasks. Returns the ID of the newly created task.

Example request:

curl -H "Authorization: Bearer S4MPL3" -F file=@/path/to/file http://localhost:8090/tasks/create/file

Example request using Python..

import requests

REST_URL = "http://localhost:8090/tasks/create/file"
SAMPLE_FILE = "/path/to/malwr.exe"
HEADERS = {"Authorization": "Bearer S4MPL3"}

with open(SAMPLE_FILE, "rb") as sample:
    files = {"file": ("temp_file_name", sample)}
    r = requests.post(REST_URL, headers=HEADERS, files=files)

# Add your code to error checking for r.status_code.

task_id = r.json()["task_id"]

# Add your code for error checking if task_id is None.

Example response.

{
    "task_id" : 1
}

Form parameters:

file (required) - sample file (multipart encoded file content)
package (optional) - analysis package to be used for the analysis
timeout (optional) (int) - analysis timeout (in seconds)
priority (optional) (int) - priority to assign to the task (1-3)
options (optional) - options to pass to the analysis package
machine (optional) - label of the analysis machine to use for the analysis
platform (optional) - name of the platform to select the analysis machine from (e.g. “windows”)
tags (optional) - define machine to start by tags. Platform must be set to use that. Tags are comma separated
custom (optional) - custom string to pass over the analysis and the processing/reporting modules
owner (optional) - task owner in case multiple users can submit files to the same cuckoo instance
clock (optional) - set virtual machine clock (format %m-%d-%Y %H:%M:%S)
memory (optional) - enable the creation of a full memory dump of the analysis machine
unique (optional) - only submit samples that have not been analyzed before
enforce_timeout (optional) - enable to enforce the execution for the full timeout value

Status codes:

200 - no error
400 - duplicated file detected (when using unique option)

/tasks/create/url¶

POST /tasks/create/url

Adds a file to the list of pending tasks. Returns the ID of the newly created task.

Example request.

curl -H "Authorization: Bearer S4MPL3" -F url="http://www.malicious.site" http://localhost:8090/tasks/create/url

Example request using Python.

import requests

REST_URL = "http://localhost:8090/tasks/create/url"
SAMPLE_URL = "http://example.org/malwr.exe"
HEADERS = {"Authorization": "Bearer S4MPL3"}

data = {"url": SAMPLE_URL}
r = requests.post(REST_URL, headers=HEADERS, data=data)

# Add your code to error checking for r.status_code.

task_id = r.json()["task_id"]

# Add your code to error checking if task_id is None.

Example response.

{
    "task_id" : 1
}

Form parameters:

url (required) - URL to analyze (multipart encoded content)
package (optional) - analysis package to be used for the analysis
timeout (optional) (int) - analysis timeout (in seconds)
priority (optional) (int) - priority to assign to the task (1-3)
options (optional) - options to pass to the analysis package
machine (optional) - label of the analysis machine to use for the analysis
platform (optional) - name of the platform to select the analysis machine from (e.g. “windows”)
tags (optional) - define machine to start by tags. Platform must be set to use that. Tags are comma separated
custom (optional) - custom string to pass over the analysis and the processing/reporting modules
owner (optional) - task owner in case multiple users can submit files to the same cuckoo instance
memory (optional) - enable the creation of a full memory dump of the analysis machine
enforce_timeout (optional) - enable to enforce the execution for the full timeout value
clock (optional) - set virtual machine clock (format %m-%d-%Y %H:%M:%S)

Status codes:

200 - no error

/tasks/create/submit¶

POST /tasks/create/submit

Adds one or more files and/or files embedded in archives or a newline separated list of URLs/hashes to the list of pending tasks. Returns the submit ID as well as the task IDs of the newly created task(s).

Example request.

# Submit two executables.
curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/create/submit -F files=@1.exe -F files=@2.exe

# Submit http://google.com
curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/create/submit -F strings=google.com

# Submit http://google.com & http://facebook.com
curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/create/submit -F strings=$'google.com\nfacebook.com'

Example request using Python.

import requests

HEADERS = {"Authorization": "Bearer S4MPL3"}

# Submit one or more files.
r = requests.post("http://localhost:8090/tasks/create/submit", files=[
    ("files", open("1.exe", "rb")),
    ("files", open("2.exe", "rb")),
], headers=HEADERS)

# Add your code to error checking for r.status_code.

submit_id = r.json()["submit_id"]
task_ids = r.json()["task_ids"]
errors = r.json()["errors"]

# Add your code to error checking on "errors".

# Submit one or more URLs or hashes.
urls = [
    "google.com", "facebook.com", "cuckoosandbox.org",
]
r = requests.post(
    "http://localhost:8090/tasks/create/submit",
    headers=HEADERS,
    data={"strings": "\n".join(urls)}
)

Example response from the executable submission.

{
    "submit_id": 1,
    "task_ids": [1, 2],
    "errors": []
}

Form parameters:

file (optional) - backwards compatibility with naming scheme for /tasks/create/file
files (optional) - sample(s) to inspect and add to our pending queue
strings (optional) - newline separated list of URLs and/or hashes (to be obtained using your VirusTotal API key)
timeout (optional) (int) - analysis timeout (in seconds)
priority (optional) (int) - priority to assign to the task (1-3)
options (optional) - options to pass to the analysis package
tags (optional) - define machine to start by tags. Platform must be set to use that. Tags are comma separated
custom (optional) - custom string to pass over the analysis and the processing/reporting modules
owner (optional) - task owner in case multiple users can submit files to the same cuckoo instance
memory (optional) - enable the creation of a full memory dump of the analysis machine
enforce_timeout (optional) - enable to enforce the execution for the full timeout value
clock (optional) - set virtual machine clock (format %m-%d-%Y %H:%M:%S)

Status codes:

200 - no error

/tasks/list¶

GET /tasks/list/ (int: limit) / (int: offset)

Returns list of tasks.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/list

Example response.

{
    "tasks": [
        {
            "category": "url",
            "machine": null,
            "errors": [],
            "target": "http://www.malicious.site",
            "package": null,
            "sample_id": null,
            "guest": {},
            "custom": null,
            "owner": "",
            "priority": 1,
            "platform": null,
            "options": null,
            "status": "pending",
            "enforce_timeout": false,
            "timeout": 0,
            "memory": false,
            "tags": []
            "id": 1,
            "added_on": "2012-12-19 14:18:25",
            "completed_on": null
        },
        {
            "category": "file",
            "machine": null,
            "errors": [],
            "target": "/tmp/malware.exe",
            "package": null,
            "sample_id": 1,
            "guest": {},
            "custom": null,
            "owner": "",
            "priority": 1,
            "platform": null,
            "options": null,
            "status": "pending",
            "enforce_timeout": false,
            "timeout": 0,
            "memory": false,
            "tags": [
                        "32bit",
                        "acrobat_6",
                    ],
            "id": 2,
            "added_on": "2012-12-19 14:18:25",
            "completed_on": null
        }
    ]
}

Parameters:

limit (optional) (int) - maximum number of returned tasks
offset (optional) (int) - data offset

Status codes:

200 - no error

/tasks/sample¶

GET /tasks/sample/ (int: sample_id)

Returns list of tasks for sample.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/sample/1

Example response.

{
    "tasks": [
        {
            "category": "file",
            "machine": null,
            "errors": [],
            "target": "/tmp/malware.exe",
            "package": null,
            "sample_id": 1,
            "guest": {},
            "custom": null,
            "owner": "",
            "priority": 1,
            "platform": null,
            "options": null,
            "status": "pending",
            "enforce_timeout": false,
            "timeout": 0,
            "memory": false,
            "tags": [
                        "32bit",
                        "acrobat_6",
                    ],
            "id": 2,
            "added_on": "2012-12-19 14:18:25",
            "completed_on": null
        }
    ]
}

Parameters:

sample_id (required) (int) - sample id to list tasks for

Status codes:

200 - no error

/tasks/view¶

GET /tasks/view/ (int: id)

Returns details on the task associated with the specified ID.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/view/1

Example response.

{
    "task": {
        "category": "url",
        "machine": null,
        "errors": [],
        "target": "http://www.malicious.site",
        "package": null,
        "sample_id": null,
        "guest": {},
        "custom": null,
        "owner": "",
        "priority": 1,
        "platform": null,
        "options": null,
        "status": "pending",
        "enforce_timeout": false,
        "timeout": 0,
        "memory": false,
        "tags": [
                    "32bit",
                    "acrobat_6",
                ],
        "id": 1,
        "added_on": "2012-12-19 14:18:25",
        "completed_on": null
    }
}

Note: possible value for key status:

pending
running
completed
reported

Parameters:

id (required) (int) - ID of the task to lookup

Status codes:

200 - no error
404 - task not found

/tasks/reschedule¶

GET /tasks/reschedule/ (int: id) / (int: priority)

Reschedule a task with the specified ID and priority (default priority is 1).

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/reschedule/1

Example response.

{
    "status": "OK"
}

Parameters:

id (required) (int) - ID of the task to reschedule
priority (optional) (int) - Task priority

Status codes:

200 - no error
404 - task not found

/tasks/delete¶

GET /tasks/delete/ (int: id)

Removes the given task from the database and deletes the results.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/delete/1

Parameters:

id (required) (int) - ID of the task to delete

Status codes:

200 - no error
404 - task not found
500 - unable to delete the task

/tasks/report¶

GET /tasks/report/ (int: id) / (str: format)

Returns the report associated with the specified task ID.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/report/1

Parameters:

id (required) (int) - ID of the task to get the report for
format (optional) - format of the report to retrieve [json/html/all/dropped/package_files]. If none is specified the JSON report will be returned. all returns all the result files as tar.bz2, dropped the dropped files as tar.bz2, package_files files uploaded to host by analysis packages.

Status codes:

200 - no error
400 - invalid report format
404 - report not found

/tasks/summary¶

GET /tasks/summary/ (int: id)

Returns a condensed report associated with the specified task ID in JSON format.

Example request.

curl http://localhost:8090/tasks/summary/1

Parameters:

id (required) (int) - ID of the task to get the report for

Status codes:

200 - no error
404 - report not found

/tasks/screenshots¶

GET /tasks/screenshots/ (int: id) / (str: number)

Returns one or all screenshots associated with the specified task ID.

Example request.

wget http://localhost:8090/tasks/screenshots/1

Parameters:

id (required) (int) - ID of the task to get the report for
screenshot (optional) - numerical identifier of a single screenshot (e.g. 0001, 0002)

Status codes:

404 - file or folder not found

/tasks/rereport¶

GET /tasks/rereport/ (int: id)

Re-run reporting for task associated with the specified task ID.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/rereport/1

Example response.

{
    "success": true
}

Parameters:

id (required) (int) - ID of the task to re-run report

Status codes:

200 - no error
404 - task not found

/tasks/reboot¶

GET /tasks/reboot/ (int: id) **

Add a reboot task to database from an existing analysis ID.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/tasks/reboot/1

Example response.

