On our daily job, we have to manage malicious piece of code every day. On this domain, we historically had two approaches: dynamic analysis on our own sandbox or manual and static analysis with reverse engineering skills.
Because static analysis can be boring for known samples, we developed a framework to automatically analyzing malware.
We released Disass some time ago and gave a short explanation of the tool during Botconf 2013 in Nantes, France. We received many comments and questions so we thought a blog post could help explain the way Disass is working.
Until last year, in order to automate static analysis, we wrote scripts (often in Python because Python is cool)
that can highlight and extract relevant informations from malicious binaries. But these scripts are seldom robust
and their behaviour is only guaranteed on the sample the manual analysis has been done.
That's precisely why we wrote Disass. Basically, Disass is a binary analysis framework written in Python to ease the
automation of malware reverse engineering. The purpose of Disass
is to automatically retrieve relevant information in malware such as the C&C, the user agent, cipher keys, etc.
By the way, Disass allows to understand static analysis in human readable code
There are two types of disassembler algorithms: linear and flow-oriented.
Disass is based on a linear disassembly module named "diStorm", which is a lightweight, easy-to-use and fast decomposer library.
A linear disassembly uses the size of the disassembled instruction to determine which byte should be disassembled next,
without regarding flow-control instructions.
The interesting point in a linear disassembly is that it's made for iteratively work on a block of code.
The bad point is that linear disassembly is unsuitable to distinguish code and data. It can be partially circumvented with the use of a tool such as pefile.
Let's go deeper: to understand how to use the framework, the example below shows the usage of a Disass script on a real malware called
"Trojan.Letsgo". This malware was made famous by the APT1 report from Mandiant. Further information on this malware can be obtained on http://www.cyberengineeringservices....