Count Upon Security

As threats and technology continue to evolve and malware becomes more sophisticated, the acquisition of volatility data and its forensic analysis is a key step during incident response and digital forensics. This article is about acquiring RAM from a disposable virtual machine before and after malware infection. This data will then be analyzed using the powerful Volatility Framework. The steps described here go hand in hand with the steps described on previous blog posts here and here about dynamic malware analysis.

We will use the methodology stated in previous post. We will start the virtual machine in a known clean state by restoring it from a snapshot or starting a golden copy. Then we will suspend/pause the machine in order to create a checkpoint state file. This operation will create a VMSS file inside the directory where the virtual machine is stored. Then we will use a tool provided by VMware named Vmss2core to convert this VMSS file into a WinDbg file (memory.dmp).  From VMware website: Vmss2core is a tool to convert VMware checkpoint state files into formats that third party debugger tools understand. Last version is available here. It can handle both suspend (.vmss) and snapshot (.vmsn) checkpoint state files as well as both monolithic and non-monolithic (separate .vmem file) encapsulation of checkpoint state data. This tools will allow us to convert the VMware memory file into a format that is readable by the Volatility Framework.

Next, we restore the virtual machine to back where it was and we infect it with malware. After that, we suspend the machine again and repeat the previous steps. This allow us to have two memory dumps that we can compare against each other in order to facilitate the finding of malicious code.

To perform the analysis and the memory forensics we will use the powerful Volatility Framework. The Volatility Framework born based on the research that was done by AAron Walters and Nick Petroni on Volatools and FATkit. The first release of the Volatility Framework was released in 2007. In 2008 the volatility team won the DFRWS and added the features used in the challenge to Volatility 1.3.  Seven years have passed and this framework has gained a lot of popularity and attention from the security community. The outcome has been a very powerful, modular and feature rich framework that combines a number of tools to perform memory forensics. The framework is written in Python and allows plugins to be easily added in order to add features. Nowadays it is on version 2.4, it supports a variety of operating systems and is being actively maintained.

Below the  steps needed to acquire the RAM data for a VMware virtual machine:

• Restore the VMware snapshot to a known clean state.
• Pause the Virtual Machine
• Execute vmss2core_win.exe
• Rename the memory.dmp file to memory-before.dmp
• Resume the virtual machine
• Execute the malware
• Pause the virtual machine
• Execute vmss2core_win.exe
• Rename the memory.dmp file to memory-after.dmp

The following picture ilustrates the usage of Vmss2core_win.exe.

After performing this steps we can proceed to the analysis. First we move the memory dumps to REMnux. Then we start the Volatility Framework which comes pre-installed. We start our analysis by listing the process as a tree using the “pstree” module on both the clean and infected memory image. With this we can benchmark the infected machine and easily spot difference on infections performed by commodity malware.  In this case we can easily spot that on the infected machine there a process named explorer.exe (PID 1872) that references a parent process (PPID 1380) that is not listed. In addition we can observe the processes vssadmin.exe (PID 1168) and iexplorer.exe (PID 1224) which were spawned by explorer.exe.  Vsadmin.exe can manage volume shadow copies on the system. This strongly suggests that we are in presence of something suspicious.

Next step is to do a deep dive. During the previous dynamic malware analysis articles series were we analyzed the same piece of malware we were able to extract indicators of compromise (IOC). These IOC’s were extracted  from our analysis performed at the network level and system level. We will use this information for our own advantage in order to reduce the effort needed to find Evil. We knew that after the infection the malware will attempt to contact the C&C using a HTTP POST request to the URL topic.php which was hosted in Russia.

Let’s use this pieces of information to search and identify the malicious code. To perform this we will look for ASCII and UNICODE strings that match those artifacts using src_strings utility that is part of The Sleuth Kit from Brian Carrier and is installed on REMnux.

The  parameter “-t d” will tell the tool to print the location of the string in decimal. We save this findings in a text file. The results are  then used by Volatility. Next, we invoke Volatility and use the “strings” plugin that can map physical offsets to virtual addresses – this only works on raw memory images and not on crash dumps – . It works by traversing all the active processes  and is able to determine which ones accessed the specified strings.  In case of a match we will have a good hint on where the malicious code might reside. In this case the plugin is able to match the strings with the process ID 1872 on two different virtual addresses.  This corroborates the fact that on the previous step we saw this process without a parent process which was rather suspicions . Another clue that we might be in good direction.

This can be easily confirmed by listing the process ID 1872 using the “pslist” module.   Given this fact we proceed with the extract of this process from memory to a file using the “procmemdump” plugin. Volatility Framework is so powerful that it can parse PE headers in memory and carve them out. With this plugin we can dump the process executable using its PID.  After extraction we can verify that the file commands correctly identifies the file as a PE32 executable. Then we can verify that the string we found previous is indeed part of this process.

Well, looks like the string “topic.php” is not in the data we just extracted. However, as we could see in the previous analysis steps we determined that the actions performed by the malicious code were in the context of this process. Furthermore, during the basic static analysis performed (see previous post) on the malicious executable we saw that it references very few libraries but two of them were noteworthy. The LoadLibrary and GetProcAddress.  This suggest the malicious executable was either packed or encrypted. In addition those libraries suggest that  malicious code was injected into a process.  There are several techniques used by threat actors to inject malicious code into benign processes. I will expand on this techniques on a different blog post. As conclusion we might be looking at malicious code that was injected in the process explorer.exe (PID 1832) and is somehow concealed. This means we will need to look deeper into the obscure and fascinating world of memory forensics. Part two will follow!

Malware sample used in this exercise:

remnux@remnux:~/Desktop$ pehash torrentlocker.exe
file:             torrentlocker.exe
md5:              4cbeb9cd18604fd16d1fa4c4350d8ab0
sha1:             eaa4b62ef92e9413477728325be004a5a43d4c83
ssdeep:           6144:NZ3RRJKEj8Emqh9ijN9MMd30zW73KQgpc837NBH:LhRzwEm7jEjz+/g2o7fH

References:

Ligh, M. H. (2011). Malware analyst’s cookbook and DVD: Tools and techniques for fighting malicious code.
Zeltser, Lenny (2013) SANS FOR610: Reverse-Engineering Malware: Malware Analysis Tools and Techniques