{
    "task_id": 1,
    "reboot_id": 3
}

Parameters:

id (required) (int) - ID of the task

Status codes:

200 - success
404 - error creating reboot task

/memory/list¶

GET /memory/list/ (int: id)

Returns a list of memory dump files or one memory dump file associated with the specified task ID.

Example request.

wget http://localhost:8090/memory/list/1

Parameters:

id (required) (int) - ID of the task to get the report for

Status codes:

404 - file or folder not found

/memory/get¶

GET /memory/get/ (int: id) / (str: number)

Returns one memory dump file associated with the specified task ID.

Example request.

wget http://localhost:8090/memory/get/1/1908

Parameters:

id (required) (int) - ID of the task to get the report for
pid (required) - numerical identifier (pid) of a single memory dump file (e.g. 205, 1908)

Status codes:

404 - file or folder not found

/files/view¶

GET /files/view/md5/ (str: md5)

GET /files/view/sha256/ (str: sha256)

GET /files/view/id/ (int: id)

Returns details on the file matching either the specified MD5 hash, SHA256 hash or ID.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/files/view/id/1

Example response.

{
    "sample": {
        "sha1": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
        "file_type": "empty",
        "file_size": 0,
        "crc32": "00000000",
        "ssdeep": "3::",
        "sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
        "sha512": "cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36ce9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e",
        "id": 1,
        "md5": "d41d8cd98f00b204e9800998ecf8427e"
    }
}

Parameters:

md5 (optional) - MD5 hash of the file to lookup
sha256 (optional) - SHA256 hash of the file to lookup
id (optional) (int) - ID of the file to lookup

Status codes:

200 - no error
400 - invalid lookup term
404 - file not found

/files/get¶

GET /files/get/ (str: sha256)

Returns the binary content of the file matching the specified SHA256 hash.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/files/get/e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 > sample.exe

Status codes:

200 - no error
404 - file not found

/pcap/get¶

GET /pcap/get/ (int: task)

Returns the content of the PCAP associated with the given task.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/pcap/get/1 > dump.pcap

Status codes:

200 - no error
404 - file not found

/machines/list¶

GET /machines/list

Returns a list with details on the analysis machines available to Cuckoo.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/machines/list

Example response.

{
    "machines": [
        {
            "status": null,
            "locked": false,
            "name": "cuckoo1",
            "resultserver_ip": "192.168.56.1",
            "ip": "192.168.56.101",
            "tags": [
                        "32bit",
                        "acrobat_6",
                    ],
            "label": "cuckoo1",
            "locked_changed_on": null,
            "platform": "windows",
            "snapshot": null,
            "interface": null,
            "status_changed_on": null,
            "id": 1,
            "resultserver_port": "2042"
        }
    ]
}

Status codes:

200 - no error

/machines/view¶

GET /machines/view/ (str: name)

Returns details on the analysis machine associated with the given name.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/machines/view/cuckoo1

Example response.

{
    "machine": {
        "status": null,
        "locked": false,
        "name": "cuckoo1",
        "resultserver_ip": "192.168.56.1",
        "ip": "192.168.56.101",
        "tags": [
                    "32bit",
                    "acrobat_6",
                ],
        "label": "cuckoo1",
        "locked_changed_on": null,
        "platform": "windows",
        "snapshot": null,
        "interface": null,
        "status_changed_on": null,
        "id": 1,
        "resultserver_port": "2042"
    }
}

Status codes:

200 - no error
404 - machine not found

/cuckoo/status¶

GET /cuckoo/status/

Returns status of the cuckoo server. In version 1.3 the diskspace entry was added. The diskspace entry shows the used, free, and total diskspace at the disk where the respective directories can be found. The diskspace entry allows monitoring of a Cuckoo node through the Cuckoo API. Note that each directory is checked separately as one may create a symlink for $CUCKOO/storage/analyses to a separate harddisk, but keep $CUCKOO/storage/binaries as-is. (This feature is only available under Unix!)

In version 1.3 the cpuload entry was also added - the cpuload entry shows the CPU load for the past minute, the past 5 minutes, and the past 15 minutes, respectively. (This feature is only available under Unix!)

Diskspace directories:

analyses - $CUCKOO/storage/analyses/
binaries - $CUCKOO/storage/binaries/
temporary - tmppath as specified in conf/cuckoo.conf

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/cuckoo/status

Example response.

{
    "tasks": {
        "reported": 165,
        "running": 2,
        "total": 167,
        "completed": 0,
        "pending": 0
    },
    "diskspace": {
        "analyses": {
            "total": 491271233536,
            "free": 71403470848,
            "used": 419867762688
        },
        "binaries": {
            "total": 491271233536,
            "free": 71403470848,
            "used": 419867762688
        },
        "temporary": {
            "total": 491271233536,
            "free": 71403470848,
            "used": 419867762688
        }
    },
    "version": "1.0",
    "protocol_version": 1,
    "hostname": "Patient0",
    "machines": {
        "available": 4,
        "total": 5
    }
}

Status codes:

200 - no error
404 - machine not found

/vpn/status¶

GET /vpn/status

Returns VPN status.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/vpn/status

Status codes:

200 - show status
500 - not available

/exit¶

GET /exit

Shuts down the server if in debug mode and using the werkzeug server.

Example request.

curl -H "Authorization: Bearer S4MPL3" http://localhost:8090/exit

Status codes:

200 - success
403 - this call can only be used in debug mode
500 - error

Distributed Cuckoo¶

As mentioned in Submit an Analysis, Cuckoo provides a REST API for Distributed Cuckoo usage. Distributed Cuckoo allows one to setup a single REST API point to which samples and URLs can be submitted which will then, in turn, be submitted to one of the configured Cuckoo nodes.

A typical setup thus includes a machine on which Distributed Cuckoo is run and one or more machines running an instance of the Cuckoo daemon and the Cuckoo REST API.

A few notes:

Using Distributed Cuckoo only makes sense when running at least two cuckoo nodes.
Distributed Cuckoo can be run on a machine that also runs a Cuckoo daemon and REST API, however, make sure it has enough disk space if the intention is to submit a lot of samples.

Starting the Distributed REST API¶

The Distributed REST API has the following command line options:

$ cuckoo distributed server --help
Usage: cuckoo distributed server [OPTIONS]

Options:
  -H, --host TEXT     Host to bind the Distributed Cuckoo server on
  -p, --port INTEGER  Port to bind the Distributed Cuckoo server on
  --uwsgi             Dump uWSGI configuration
  --nginx             Dump nginx configuration
  --help              Show this message and exit.

As may be derived from the help output, starting Distributed Cuckoo may be as simple as running cuckoo distributed server.

The various configuration options are described in the configuration file, but following we have more in-depth descriptions as well. More advanced usage naturally includes deployment using uWSGI and nginx.

Distributed Cuckoo Configuration¶

Report Formats¶

The reporting formats denote which reports you’d like to retrieve later on. Note that all task-related data will be removed from the Cuckoo nodes once the related reports have been fetched so that the machines are not running out of disk space. This does, however, force you to specify all the report formats that you’re interested in, because otherwise that information will be lost.

Reporting formats include, but are not limited to and may also include your own reporting formats, report.json, report.html, etc.

Samples Directory¶

The samples directory denotes the directory where the submitted samples will be stored temporarily, until the associated task has been deleted.

Reports Directory¶

Much like the Samples Directory the Reports Directory defines the directory where reports will be stored until they’re fetched and deleted from the Distributed REST API.

RESTful resources¶

Following are all RESTful resources. Also make sure to check out the Quick usage section which documents the most commonly used commands.

Resource	Description
`GET` GET /api/node	Get a list of all enabled Cuckoo nodes.
`POST` POST /api/node	Register a new Cuckoo node.
`GET` GET /api/node/<name>	Get basic information about a node.
`PUT` PUT /api/node/<name>	Update basic information of a node.
`POST` POST /api/node/<name>/refresh	Refresh a Cuckoo nodes metadata.
`DELETE` DELETE /api/node/<name>	Disable (not completely remove!) a node.
`GET` GET /api/task	Get a list of all (or a part) of the tasks in the database.
`POST` POST /api/task	Create a new analysis task.
`GET` GET /api/task/<id>	Get basic information about a task.
`DELETE` DELETE /api/task/<id>	Delete all associated information of a task.
`GET` GET /api/report/<id>/<format>	Fetch an analysis report.
`GET` GET /api/pcap/<id>	Fetches the PCAP of an analysis.

GET /api/node¶

Returns all enabled nodes. For each node the information includes the associated name, its API URL, and machines:

$ curl http://localhost:9003/api/node
{
    "success": true,
    "nodes": {
        "localhost": {
            "machines": [
                {
                    "name": "cuckoo1",
                    "platform": "windows",
                    "tags": []
                }
            ],
            "name": "localhost",
            "url": "http://localhost:8090/"
        }
    }
}

POST /api/node¶

Register a new Cuckoo node by providing the name and the URL:

$ curl http://localhost:9003/api/node -F name=localhost \
    -F url=http://localhost:8090/
{
    "success": true
}

GET /api/node/<name>¶

Get basic information about a particular Cuckoo node:

$ curl http://localhost:9003/api/node/localhost
{
    "success": true,
    "nodes": [
        {
            "name": "localhost",
            "url": "http://localhost:8090/"
            "machines": [
                {
                    "name": "cuckoo1",
                    "platform": "windows",
                    "tags": []
                }
            ]
        }
    ]
}

PUT /api/node/<name>¶

Update basic information of a Cuckoo node:

$ curl -XPUT http://localhost:9003/api/node/localhost -F name=newhost \
    -F url=http://1.2.3.4:8090/
{
    "success": true
}

POST /api/node/<name>/refresh¶

Refreshes metadata associated by a Cuckoo node, in particular, its machines:

$ curl -XPOST http://localhost:9003/api/node/localhost/refresh
{
    "success": true,
    "machines": [
        {
            "name": "cuckoo1",
            "platform": "windows",
            "tags": []
        },
        {
            "name": "cuckoo2",
            "platform": "windows",
            "tags": []
        }
    ]
}

DELETE /api/node/<name>¶

Disable a Cuckoo node, therefore not having it process any new tasks, but keeping its history in the Distributed Cuckoo database:

$ curl -XDELETE http://localhost:9003/api/node/localhost
{
    "success": true
}

GET /api/task¶

Get a list of all tasks in the database. In order to limit the amount of results, there’s an offset, limit, finished, and owner field available:

$ curl http://localhost:9003/api/task?limit=1
{
    "success": true,
    "tasks": {
        "1": {
            "clock": null,
            "custom": null,
            "owner": "",
            "enforce_timeout": null,
            "machine": null,
            "memory": null,
            "options": null,
            "package": null,
            "path": "/tmp/dist-samples/tmphal8mS",
            "platform": "windows",
            "priority": 1,
            "tags": null,
            "task_id": 1,
            "timeout": null
        }
    }
}

POST /api/task¶

Submit a new file or URL to be analyzed:

$ curl http://localhost:9003/api/task -F file=@sample.exe
{
    "success": true,
    "task_id": 2
}

GET /api/task/<id>¶

Get basic information about a particular task:

$ curl http://localhost:9003/api/task/2
{
    "success": true,
    "tasks": {
        "2": {
            "id": 2,
            "clock": null,
            "custom": null,
            "owner": "",
            "enforce_timeout": null,
            "machine": null,
            "memory": null,
            "options": null,
            "package": null,
            "path": "/tmp/tmpPwUeXm",
            "platform": "windows",
            "priority": 1,
            "tags": null,
            "timeout": null,
            "task_id": 1,
            "node_id": 2,
            "finished": false
        }
    }
}

DELETE /api/task/<id>¶

Delete all associated data of a task, namely the binary, the PCAP, and the reports:

$ curl -XDELETE http://localhost:9003/api/task/2
{
    "success": true
}

GET /api/report/<id>/<format>¶

Fetch a report for the given task in the specified format:

# Defaults to the JSON report.
$ curl http://localhost:9003/api/report/2
...

GET /api/pcap/<id>¶

Fetches the PCAP for the given task:

$ curl http://localhost:9003/api/pcap/2
...

