Egress-Assess Malware Modules

Github Link – https://github.com/ChrisTruncer/Egress-Assess

Steve Borosh (@424f424f) and I have been working on adding a new type of module into Egress-Assess for a month or two now. Currently, Egress-Assess lets you exfiltrate faux or real data over a variety of different protocols on both Linux and Windows systems.  However, Steve had the idea to create malware modules for Egress-Assess, and we started working on it.

A major resource that needs to be called out for really helping to push this idea forward is Raphael Mudge with the Malleable C2 Profiles that he created for use with Beacon.  Props to him for adding an awesome capability for Beacon and helping to push the idea of hacking to get caught forward.

We want to be able to allow users to use Egress-Assess to emulate known malware within any network.  We scoured the internet for various sources where companies have documented different network indicators used to identify malware operating over the network.  After a lot of research, we are happy to merge in the following malware modules into Egress-Assess:

Egress-Assess Supported Malware

  • Zeus
  • Darkhotel
  • etumbot
  • putterpanda

The various malware modules use known/documented C2 domains for the host headers (which are randomly selected from a large list of documented domains).  Additionally, if the malware being emulated is using GET/POST requests for C2 comms (which all currently included are), we are then also emulating the malware’s comms via each malware family’s respective method for communicating (custom uri parameters, post request data, etc.).

Ideally, you should now have the ability to generate network traffic that conforms to the respective malware’s documented methodology for C2 comms (for which we have created a module).  If there are any requests for specific pieces of malware that you would like to see added into Egress-Assess, please get in touch with Steve or myself on twitter, e-mail, or create an Issue on Github and let us know what you would like added in.