Proposed setup¶

The following description depicts a Distributed Cuckoo setup with two Cuckoo machines, cuckoo0 and cuckoo1. In this setup the first machine, cuckoo0, also hosts the Distributed Cuckoo REST API.

Configuration settings¶

Our setup will require a couple of updates with regards to the configuration files.

conf/cuckoo.conf¶

Update process_results to off as we will be running our own results processing script (for performance reasons).

Update tmppath to something that holds enough storage to store a few hundred binaries. On some servers or setups /tmp may have a limited amount of space and thus this wouldn’t suffice.

Update connection to use something not sqlite3. Preferably PostgreSQL or MySQL. SQLite3 doesn’t support multi-threaded applications and as such is not a good choice for systems such as Cuckoo (as-is).

You should create a database specifically for the distributed cuckoo setup. Do not be tempted to use any existing cuckoo database in order to avoid update problems with the DB scripts. In the configuration use the new database name. The remaining configuration such as usernames, servers, etc can be the same as for your cuckoo install. Don’t forget to use one DB per node and one for the machine running Distributed Cuckoo (the “management machine” or “controller”).

conf/processing.conf¶

You may want to disable some processing modules, such as virustotal.

conf/reporting.conf¶

Depending on which report(s) are required for integration with your system it might make sense to only make those report(s) that you’re going to use. Thus disabling the other ones.

conf/virtualbox.conf¶

Assuming VirtualBox is the Virtual Machine manager of choice, the mode will have to be changed to headless or you will have some restless nights (this is the default nowadays).

Setup Cuckoo¶

On each machine you will have to run the Cuckoo Daemon, the Cuckoo API, and one or more Cuckoo Process instances. For more information on setting that up, please refer to Starting Cuckoo.

Setup Distributed Cuckoo¶

On the Distributed Cuckoo machine you’ll have to setup the Distributed Cuckoo REST API and the Distributed Cuckoo Worker.

As stated earlier, Distributed Cuckoo REST API may be started by running cuckoo distributed server or by deploying it properly with uWSGI and nginx.

The Distributed Cuckoo Worker may be started by running supervisorctl start distributed in the CWD (make sure to start supervisord first as per Cuckoo in the background). This will automatically start the Worker with the correct configuration and arguments, etc.

Register Cuckoo nodes¶

As outlined in Quick usage the Cuckoo nodes have to be registered with the Distributed Cuckoo REST API:

$ curl http://localhost:9003/api/node -F name=cuckoo0 -F url=http://localhost:8090/
$ curl http://localhost:9003/api/node -F name=cuckoo1 -F url=http://1.2.3.4:8090/

Having registered the Cuckoo nodes all that’s left to do now is to submit tasks and fetch reports once finished. Documentation on these commands can be found in the Quick usage section. In case your Cuckoo node is not on localhost, replace localhost with the IP address of the node where the Cuckoo REST API is running.

If you want to experiment with load balancing between the nodes you may want to try using a lower value for the threshold parameter in the $CWD/distributed/settings.py file as the default value is 500 (meaning tasks are assigned to Cuckoo nodes in batches of 500).

Quick usage¶

For practical usage the following few commands will be most interesting.

Register a Cuckoo node, in this case a Cuckoo API running on the same machine in this case:

$ curl http://localhost:9003/api/node -F name=localhost -F ip=127.0.0.1

Disable a Cuckoo node:

$ curl -XDELETE http://localhost:9003/api/node/localhost

Submit a new analysis task without any special requirements (e.g., using Cuckoo tags, a particular machine, etc):

$ curl http://localhost:9003/api/task -F file=@/path/to/sample.exe

Get the report of a task has been finished (if it hasn’t finished you’ll get an error with code 420). Following example will default to the JSON report:

$ curl http://localhost:9003/api/report/1

If a Cuckoo node gets stuck and needs a reset, the following steps could be performed to restart it cleanly. Note that this requires usage of our SaltStack configuration and some manual SQL commands (and preferably the Distributed Cuckoo Worker is temporary disabled, i.e., supervisorctl stop distributed):

$ psql -c "UPDATE task SET status = 'pending' WHERE status = 'processing' AND node_id = 123"
$ salt cuckoo1 state.apply cuckoo.clean
$ salt cuckoo1 state.apply cuckoo.start

If the entire Cuckoo cluster was somehow locked up, i.e., all tasks have been ‘assigned’, are ‘processing’, or have the ‘finished’ status while none of the Cuckoo nodes are currently working on said analyses (e.g., due to numerous resets etc), then the following steps may be used to reset the entire state:

$ supervisorctl -c ~/.cuckoo/supervisord.conf stop distributed
$ salt '*' state.apply cuckoo.stop
$ salt '*' state.apply cuckoo.clean
$ psql -c "UPDATE task SET status = 'pending', node_id = null WHERE status IN ('assigned', 'processing', 'finished')"
$ salt '*' state.apply cuckoo.start
$ supervisorctl -c ~/.cuckoo/supervisord.conf start distributed

If a Cuckoo node has a number of tasks that failed to process, therefore locking up the Cuckoo node altogether, then upgrading the Cuckoo instances with a bugfixed version and re-processing all analyses may do the trick:

$ salt cuckoo1 state.apply cuckoo.update  # Upgrade Cuckoo.
# To make sure there are failed analyses in the first place.
$ salt cuckoo1 cmd.run "sudo -u cuckoo psql -c \"SELECT * FROM tasks WHERE status = 'failed_processing'\"
# Reset each analyses to be re-processed.
$ salt cuckoo1 cmd.run "sudo -u cuckoo psql -c \"UPDATE tasks SET status = 'completed', processing = null WHERE status = 'failed_processing'\""

In order to upgrade the Distributed Cuckoo master, one may want to perform the following steps:

$ /etc/init.d/uwsgi stop
$ supervisorctl -c ~/.cuckoo/supervisord.conf stop distributed
$ pip uninstall -y cuckoo
$ pip install cuckoo==2.0.0         # Specify your version here.
$ pip install Cuckoo-2.0.0.tar.gz   # Or use a locally archived build.
$ cuckoo distributed migrate
$ supervisorctl -c ~/.cuckoo/supervisord.conf start distributed
$ /etc/init.d/uwsgi start
$ /etc/init.d/nginx restart

In order to test your entire Cuckoo cluster, i.e., every machine on every Cuckoo node, one may take the stuff/distributed/cluster-test.py script as an example. As-is it allows one to check for an active internet connection in each and every configured machine in the cluster. This script may be used to identify machines that are incorrect or have been corrupted in one way or another. Example usage may look as follows:

# Assuming Distributed Cuckoo listens on localhost and that you want to
# run the 'internet' script (see also the source of cluster-test.py).
$ python stuff/distributed/cluster-test.py localhost -s internet

Utilities¶

Cuckoo comes with a set of pre-built utilities to automate several common tasks. Before these utilities could be found in the utils/ directory but since then we have moved to Cuckoo Apps.

Cuckoo Apps¶

A Cuckoo App is essentially just a Cuckoo sub-command. There exist a couple of Cuckoo Apps, each with their own functionality. It is important to note that each Cuckoo App can be invoked in the same way. Following are some examples:

$ cuckoo submit --help
$ cuckoo api --help
$ cuckoo clean --help

In these examples we provided the --help parameter which shows the functionality and all available parameters for the particular Cuckoo App.

Submission Utility¶

Submits samples to analysis. This tool is described in Submit an Analysis.

Web Utility¶

Cuckoo’s web interface. This tool is described in Web interface.

Processing Utility¶

Changed in version 2.0.0: We used to have longstanding issues with ./utils/process.py randomly freezing up and ./utils/process2.py only being able to handle PostgreSQL-based databases. These two commands have now been merged into one Cuckoo App and no longer show signs of said issues or limitations.

For bigger Cuckoo setups it is recommended to separate the results processing from the Cuckoo analyses due to performance issues (with multiple threads & the Python GIL). Using cuckoo process it is also possible to re-generate Cuckoo reports, this is mostly used while developing and debugging Cuckoo Processing modules, Cuckoo Signatures, and Cuckoo Reporting modules.

In order to do results processing in one or more separate process(es) one has to disable the process_results configuration item in $CWD/conf/cuckoo.conf by setting the value to off. Then a Cuckoo Processing instance has to be started, this can be done as follows:

$ cuckoo process instance1

If one Cuckoo Processing instance is not enough to handle all the incoming analyses, simply create a second, third, and possibly more instances:

$ cuckoo process instance2

In order to re-generate a Cuckoo report of an analysis task, use the -r switch:

$ cuckoo process -r 1

It is also possible to re-generate multiple or a range of Cuckoo reports at once. The following will reprocess tasks 1, 2, 5, 6, 7, 8, 9, 10:

$ cuckoo process -r 1,2,5-10

For more information see also the help on this Cuckoo App:

$ cuckoo process --help
Usage: cuckoo process [OPTIONS] [INSTANCE]

  Process raw task data into reports.

Options:
  -r, --report TEXT       Re-generate one or more reports
  -m, --maxcount INTEGER  Maximum number of analyses to process
  --help                  Show this message and exit.

In automated mode an instance name is required (e.g., instance1) as seen in the examples earlier above!

Community Download Utility¶

This Cuckoo App downloads Cuckoo Signatures, the latest monitoring binaries, and other goodies from the Cuckoo Community Repository and installs them in your CWD.

To get all the latest and greatest from the Cuckoo Community simply execute as follows and wait until it finishes - it currently doesn’t have any progress indication:

$ cuckoo community

For more usage see as follows:

$ cuckoo community --help
Usage: cuckoo community [OPTIONS]

  Utility to fetch supplies from the Cuckoo Community.

Options:
  -f, --force              Overwrite existing files
  -b, --branch TEXT        Specify a different community branch rather than
                           master
  --file, --filepath PATH  Specify a local copy of a community .tar.gz file
  --help                   Show this message and exit.

Database migration utility¶

Changed in version 2.0.0: This used to be a special process, but has since been integrated properly as a Cuckoo App.

This utility helps migrating your data between Cuckoo releases. It’s developed on top of the Alembic framework and it should provide data migration for both SQL database and Mongo database. This tool is already described in Upgrading from a previous release.

Stats utility¶

Deprecated since version 2.0-rc2: This utility will not be ported to a Cuckoo App as this information can also be retrieved through both the Cuckoo API as well as the Cuckoo Web Interface.

Machine utility¶

Changed in version 2.0.0: This used to be a standalone and hacky script directly modifying the Cuckoo configuration. It’s now much better integrated and will be able to somewhat properly interact with Cuckoo.

The machine Cuckoo App is designed to help you automatize the configuration of virtual machines in Cuckoo. It takes a list of machine details as arguments and write them in the specified configuration file of the machinery module enabled in cuckoo.conf. Following are the available options:

$ cuckoo machine --help
Usage: cuckoo machine [OPTIONS] VMNAME [IP]

Options:
  --debug              Enable verbose logging
  --add                Add a Virtual Machine
  --delete             Delete a Virtual Machine
  --platform TEXT      Guest Operating System
  --options TEXT       Machine options
  --tags TEXT          Tags for this Virtual Machine
  --interface TEXT     Sniffer interface for this Virtual Machine
  --snapshot TEXT      Specific Virtual Machine Snapshot to use
  --resultserver TEXT  IP:Port of the Result Server
  --help               Show this message and exit.

As an example, a machine may be added to Cuckoo’s configuration as follows:

$ cuckoo machine --add cuckoo1 192.168.56.101 --platform windows --snapshot vmcloak

Distributed scripts¶

This tool is described in Distributed Cuckoo.

Mac OS X Bootstrap scripts¶

Deprecated since version 2.0.0: These files will be moved elsewhere in an upcoming update and so should any documentation that references these scripts.

A couple of bootstrap scripts used for Mac OS X analysis are located in utils/darwin folder, they are used to bootstrap the guest and host system for Mac OS X malware analysis. Some settings are defined as constants inside them, so it is suggested to have a look at them and configure them for your needs.

SMTP Sinkhole¶

Deprecated since version 2.0.0: This script has been removed since this functionality should be implemented properly using a Postfix setup.

Setup script¶

Deprecated since version 2.0.0: This script has been replaced by a similar but much more powerful SaltStack state.

Cuckoo Rooter¶

The Cuckoo Rooter is a new concept, providing root access for various commands to Cuckoo (which itself generally speaking runs as non-root). This command is currently only available for Ubuntu and Debian-like systems.

In particular, the rooter helps Cuckoo out with running network-related commands in order to provide per-analysis routing options. For more information on that, please refer to the Per-Analysis Network Routing document. Cuckoo and the rooter communicate through a UNIX socket for which the rooter makes sure that Cuckoo can reach it.

Its usage is as follows:

$ cuckoo rooter --help
Usage: cuckoo rooter [OPTIONS] [SOCKET]

Options:
  -g, --group TEXT  Unix socket group
  --service PATH    Path to service(8) for invoking OpenVPN
  --iptables PATH   Path to iptables(8)
  --ip PATH         Path to ip(8)
  --sudo            Request superuser privileges
  --help            Show this message and exit.

By default the rooter will default to chown’ing the cuckoo user as user and group for the UNIX socket, as recommended when Installing Cuckoo. If you’re running Cuckoo under a user other than cuckoo, you will have to specify this to the rooter as follows:

$ sudo cuckoo rooter -g <user>

The other options are fairly straightforward - you can specify the paths to specific Linux commands. By default one shouldn’t have to do this though, as the rooter takes the default paths for the various utilities as per a default setup.

Virtualenv¶

Due to the fact that the rooter must be run as root user, there are some slight complications when using a virtualenv to run Cuckoo. More specifically, when running sudo cuckoo rooter, the $VIRTUAL_ENV environment variable will not be passed along, due to which Python will not be executed from the same virtualenv as it would have been normally.

To resolve this one simply has to execute the cuckoo binary from the virtualenv session directly. E.g., if your virtualenv is located at ~/venv, then running the rooter command could be done as follows:

$ sudo ~/venv/bin/cuckoo rooter

Alternatively one may use the --sudo flag which will call sudo on the correct cuckoo binary with all the provided flags. In turn the user will have to enter his or her password and, assuming all is fine, the Cuckoo Rooter will be started properly, e.g.:

(venv)$ cuckoo rooter --sudo

Cuckoo Rooter Usage¶

Using the Cuckoo Rooter is actually pretty easy. If you know how to start it, you’re basically good to go. Even though Cuckoo talks with the Cuckoo Rooter for each analysis with a routing option other than None Routing, the Cuckoo Rooter does not keep any state or attach to any Cuckoo instance in particular.

It is therefore that once the Cuckoo Rooter has been started you may leave it be - the Cuckoo Rooter will take care of itself from that point onwards, no matter how often you restart your Cuckoo instance.

Cuckoo Feedback¶

New in version 2.0.0.

The Cuckoo Feedback form allows users to provide instant feedback to the Cuckoo Core Developer team. By doing so, our development team will be able to more quickly react upon errors, partially incorrect analysis results, errors occurred during an analysis or in the web interface, and anything else that our users think requires some extra attention. All in all, this optional feature gives those users that are interested in a second opinion the ability to do so in a convenient way for both the user as well as the team behind Cuckoo Sandbox.

Note

As a user you are able to ping back to us through the Cuckoo Feedback from embedded in most pages of the web interface (e.g., an analysis page or a 404 page not found / 500 internal error page).

Following a screenshot of a part of the new (as of Cuckoo 2.0.0) analysis results page with the side bar locked in (i.e., permanently open).

At the bottom of the side bar you’ll see the Feedback button which will pop up the following feedback form. Naturally filling out all of the fields in this form will allow you to send us feedback (in a secure manner).

It should be noted that, may you decide to provide feedback on a regular, you can also fill out your name, company, and email address (where you’ll receive any answers) in the $CWD/conf/cuckoo.conf configuration file so those will be auto-filled for you upon opening the feedback form.

Analysis Packages¶

The analysis packages are a core component of Cuckoo Sandbox. They consist in structured Python classes which, when executed in the guest machines, describe how Cuckoo’s analyzer component should conduct the analysis.

Cuckoo provides some default analysis packages that you can use, but you are able to create your own or modify the existing ones. You can find them at analyzer/windows/modules/packages/.

As described in Submit an Analysis, you can specify some options to the analysis packages in the form of key1=value1,key2=value2. The existing analysis packages already include some default options that can be enabled.

Following is a list of the options that work for all analysis packages unless explicitly stated otherwise:

free [yes/no]: if enabled, no behavioral logs will be produced and the malware will be executed freely.
procmemdump [yes/no]: if enabled, take memory dumps of all actively monitored processes.
human 0: if disabled, human-like interaction (i.e., mouse movements) will not be enabled

Following is the list of existing packages in alphabetical order:

applet: used to analyze Java applets.
Options:
- class: specify the name of the class to be executed. This option is mandatory for a correct execution.
bin: used to analyze generic binary data, such as shellcodes.
cpl: used to analyze Control Panel Applets.
dll: used to run and analyze Dynamically Linked Libraries.
Options:
- function: specify the function to be executed. If none is specified, Cuckoo will try to run DllMain.
- arguments: specify arguments to pass to the DLL through commandline.
- loader: specify a process name to use to fake the DLL launcher name instead of rundll32.exe (this is used to fool possible anti-sandboxing tricks of certain malware)
doc: used to run and analyze Microsoft Word documents.
exe: default analysis package used to analyze generic Windows executables.
Options:
- arguments: specify any command line argument to pass to the initial process of the submitted malware.
generic: used to run and analyze generic samples via cmd.exe.
ie: used to analyze Internet Explorer’s behavior when opening the given URL or HTML file.
jar: used to analyze Java JAR containers.
Options:
- class: specify the path of the class to be executed. If none is specified, Cuckoo will try to execute the main function specified in the Jar’s MANIFEST file.
js: used to run and analyze Javascript files (e.g., those found in attachments of emails).
hta: used to run and analyze HTML Application files.
msi: used to run and analyze MSI windows installer.
pdf: used to run and analyze PDF documents.
ppt: used to run and analyze Microsoft PowerPoint documents.
ps1: used to run and analyze PowerShell scripts.
python: used to run and analyze Python scripts.
vbs: used to run and analyze VBScript files.
wsf: used to run and analyze Windows Script Host files.
xls: used to run and analyze Microsoft Excel documents.
zip: used to run and analyze Zip archives.
Options:
- file: specify the name of the file contained in the archive to execute. If none is specified, Cuckoo will try to execute sample.exe.
- arguments: specify any command line argument to pass to the initial process of the submitted malware.
- password: specify the password of the archive. If none is specified, Cuckoo will try to extract the archive without password or use the password “infected”.

You can find more details on how to start creating new analysis packages in the Analysis Packages customization chapter.

As you already know, you can select which analysis package to use by specifying its name at submission time (see Submit an Analysis) as follows:

$ cuckoo submit --package <package name> /path/to/malware

If none is specified, Cuckoo will try to detect the file type and select the correct analysis package accordingly. If the file type is not supported by default the analysis will be aborted, therefore we encourage to specify the package name whenever possible.

For example, to launch a malware and specify some options you can do:

$ cuckoo submit --package dll --options function=FunctionName,loader=explorer.exe /path/to/malware.dll

Analysis Results¶

Once an analysis is completed, several files are stored in a dedicated directory. All the analyses are stored under the $CWD/storage/analyses/ inside a subdirectory named after the incremental numerical ID that represents the analysis task in the database.

Following is an example of an analysis directory structure:

.
|-- analysis.log
|-- binary
|-- dump.pcap
|-- memory.dmp
|-- files
|   |-- 1234567890_dropped.exe
|-- logs
|   |-- 1232.bson
|   |-- 1540.bson
|   `-- 1118.bson
|-- reports
|   |-- report.html
|   |-- report.json
`-- shots
    |-- 0001.jpg
    |-- 0002.jpg
    |-- 0003.jpg
    `-- 0004.jpg

analysis.log¶

This is a log file generated by the analyzer that contains a trace of the analysis execution inside the guest environment. It will report the creation of processes, files and eventual errors occurred during the execution.

dump.pcap¶

This is the network dump generated by tcpdump or any other corresponding network sniffer.

dump_sorted.pcap¶

This is a sorted version of dump.pcap in the sense that it allows the Web Interface to quickly lookup TCP stream.

memory.dmp¶

In case you enabled it, this file contains the full memory dump of the analysis machine.

files/¶

This directory contains all the files the malware operated on and that Cuckoo was able to dump.

files.json¶

This file contains a JSON-encoded entry for each dropped file available (i.e., all files in files/, shots/, etc). It contains meta information, where available, about all processes that touched the file, its original file path in the Guest, etc.

logs/¶

This directory contains all the raw logs generated by Cuckoo’s process monitoring.

reports/¶

This directory contains all the reports generated by Cuckoo as explained in the Configuration chapter.

shots/¶

This directory contains all the screenshots of the guest’s desktop taken during the malware execution.

tlsmaster.txt¶

This file contains the TLS Master Secrets that were captured during the analysis. TLS Master Secrets can be used to decrypt SSL/TLS traffic and are thus used to decrypt HTTPS streams.

Clean all Tasks and Samples¶

Changed in version 2.0.0: Turned into a proper Cuckoo App rather than a standalone script.

Since Cuckoo 1.2 a built-in clean feature has been featured, it drops all associated information of the tasks and samples in the database, on the harddisk, from MongoDB, and from ElasticSearch. If you submit a task after running clean you’ll start over with Task #1 again.

To clean your setup, run:

$ cuckoo clean

To sum up, this command does the following:

Delete analysis results.
Delete submitted binaries.
Delete all associated information of the tasks and samples in the configured database.
Delete all data in the configured MongoDB database (if configured and enabled in $CWD/conf/reporting.conf).
Delete all data in the configured ElasticSearch database (if configured and enabled in $CWD/conf/reporting.conf).

Warning

If you use this command you will permanently delete all data stored by Cuckoo in all available storages: the file system, the SQL database, the MongoDB database, and the ElasticSearch database. Use it only if you are sure you would clean up all the data.

Customization¶

This chapter explains how to customize Cuckoo. Cuckoo is written in a modular architecture built to be as customizable as it can, to fit the needs of all users.

Auxiliary Modules¶

Auxiliary modules define some procedures that need to be executed in parallel to every single analysis process. All auxiliary modules should be placed under the cuckoo/cuckoo/auxiliary/ directory, that way the module will fall under the cuckoo.auxiliary module.

The skeleton of a module would look something like this:

from cuckoo.common.abstracts import Auxiliary

class MyAuxiliary(Auxiliary):

    def start(self):
        # Do something.

    def stop(self):
        # Stop the execution.

The function start() will be executed before starting the analysis machine and effectively executing the submitted malicious file, while the stop() function will be launched at the very end of the analysis process, before launching the processing and reporting procedures.

For example, an auxiliary module provided by default in Cuckoo is called sniffer.py and takes care of executing tcpdump in order to dump the generated network traffic.

Machinery Modules¶

Machinery modules define how Cuckoo should interact with your virtualization software (or potentially even with physical disk imaging solutions). Since we decided to not enforce any particular vendor, from release 0.4 you are able to use your preferred solution and, in case it’s not supported by default, write a custom Python module that defines how to make Cuckoo use it.

Every machinery module should be located inside the cuckoo/cuckoo/machinery/ directory so that it will fall under the cuckoo.machinery module.

A basic machinery module would look like this:

from cuckoo.common.abstracts import Machinery
from cuckoo.common.exceptions import CuckooMachineError

class MyMachinery(Machinery):
    def start(self, label):
        try:
            revert(label)
            start(label)
        except SomethingBadHappens:
            raise CuckooMachineError("oops!")

    def stop(self, label):
        try:
            stop(label)
        except SomethingBadHappens:
            raise CuckooMachineError("oops!")

The only requirements for Cuckoo are that:

The class inherits from Machinery.

You have a start() and stop() functions.

You raise CuckooMachineError when something fails.

As you understand, the machinery module is a core part of a Cuckoo setup, therefore make sure to spend enough time debugging your code and make it solid and resistant to any unexpected error.

Configuration¶

Every machinery module should come with a dedicated configuration file located in $CWD/conf/<machinery module name>.conf (which translates to cuckoo/data/conf/<machinery>conf in the Git repository). For example for cuckoo/cuckoo/machinery/kvm.py we have a $CWD/conf/kvm.conf.

The configuration file should follow the default structure:

[kvm]
# Specify a comma-separated list of available machines to be used. For each
# specified ID you have to define a dedicated section containing the details
# on the respective machine. (E.g. cuckoo1,cuckoo2,cuckoo3)
machines = cuckoo1

[cuckoo1]
# Specify the label name of the current machine as specified in your
# libvirt configuration.
label = cuckoo1

# Specify the operating system platform used by current machine
# [windows/darwin/linux].
platform = windows

# Specify the IP address of the current machine. Make sure that the IP address
# is valid and that the host machine is able to reach it. If not, the analysis
# will fail.
ip = 192.168.122.105

A main section called [<name of the module>] with a machines field containing a comma-separated list of machines IDs.

For each machine you should specify a label, a platform and its ip.

These fields are required by Cuckoo in order to use the already embedded initialize() function that generates the list of available machines.

If you plan to change the configuration structure you should override the initialize() function (inside your own module, no need to modify Cuckoo’s core code). You can find its original code in the Machinery abstract inside cuckoo/common/abstracts.py.

LibVirt¶

Starting with Cuckoo 0.5 developing new machinery modules based on LibVirt is easy. Inside cuckoo/common/abstracts.py you can find LibVirtMachinery that already provides all the functionality for a LibVirt module. Just inherit this base class and specify your connection string, as in the example below:

from cuckoo.common.abstracts import LibVirtMachinery

class MyMachinery(LibVirtMachinery):
    # Set connection string.
    dsn = "my:///connection"

Starting with Cuckoo 2.0.7a1 you can use a custom dsn by setting it in the kvm.conf file. Example:

dsn = qemu+ssh://192.168.56.1/system

This works for all the virtualization technologies supported by LibVirt. Just remember to check if your LibVirt package (if you are using one, for example from your Linux distribution) is compiled with the support for the technology you need.

You can check it with the following command:

$ virsh -V
Virsh command line tool of libvirt 0.9.13
See web site at http://libvirt.org/

Compiled with support for:
 Hypervisors: QEmu/KVM LXC UML Xen OpenVZ VMWare Test
 Networking: Remote Daemon Network Bridging Interface Nwfilter VirtualPort
 Storage: Dir Disk Filesystem SCSI Multipath iSCSI LVM
 Miscellaneous: Nodedev AppArmor Secrets Debug Readline Modular

If you don’t find your virtualization technology in the list of Hypervisors, you will need to recompile LibVirt with the specific support for the missing one.

Analysis Packages¶

As explained in Analysis Packages, analysis packages are structured Python classes that describe how Cuckoo’s analyzer component should conduct the analysis procedure for a given file inside the guest environment.

As you already know, you can create your own packages and add them along with the default ones. Designing new packages is very easy and requires just a minimal understanding of programming and of the Python language.

Getting started¶

As an example we’ll take a look at the default package for analyzing generic Windows executables, located at $CWD/analyzer/windows/packages/exe.py (which translates to cuckoo/data/analyzer/windows/packages/exe.py in the Git repository):

from lib.common.abstracts import Package

class Exe(Package):
    """EXE analysis package."""

    def start(self, path):
        args = self.options.get("arguments")
        return self.execute(path, args)

It seems really easy, thanks to all method inherited by Package object. Let’s have a look as some of the main methods an analysis package inherits from Package object:

from lib.api.process import Process
from lib.common.exceptions import CuckooPackageError

class Package(object):
    def start(self):
        raise NotImplementedError

    def check(self):
        return True

    def execute(self, path, args):
        dll = self.options.get("dll")
        free = self.options.get("free")
        suspended = True
        if free:
            suspended = False

        p = Process()
        if not p.execute(path=path, args=args, suspended=suspended):
            raise CuckooPackageError(
                "Unable to execute the initial process, analysis aborted."
            )

        if not free and suspended:
            p.inject(dll)
            p.resume()
            p.close()
            return p.pid

    def finish(self):
        if self.options.get("procmemdump"):
            for pid in self.pids:
                p = Process(pid=pid)
                p.dump_memory()
        return True

Let’s walk through the code:

Line 1: import the Process API class, which is used to create and manipulate Windows processes.
Line 2: import the CuckooPackageError exception, which is used to notify issues with the execution of the package to the analyzer.
Line 4: define the main class, inheriting object.
Line 5: define the start() function, which takes as argument the path to the file to execute. It should be implemented by each analysis package.
Line 8: define the check() function.
Line 13: acquire the free option, which is used to define whether the process should be monitored or not.
Line 18: initialize a Process instance.
Line 19: try to execute the malware, if it fails it aborts the execution and notify the analyzer.
Line 24: check if the process should be monitored.
Line 25: inject the process with our DLL.
Line 26: resume the process from the suspended state.
Line 28: return the PID of the newly created process to the analyzer.
Line 30: define the finish() function.
Line 31: check if the procmemdump option was enabled.
Line 32: loop through the currently monitored processes.
Line 33: open a Process instance.
Line 34: take a dump of the process memory.

`start()`¶

In this function you have to place all the initialization operations you want to run. This may include running the malware process, launching additional applications, taking memory snapshots and more.

`check()`¶

This function is executed by Cuckoo every second while the malware is running. You can use this function to perform any kind of recurrent operation.

For example if in your analysis you are looking for just one specific indicator to be created (e.g., a file) you could place your condition in this function and if it returns False, the analysis will terminate right away.

Think of it as “should the analysis continue or not?”.

For example:

def check(self):
    if os.path.exists("C:\\config.bin"):
        return False
    else:
        return True

This check() function will cause Cuckoo to immediately terminate the analysis whenever C:\\config.bin is created.

`execute()`¶

Wraps the malware execution and deal with DLL injection.

`finish()`¶

This function is simply called by Cuckoo before terminating the analysis and powering off the machine. By default, this function contains an optional feature to dump the process memory of all the monitored processes.

Options¶

Every package have automatically access to a dictionary containing all user-specified options (see Submit an Analysis).

Such options are made available in the attribute self.options. For example let’s assume that the user specified the following string at submission:

foo=1,bar=2

The analysis package selected will have access to these values:

from lib.common.abstracts import Package

class Example(Package):

    def start(self, path):
        foo = self.options["foo"]
        bar = self.options["bar"]

    def check():
        return True

    def finish():
        return True

These options can be used for anything you might need to configure inside your package.

Process API¶

The Process class provides access to different process-related features and functions. You can import it in your analysis packages with:

from lib.api.process import Process

You then initialize an instance with:

p = Process()

In case you want to open an existing process instead of creating a new one, you can specify multiple arguments:

pid: PID of the process you want to operate on.

h_process: handle of a process you want to operate on.

thread_id: thread ID of a process you want to operate on.

h_thread: handle of the thread of a process you want to operate on.

This class implements several methods that you can use in your own scripts.

Methods¶

Process.open()¶

Opens an handle to a running process. Returns True or False in case of success or failure of the operation.

Return type:	boolean

Example Usage:

p = Process(pid=1234)
p.open()
handle = p.h_process

Process.exit_code()¶

Returns the exit code of the opened process. If it wasn’t already done before, exit_code() will perform a call to open() to acquire an handle to the process.

Return type:	ulong

Example Usage:

p = Process(pid=1234)
code = p.exit_code()

Process.is_alive()¶

Calls exit_code() and verify if the returned code is STILL_ACTIVE, meaning that the given process is still running. Returns True or False.

Return type:	boolean

Example Usage:

p = Process(pid=1234)
if p.is_alive():
    print("Still running!")

Process.get_parent_pid()¶

Returns the PID of the parent process of the opened process. If it wasn’t already done before, get_parent_pid() will perform a call to open() to acquire an handle to the process.

Return type:	int

Example Usage:

p = Process(pid=1234)
ppid = p.get_parent_pid()

Process.execute(path[, args=None[, suspended=False]])¶

Executes the file at the specified path. Returns True or False in case of success or failure of the operation.

Parameters:	path (string) – path to the file to execute args (string) – arguments to pass to the process command line suspended (boolean) – enable or disable suspended mode flag at process creation
Return type:	boolean

Example Usage:

p = Process()
p.execute(path="C:\\WINDOWS\\system32\\calc.exe", args="Something", suspended=True)

Process.resume()¶

Resumes the opened process from a suspended state. Returns True or False in case of success or failure of the operation.

Return type:	boolean

Example Usage:

p = Process()
p.execute(path="C:\\WINDOWS\\system32\\calc.exe", args="Something", suspended=True)
p.resume()

Process.terminate()¶

Terminates the opened process. Returns True or False in case of success or failure of the operation.

Return type:	boolean

Example Usage:

p = Process(pid=1234)
if p.terminate():
    print("Process terminated!")
else:
    print("Could not terminate the process!")

Process.inject([dll[, apc=False]])¶

Injects our DLL into the opened process. Returns True or False in case of success or failure of the operation.

Parameters:	dll (string) – path to the DLL to inject into the process apc (boolean) – enable to use `QueueUserAPC()` injection instead of `CreateRemoteThread()`, beware that if the process is in suspended mode, Cuckoo will always use `QueueUserAPC()`
Return type:	boolean

Example Usage:

p = Process()
p.execute(path="C:\\WINDOWS\\system32\\calc.exe", args="Something", suspended=True)
p.inject()
p.resume()

Process.dump_memory()¶

Takes a snapshot of the given process’ memory space. Returns True or False in case of success or failure of the operation.

Return type:	boolean

Example Usage:

p = Process(pid=1234)
p.dump_memory()

Processing Modules¶

Cuckoo’s processing modules are Python scripts that let you define custom ways to analyze the raw results generated by the sandbox and append some information to a global container that will be later used by the signatures and the reporting modules.

You can create as many modules as you want, as long as they follow a predefined structure that we will present in this chapter.

Global Container¶

After an analysis is completed, Cuckoo will invoke all the processing modules available in the cuckoo/processing/ directory, all of which fall under the cuckoo.processing module. Any additional module you decide to create must be placed inside that directory.

Every module should also have a dedicated section in the $CWD/conf/processing.conf file: for example if you create a module cuckoo/processing/foobar.py you will have to append the following section to $CWD/conf/processing.conf:

[foobar]
enabled = yes

Every module will then be initialized and executed and the data returned will be appended in a data structure that we’ll call global container.

This container is simply just a big Python dictionary that includes the abstracted results produced by all the modules classified by their identification key.

Cuckoo already provides a default set of modules which will generate a standard global container. It’s important for the existing reporting modules (HTML report etc.) that these default modules are not modified, otherwise the resulting global container structure would change and the reporting modules wouldn’t be able to recognize it and extract the information used to build the final reports.

The currently available default processing modules are:

AnalysisInfo (cuckoo/processing/analysisinfo.py) - generates some basic information on the current analysis, such as timestamps, version of Cuckoo and so on.
ApkInfo (cuckoo/processing/apkinfo.py) - generates some basic information on the current APK analysis (Android analysis).
Baseline (cuckoo/processing/baseline.py) - baseline results from gathered information.
BehaviorAnalysis (cuckoo/processing/behavior.py) - parses the raw behavioral logs and perform some initial transformations and interpretations, including the complete processes tracing, a behavioral summary and a process tree.
Buffer (cuckoo/processing/buffer.py) - dropped buffer analysis.
Debug (cuckoo/processing/debug.py) - includes errors and the analysis.log generated by the analyzer.
Droidmon (cuckoo/processing/droidmon.py) - extract Dynamic API calls Info From Droidmon logs.
Dropped (cuckoo/processing/dropped.py) - includes information on the files dropped by the malware and dumped by Cuckoo.
DumpTls (cuckoo/processing/dumptls.py) - cross-references TLS master secrets extracted from the monitor and key information extracted from the PCAP to dump a master secrets file.
GooglePlay (cuckoo/processing/googleplay.py) - Google Play information about the analysis session.
Irma (cuckoo/processing/irma.py) - IRMA connector.
Memory (cuckoo/processing/memory.py) - executes Volatility on a full memory dump.
Misp (cuckoo/processing/misp.py) - MISP connector.
NetworkAnalysis (cuckoo/processing/network.py) - parses the PCAP file and extracts some network information, such as DNS traffic, domains, IPs, HTTP requests, IRC and SMTP traffic.
ProcMemory (cuckoo/processing/procmemory.py) - performs analysis of process memory dump. Note: the module is able to process user defined Yara rules from data/yara/memory/index_memory.yar. Just edit this file to add your Yara rules.
ProcMon (cuckoo/processing/procmon.py) - extracts events from procmon.exe output.
Screenshots (cuckoo/processing/screenshots.py) - screenshot and OCR analysis.
Snort (cuckoo/processing/snort.py) - Snort processing module.
StaticAnalysis (cuckoo/processing/static.py) - performs some static analysis of PE32 files.
Strings (cuckoo/processing/strings.py) - extracts strings from the analyzed binary.
Suricata (cuckoo/processing/suricata.py) - Suricata processing module.
TargetInfo (cuckoo/processing/targetinfo.py) - includes information on the analyzed file, such as hashes.
VirusTotal (cuckoo/processing/virustotal.py) - searches on VirusTotal.com for antivirus signatures of the analyzed file. Note: the file is not uploaded on VirusTotal.com, if the file was not previously uploaded on the website no results will be retrieved.

Getting started¶

In order to make them available to Cuckoo, all processing modules must be placed inside the cuckoo/processing/ directory.

A basic processing module could look like:

from cuckoo.common.abstracts import Processing

class MyModule(Processing):

    def run(self):
        self.key = "key"
        data = do_something()
        return data

Every processing module should contain:

A class inheriting Processing.
A run() function.
A self.key attribute defining the name to be used as a sub container for the returned data.
A set of data (list, dictionary, string, etc.) that will be appended to the global container.

You can also specify an order value, which allows you to run the available processing modules in an ordered sequence. By default all modules are set with an order value of 1 and are executed in alphabetical order.

If you want to change this value your module would look like:

from cuckoo.common.abstracts import Processing

class MyModule(Processing):
    order = 2

    def run(self):
        self.key = "key"
        data = do_something()
        return data

You can also manually disable a processing module by setting the enabled attribute to False:

from cuckoo.common.abstracts import Processing

class MyModule(Processing):
    enabled = False

    def run(self):
        self.key = "key"
        data = do_something()
        return data

The processing modules are provided with some attributes that can be used to access the raw results for the given analysis:

self.analysis_path: path to the folder containing the results (e.g., $CWD/storage/analysis/1)

self.log_path: path to the analysis.log file.

self.file_path: path to the analyzed file.

self.dropped_path: path to the folder containing the dropped files.

self.logs_path: path to the folder containing the raw behavioral logs.

self.shots_path: path to the folder containing the screenshots.

self.pcap_path: path to the network pcap dump.

self.memory_path: path to the full memory dump, if created.

self.pmemory_path: path to the process memory dumps, if created.

With these attributes you should be able to easily access all the raw results stored by Cuckoo and perform your analytic operations on them.

As a last note, a good practice is to use the CuckooProcessingError exception whenever the module encounters an issue you want to report to Cuckoo. This can be done by importing the class like this:

from cuckoo.common.exceptions import CuckooProcessingError
from cuckoo.common.abstracts import Processing

class MyModule(Processing):

    def run(self):
        self.key = "key"

        try:
            data = do_something()
        except SomethingFailed:
            raise CuckooProcessingError("Failed")

        return data

Signatures¶

With Cuckoo you’re able to create some customized signatures that you can run against the analysis results in order to identify some predefined pattern that might represent a particular malicious behavior or an indicator you’re interested in.

These signatures are very useful to give a context to the analyses: both because they simplify the interpretation of the results as well as for automatically identifying malware samples of interest.

Some examples of what you can use Cuckoo’s signatures for:

Identify a particular malware family you’re interested in by isolating some unique behaviors (like file names or mutexes).
Spot interesting modifications the malware performs on the system, such as installation of device drivers.
Identify particular malware categories, such as Banking Trojans or Ransomware by isolating typical actions commonly performed by those.
Classify samples into the categories malware/unknown (it is not possible to identify clean samples)

You can find signatures created by us and by other Cuckoo users on our Community repository.

Getting started¶

Creation of signatures is a fairly simple process and requires just a decent understanding of Python programming.

First things first, all signatures must be located inside the cuckoo/cuckoo/signatures/ directory in Cuckoo or the modules/signatures/ directory of the Community repository (the Community repository is still using legacy directory structuring).

The following is a basic example signature:

from cuckoo.common.abstracts import Signature

class CreatesExe(Signature):
    name = "creates_exe"
    description = "Creates a Windows executable on the filesystem"
    severity = 2
    categories = ["generic"]
    authors = ["Cuckoo Developers"]
    minimum = "2.0"

    def on_complete(self):
        return self.check_file(pattern=".*\\.exe$", regex=True)

As you can see the structure is really simple and consistent with the other modules. We’re going to get into details later, but since version 1.2 Cuckoo provides some helper functions that make the process of creating signatures much easier.

In this example we just walk through all the accessed files in the summary and check if there is anything ending with “.exe”: in that case it will return True, meaning that the signature matched, otherwise return False.

The function on_complete is called at the end of the cuckoo signature process. Other function will be called before on specific events and help you to write more sophisticated and faster signatures.

In case the signature gets matched, a new entry in the “signatures” section will be added to the global container roughly as follows:

"signatures": [
    {
        "severity": 2,
        "description": "Creates a Windows executable on the filesystem",
        "alert": false,
        "references": [],
        "data": [
            {
                "file_name": "C:\\d.exe"
            }
        ],
        "name": "creates_exe"
    }
]

Creating your new signature¶

In order to make you better understand the process of creating a signature, we are going to create a very simple one together and walk through the steps and the available options. For this purpose, we’re simply going to create a signature that checks whether the malware analyzed opened a mutex named “i_am_a_malware”.

The first thing to do is import the dependencies, create a skeleton and define some initial attributes. These are the ones you can currently set:

name: an identifier for the signature.

description: a brief description of what the signature represents.

severity: a number identifying the severity of the events matched (generally between 1 and 3).

categories: a list of categories that describe the type of event being matched (for example “banker”, “injection” or “anti-vm”).

families: a list of malware family names, in case the signature specifically matches a known one.

authors: a list of people who authored the signature.

references: a list of references (URLs) to give context to the signature.

enable: if set to False the signature will be skipped.

alert: if set to True can be used to specify that the signature should be reported (perhaps by a dedicated reporting module).

minimum: the minimum required version of Cuckoo to successfully run this signature.

maximum: the maximum required version of Cuckoo to successfully run this signature.

In our example, we would create the following skeleton:

from cuckoo.common.abstracts import Signature

class BadBadMalware(Signature): # We initialize the class inheriting Signature.
    name = "badbadmalware" # We define the name of the signature
    description = "Creates a mutex known to be associated with Win32.BadBadMalware" # We provide a description
    severity = 3 # We set the severity to maximum
    categories = ["trojan"] # We add a category
    families = ["badbadmalware"] # We add the name of our fictional malware family
    authors = ["Me"] # We specify the author
    minimum = "2.0" # We specify that in order to run the signature, the user will simply need Cuckoo 2.0

    def on_complete(self):
        return

This is a perfectly valid signature. It doesn’t really do anything yet, so now we need to define the conditions for the signature to be matched.

As we said, we want to match a particular mutex name, so we proceed as follows:

from cuckoo.common.abstracts import Signature

class BadBadMalware(Signature):
    name = "badbadmalware"
    description = "Creates a mutex known to be associated with Win32.BadBadMalware"
    severity = 3
    categories = ["trojan"]
    families = ["badbadmalware"]
    authors = ["Me"]
    minimum = "2.0"

    def on_complete(self):
        return self.check_mutex("i_am_a_malware")

Simple as that, now our signature will return True whether the analyzed malware was observed opening the specified mutex.

If you want to be more explicit and directly access the global container, you could translate the previous signature in the following way:

from cuckoo.common.abstracts import Signature

class BadBadMalware(Signature):
    name = "badbadmalware"
    description = "Creates a mutex known to be associated with Win32.BadBadMalware"
    severity = 3
    categories = ["trojan"]
    families = ["badbadmalware"]
    authors = ["Me"]
    minimum = "2.0"

    def on_complete(self):
        for process in self.get_processes_by_pid():
            if "summary" in process and "mutexes" in process["summary"]:
                for mutex in process["summary"]["mutexes"]:
                    if mutex == "i_am_a_malware":
                        return True

        return False

Evented Signatures¶

Since version 1.0, Cuckoo provides a way to write more high performance signatures. In the past every signature was required to loop through the whole collection of API calls collected during the analysis. This was unnecessarily causing performance issues when such collection would be of a large size.

Since 1.2 Cuckoo only supports the so called “evented signatures”. The old signatures based on the run function can be ported to using on_complete. The main difference is that with this new format, all the signatures will be executed in parallel and a callback function called on_call() will be invoked for each signature within one single loop through the collection of API calls.

An example signature using this technique is the following:

from cuckoo.common.abstracts import Signature

class SystemMetrics(Signature):
    name = "generic_metrics"
    description = "Uses GetSystemMetrics"
    severity = 2
    categories = ["generic"]
    authors = ["Cuckoo Developers"]
    minimum = "2.0"

    # Evented signatures can specify filters that reduce the amount of
    # API calls that are streamed in. One can filter Process name, API
    # name/identifier and category. These should be sets for faster lookup.
    filter_processnames = set()
    filter_apinames = set(["GetSystemMetrics"])
    filter_categories = set()

    # This is a signature template. It should be used as a skeleton for
    # creating custom signatures, therefore is disabled by default.
    # The on_call function is used in "evented" signatures.
    # These use a more efficient way of processing logged API calls.
    enabled = False

    def on_complete(self):
        # In the on_complete method one can implement any cleanup code and
        #  decide one last time if this signature matches or not.
        #  Return True in case it matches.
        return False

    # This method will be called for every logged API call by the loop
    # in the RunSignatures plugin. The return value determines the "state"
    # of this signature. True means the signature matched and False it did not this time.
    # Use self.deactivate() to stop streaming in API calls.
    def on_call(self, call, pid, tid):
        # This check would in reality not be needed as we already make use
        # of filter_apinames above.
        if call["api"] == "GetSystemMetrics":
            # Signature matched, return True.
            return True

        # continue
        return None

The inline comments are already self-explanatory.

Another event is triggered when a signature matches.

required = ["creates_exe", "badmalware"]

def on_signature(self, matched_sig):
    if matched_sig in self.required:
        self.required.remove(matched_sig)

    if not self.required:
        return True

    return False

This kind of signature can be used to combine several signatures identifying anomalies into one signature classifying the sample (malware alert).

Marks & Helpers¶

Starting from version 1.2, signatures are able to log exactly what triggered the signature. This allows users to better understand why this signature is present in the log, and to be able to better focus malware analysis.

For examples on marks and helpers please refer to the Cuckoo Community for now - until we write some thorough up-to-date documentation on that.

Reporting Modules¶

After the raw analysis results have been processed and abstracted by the processing modules and the global container is generated (ref. Processing Modules), it is passed over by Cuckoo to all the reporting modules available, which will make use of it and will make it accessible and consumable in different formats.

Getting Started¶

All reporting modules must be placed inside the cuckoo/cuckoo/reporting/ directory (which translates to the cuckoo.reporting module).

Every module must also have a dedicated section in the $CWD/conf/reporting.conf file: for example if you create a module cuckoo/cuckoo/reporting/foobar.py you will have to append the following section to $CWD/conf/reporting.conf (and thus cuckoo/data/conf/reporting.conf in the Git repository):

[foobar]
enabled = on

Every additional option you add to your section will be available to your reporting module in the self.options dictionary.

Following is an example of a working JSON reporting module:

import os
import json
import codecs

from cuckoo.common.abstracts import Report
from cuckoo.common.exceptions import CuckooReportError

class JsonDump(Report):
    """Saves analysis results in JSON format."""

    def run(self, results):
        """Writes report.
        @param results: Cuckoo results dict.
        @raise CuckooReportError: if fails to write report.
        """
        try:
            report = codecs.open(os.path.join(self.reports_path, "report.json"), "w", "utf-8")
            json.dump(results, report, sort_keys=False, indent=4)
            report.close()
        except (UnicodeError, TypeError, IOError) as e:
            raise CuckooReportError("Failed to generate JSON report: %s" % e)

This code is very simple, it receives the global container produced by the processing modules, converts it into JSON and writes it to a file.

There are few requirements for writing a valid reporting module:

Declare your class inheriting from Report.

Have a run() function performing the main operations.

Try to catch most exceptions and raise CuckooReportError to notify the issue.

All reporting modules have access to some attributes:

self.analysis_path: path to the folder containing the raw analysis results (e.g. storage/analyses/1/)

self.reports_path: path to the folder where the reports should be written (e.g. storage/analyses/1/reports/)

self.options: a dictionary containing all the options specified in the report’s configuration section in conf/reporting.conf.

Development¶

This chapter explains how to write Cuckoo’s code and how to contribute.

Development Notes¶

Git branches¶

Cuckoo Sandbox source code is available in our official Git repository.

Up until version 1.0 we used to coordinate all ongoing development in a dedicated “development” branch and we’ve been exclusively merging pull requests in such branch. Since version 1.1 we moved development to the traditional “master” branch and we make use of GitHub’s tags and release system to reference development milestones in time.

Release Versioning¶

At the moment we utilize three types of releases: * 1.2.3, an official release, preferably accompanied by a blogpost * 1.2.4a1, an alpha release that showcases functionality that will be present in the upcoming release * 1.2.3.1, a hotfix release, meant to fix critical issues, usually found in the latest official release

Ticketing system¶

To submit bug reports or feature requests, please use GitHub’s Issue tracking system.

Contribute¶

To submit your patch just create a Pull Request from your GitHub fork. If you don’t now how to create a Pull Request take a look to GitHub help.

Coding Style¶

In order to contribute code to the project, you must diligently follow the style rules describe in this chapter. Having a clean and structured code is very important for our development lifecycle. We do help out with code refactoring where required, but please try to do as much as possible on your own.

Essentially Cuckoo’s code style is based on PEP 8 - Style Guide for Python Code and PEP 257 – Docstring Conventions.

Formatting¶

Copyright header¶

All existing source code files start with the following copyright header:

# Copyright (C) 2010-2013 Claudio Guarnieri.
# Copyright (C) 2014-2016 Cuckoo Foundation.
# This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org
# See the file 'docs/LICENSE' for copying permission.

Newly created files should start with the following copyright header:

# Copyright (C) 2016 Cuckoo Foundation.
# This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org
# See the file 'docs/LICENSE' for copying permission.

Indentation¶

The code must have a 4-spaces-tabs indentation. Since Python enforce the indentation, make sure to configure your editor properly or your code might cause malfunctioning.

Maximum Line Length¶

Limit all lines to a maximum of 79 characters.

Blank Lines¶

Separate the class definition and the top level function with one blank line. Methods definitions inside a class are separated by a single blank line:

class MyClass:
    """Doing something."""

    def __init__(self):
        """Initialize"""
        pass

    def do_it(self, what):
        """Do it.
        @param what: do what.
        """
        pass

Use blank lines in functions, sparingly, to isolate logic sections. Import blocks are separated by a single blank line, import blocks are separated from classes by one blank line.

Imports¶

Imports must be on separate lines. If you’re importing multiple objects from a package, use a single line:

from lib import a, b, c

NOT:

from lib import a
from lib import b
from lib import c

Always specify explicitly the objects to import:

from lib import a, b, c

NOT:

from lib import *

Strings¶

Strings must be delimited by double quotes (“).

Printing and Logging¶

We discourage the use of print(): if you need to log an event please use Python’s logging which is already initialized by Cuckoo.

In your module add:

import logging
log = logging.getLogger(__name__)

And use the log handle. More details can be found in the Python documentation, but as follows is an example:

log.info("Log message")

Exceptions¶

Custom exceptions must be defined in the cuckoo/common/exceptions.py file.

The following is the current Cuckoo exceptions chain:

.-- CuckooCriticalError
|   |-- CuckooStartupError
|   |-- CuckooDatabaseError
|   |-- CuckooMachineError
|   `-- CuckooDependencyError
|-- CuckooOperationalError
|   |-- CuckooAnalysisError
|   |-- CuckooProcessingError
|   `-- CuckooReportError
`-- CuckooGuestError

Beware that the use of CuckooCriticalError and its child exceptions will cause Cuckoo to terminate.

Naming¶

Custom exception names must start with “Cuckoo” and end with “Error” if it represents an unexpected malfunction.

Exception handling¶

When catching an exception and accessing its handle, use as e:

try:
    foo()
except Exception as e:
    bar()

NOT:

try:
    foo()
except Exception, something:
    bar()

It’s a good practice use “e” instead of “e.message”.

Documentation¶

All code must be documented in docstring format, see PEP 257 – Docstring Conventions. Additional comments may be added in logical blocks to make the code easier to understand.

Automated testing¶

We believe in automated testing to provide high quality code and avoid easily overlooked mistakes.

When possible, all code must be committed with proper unit tests. Particular attention must be placed when fixing bugs: it’s good practice to write unit tests to reproduce the bug. All unit tests and fixtures are placed in the tests folder in the Cuckoo root. We have adopted Pytest as unit testing framework.

Development with the Python Package¶

With the new Python package developing and testing code now works slightly different than it used to be. As one will first have to Install Cuckoo before being able to use it in the first place, a simple modify-and-test development sequence doesn’t work out-of-the-box as it used to do.

Following we outline how to develop and test new features while using the Cuckoo Package.

Initialize a new virtualenv. Note that any virtualenv’s in /tmp won’t survive a reboot and as such a more convenient location may be, e.g., ~/venv/cuckoo-development (i.e., place the cuckoo-development virtualenv in a generic ~/venv/ directory for all your virtualenv’s).
```
$ virtualenv /tmp/cuckoo-development
```
Activate the virtualenv. This has to be done every time you start a new shell session (unless you put the command in ~/.bashrc or similar, of course).
```
$ . /tmp/cuckoo-development/bin/activate
```
In order to create a Cuckoo distribution package it is required to obtain the matching monitoring binaries from our Community repository for this version of Cuckoo. Fortunately we provide a simple-to-use script to fetch them semi-automatically for you. From the repository root directory one may run as follows to automatically grab the binaries.
```
(cuckoo-development)$ python stuff/monitor.py
```
Install Cuckoo in development mode, in which files from the current directory (a git clone’d Cuckoo repository on the package branch) will be used during execution.
```
(cuckoo-development)$ python setup.py sdist develop
```

You will now be ready to modify and test files. Note that the code files are located in the cuckoo/ directory of the Git repository and the fact that, even though you will be testing a development version of the repository, all the rules from the Cuckoo Working Directory and Cuckoo Working Directory Usage are still in-place.

Happy development! Please reach out to us if you require additional help to get up-and-running with the latest development tricks.

Frontend¶

Caution

This documentation is WIP.

The Javascript code in Cuckoo web is developed in ECMASript 6. For browser compatibility, this will need to be transpiled back to ECMAScript 5.

Cuckoo makes use of Gulp to build from source to static (frontend sources). Before you can use this, make sure that the following dependencies are installed (required for Node and following assets):

Node.js (http://nodejs.org)

NPM (comes with the installation of Node)

Sass (http://sass-lang.com)

Gulp (http://gulpjs.com)

On a Debian based system the package requirements are:

apt-get install build-essential
curl -sL https://deb.nodesource.com/setup_6.x | sudo -E bash -
sudo apt-get install nodejs
sudo npm install gulp -g
sudo apt-get install ruby-full rubygems
sudo gem update --system
sudo gem install sass
npm install
# At this point the required libs are installed.
# Run the following command to build new JS and CSS:
gulp build

After these packages have been installed, navigate to the source folder (cd cuckoo/web/src) and run npm install. This will install all node modules as listed in package.json. After this you should be good to go!

NPM Executables¶

While in the cuckoo/web/src directory (or the directory where package.json is located) you can run the following commands:

gulp OR npm start: runs build processes and starts watcher
gulp build OR npm run-script build: build source to static ONCE.
gulp styles: only runs the ‘styles’ task (compiles SCSS to static/css)

Transpiling/Compiling¶

Try modifying one of the .js files in the cuckoo/web/src/scripts/ directory and confirm that Pycharm transpiles the Javascript to ECMAScript 5.

Creating new tasks¶

You can easily plug in new tasks by creating a new javascript file in cuckoo/web/src/tasks. Gulpfile.js automagically loads these tasks and will be available throughout the npm session gulp uses (this means you don’t have to do a lot more.).

A gulp module in its basic form looks like this:

- coming!

Developing with Pycharm¶

Within this section we will cover a vast array of Pycharm configuration options in the context of Cuckoo development. We will try to cover all aspects of running and developing Cuckoo under this IDE.

Cuckoo Web¶

This section covers the Cuckoo Web interface that runs on Django. The code is quite easy to modify and creating custom features is simple.

Locations and concepts¶

Cuckoo Web provides the web interface and a REST API
The Django project root is located at cuckoo/web
The configuration is located at cuckoo/web/web/settings.py
URL dispatchers are in cuckoo/web/web/urls.py, as well as other locations such as (but not limited to) cuckoo/web/analysis/urls.py
The HTML templates use the Django Templating Language.
The front-end uses cuckoo/web/static/js/cuckoo/ for the Cuckoo related JavaScript includes, while their sources are in cuckoo/web/static/js/cuckoo/src/ (ECMAScript 5/6) - See paragraph ‘JavaScript transpiling’.
So called ‘controllers’ are used instead of class-based views, where a controller is responsible for (usually back-end) actions that don’t belong in view functions. Example: cuckoo/web/controllers/analysis/analysis.py
View functions are functions used by views, located in routes.py. Example: cuckoo/web/controllers/analysis/routes.py
API functions are functions used by the API, located in api.py. Example: cuckoo/web/controllers/analysis/api.py

Running and debugging¶

Running and debugging Cuckoo web straight from Pycharm comes down to circumventing the cuckoo launcher and using Pycharm’s built-in Django server. Thankfully, no modifications are neccesary to the Cuckoo code in order to do this.

Firstly, It is recommended that you work in a virtualenv to keep the dependencies required by Cuckoo seperate from your system-wide installed Python. Secondly, you should install Cuckoo in development mode; python setup.py develop.

Assuming Cuckoo is installed correctly (and has an active working directory; see Cuckoo Working Directory Installation ); Start Pycharm and open the Cuckoo directory. Go to Run->Edit Configurations and click the + button. Pick ‘Django server’. Use the following values:

Name - web
Host - 127.0.0.1
Port - 8080
Environment variables - Click ... and add 2 new values: CUCKOO_APP: web and CUCKOO_CWD: /home/test/.cuckoo/, where the path is the location of your CWD (Cuckoo Working Directory).
Python interpreter - Pick the virtualenv you made earlier. If it’s not there, add the virtualenv to this project under File->Settings->Project: Cuckoo->Project Interpreter
Working directory - This absolute path to the Django project root. For me this is /home/test/PycharmProjects/virtualenv/cuckoo/cuckoo/web/

Cuckoo web can now be run (and debugged) from Pycharm. Go to Run->Run->web from the menu and the webserver shall start.

JavaScript transpiling¶

Caution

Transpiling JavaScript through Pycharm file watchers is not recommended. The recommended way is explained in the ‘Frontend’ section of the documentation.

The Javascript code in Cuckoo web is developed in ECMAScript 6. For browser compatibility, this will need to be transpiled back to ECMAScript 5.

Firstly, make Pycharm regonize and understand the ECMAScript 6 syntax. Go to File->Settings->Languages & Frameworks->Javascript and pick ‘ECMAScript 6’ from the ‘Javascript language version’ dropdown. Hit Apply.

Then, use Babel to transpile the Javascript code. Install Babel in the Cuckoo project root (requires npm):

(cuckoo)    test:$ pwd
/home/test/PycharmProjects/virtualenv/cuckoo
(cuckoo)    test:$ npm install --save-dev babel-cl

Which will create a folder called node_modules in the Cuckoo project root.

Switch back to Pycharm and open any .js file in cuckoo/web/static/js/cuckoo/src/. Pycharm will ask you if you want to configure a File watcher for this file. Click Add watcher (if this option is not available to you, find the ‘file watcher’ configuration under File->Settings->Tools->File watchers).

In the following pop-up screen ‘Edit Watcher’, enter these values.

Name - Babel ES6->ES5
Description - Transpiles ECMAScript 6 code to ECMAScript 5
Output filters - None
Show console - Error
Immediate file synchronisation - yes
Track only root files - yes
Trigger watcher regardless of syntax errors - no
File type - Javascript
Scope - Click ... -> Click + (add scope) -> Click local -> Press OK. In the file browser, browse to cuckoo/web/static/js/cuckoo/src/ and whilst selecting the src folder, click include. The files containing in src should now turn green. Press OK.
Program - Should be the absolute path to node_modules/.bin/babel, for me this is /home/test/PycharmProjects/virtualenv/cuckoo/node_modules/.bin/babel. Double check that the path you enter reflects the actual location of the node_modules/.bin/babel file.
Arguments - --source-maps --out-file $FileNameWithoutExtension$.js $FilePath$
Working directory - Browse and select cuckoo/web/static/js/cuckoo
Output paths to refresh $FileNameWithoutExtension$-compiled.js:$FileNameWithoutExtension$-compiled.js.map

Finally; a mock manage.py file needs to be created in order for Pycharm to see it as a Django project. Create the following file cuckoo/web/web/manage.py with the contents:

#!/usr/bin/env python
import sys

if __name__ == "__main__":
   from django.core.management import execute_from_command_line
   execute_from_command_line(sys.argv)

Go to File->Settings->Languages & Frameworks->Django and;

Django Project root - cuckoo/web
Settings - web/settings.py
Manage script - web/manage.py

Testing¶

The configuration should now be complete. Try running Cuckoo from within Pycharm & happy coding!

Final Remarks¶

Links¶

www.cuckoosandbox.org

github.com/cuckoosandbox

malwr.com

Join the discussion¶

If you are encountering an issue you can’t solve and are looking for some help, go to our Discussion page and pick a platform of your choice. This is where you can get in contact with the Cuckoo Developers and users (our preference go to the Slack & IRC).

Please read the following rules before posting:

Before posting, read our Github issue tracker, the Cuckoo blog, the documentation and Google about your issue. DO NOT post questions that have already been answered over and over everywhere.

Posting messages saying just something like “Doesn’t work, help me” are completely useless. If something is not working report the error, paste the logs, the configuration files, the information on the virtual machine, the results of the troubleshooting, etc. Give context. We are not wizards and we don’t have a crystal ball.

Use a proper title. Stuff like “Doesn’t work”, “Help me”, “Error” are not proper titles.

Support Us¶

Cuckoo Sandbox is a completely open source software, released freely to the public and developed mostly during free time by volunteers. If you enjoy it and want to see it kept developed and updated, please consider supporting us.

We are always looking for financial support, hardware support and contributions of any sort. If you’re interested in cooperating, feel free to contact us.

People¶

Cuckoo Sandbox is an open source project result of the efforts and contributions of a lot of people who enjoyed volunteering some of their time for a greater good :).

Active Developers¶

Name Role Contact

Claudio nex Guarnieri Project Founder nex at nex dot sx

Alessandro jekil Tanasi Core Developer alessandro at tanasi dot it

Jurriaan skier Bremer Lead Developer jbr at cuckoo dot sh

Mark rep Schloesser Core Developer ms at mwcollect dot org

Contributors¶

It’s hard at this point to keep track of all individual contributions. In the Cuckoo Contributors page there is the list of people who contributed code to our GitHub repository.

There is a number of friends who provided feedback, ideas and support during the years of development of this project, including but not limited to:

Felix Leder

Tillmann Werner

Georg Wicherski

David Watson

Christian Seifert

Supporters¶

The Honeynet Project

The Shadowserver Foundation