Tuesday, July 5, 2022

The 10th Annual Volatility Plugin Contest!

This year not only marks 15 years since the first public release of Volatility, we are also excited to announce the 10th annual Volatility Plugin Contest is now open! Submissions will be accepted until December 31, 2022.

Volatility Plugin Contest

The 2022 Volatility Plugin Contest is your chance to get industry-wide visibility for your work, put groundbreaking capabilities into the hands of investigators, and contribute back to the open source forensics community. Since its inception, the contest has encouraged research and development in the field of memory analysis. Over the last 10 years, participant contributions from all around the world have helped to lay the foundation for the next generation of memory forensics.  

Winners this year will receive over 6000 USD in cash prizes! 

Get visibility for your work, contribute to an important open-source project, and get a chance to win a cash prize! For more information, see the full details here: 2022 Volatility Plugin Contest  

If you are looking for inspiration, check out the 2021 Volatility Plugin Contest Results.

Acknowledgements


We would like to thank Volexity and our other sustaining donors for their continued support.

Friday, February 18, 2022

The 2021 Volatility Plugin Contest results are in!

Results from the 9th Annual Volatility Plugin Contest are in! And this year, there were 7 submissions from 7 different countries! Submissions included a new web interface, a new address layer, 6 updates to existing plugins, and 15 new Volatility 3 plugins. Once again, we would like to thank the participants for their hard work on their submissions and contributions to Volatility. As in previous years, it was great to see contestants who had submitted in prior contests and submissions from across the global Volatility community.  

It's now 15 years since the first public release of Volatility! It has been exciting to see researchers in the memory forensics field continue to innovate. Later this year, we are planning something special to commemorate all the contributors who have joined us on this journey. 

Independent open source projects and communities only remain viable because of contributors who are willing to sacrifice their time and resources. Please show your appreciation for the contestants’ contributions by following them on Twitter/GitHub/LinkedIn, providing feedback on their ideas, and helping to improve their code with testing, documentation, or contributing patches. 


We would like to thank Volexity for being a sustaining sponsor of the Volatility Foundation and, in particular, for contributing to this year’s contest. We would also like to thank the core Volatility developers and the previous winners of the contest who helped review and deliberate the submissions.

Placements and Prizes for the 2021 Volatility Plugin Contest:

1st place and $3000 USD cash or One Free Seat at Malware and Memory Forensics Training by the Volatility Team goes to:

 Amir Sheffer & Ofek Shaked: Linux Namespaces Support and Docker Plugin

2nd place and $2000 USD cash goes to:

Kevin Breen: Symbol Generator & Public ISF Server, Cobalt Strike Plugin, Rich Header Plugin, and LastPass Credential Recovery Plugin

3rd place and $1000 USD cash goes to:

Frank Block: PTE Analysis Plugins 


Below is a detailed summary of all submissions, ordered alphabetically by first name. If you have feedback for the participants, we're sure they'd love to hear your thoughts! As previously mentioned, these developers deserve praise for their amazing work. We look forward to seeing future work by these authors!

Amir Sheffer & Ofek Shaked: Linux Namespaces Support and Docker Plugin

Container technology is widely used in production Linux settings, and the highly focused analysis of per-container information can help to greatly focus investigations and identify key related artifacts. This submission provides a suite of Volatility 3 plugins for memory forensics of Docker containers.  This included expanding core capabilities in Volatility 3 by making them aware of Linux namespaces and augmenting the number of supported kernel versions.  For example, an analyst can quickly detect the presence of a container, collect information about the container and its capabilities, display information about its mount points, and provide detailed network configuration data.

Related References:

https://github.com/amir9339/volatility-docker
https://github.com/oshaked1
https://github.com/amir9339

Felix Guyard: VolWeb

This submission provides an exciting new web interface to Volatility 3 built using the Django framework. The objectives for the project were to improve investigator efficiency, centralize collaborative analysis, and make memory analysis more "human" friendly. VolWeb also allows investigators to manage memory analysis investigations and search for string-based indicators of compromise. It provides a promising new platform for future work and integrations.

Related References:

https://twitter.com/k1nd0ne
https://k1nd0ne.github.io/index.html
https://github.com/k1nd0ne/VolWeb

Frank Block: PTE Analysis Plugins

The author contributes several Windows plugins for Volatility 3 that extend the code injection detection capabilities of malfind, while also adding low-level PTE enumeration functionality similar to !pte in Windbg. Building on the author's novel research, he has identified potential false negatives in malfind that can occur when the Windows VAD data does not match the underlying page protections, encoded in the PTEs. The author has written a comprehensive library for enumerating and inspecting Windows PTEs and a set of example capabilities on top. All-in-all, it's a great contribution to the Volatility 3 ecosystem! It is also extremely well documented with research publications, blog posts, and a great talk on the subject.

Related References:

https://insinuator.net/2021/12/release-of-pte-analysis-plugins-for-volatility-3
https://github.com/f-block/volatility-plugins

Gerhart: Hyper-V Volatility Introspection Layer

Virtual memory introspection is a technique for monitoring the runtime state of a virtual machine. This submission adds the ability to analyze live Windows Hyper-V virtual machines without acquiring a full memory dump. The new Volatility 3 layer for Hyper-V adds an interface reminiscent of LiveCloudKd or Sysinternals LiveKd, but with the power of Volatility 3's extensive plugins.

Related References:

https://twitter.com/gerhart_x
https://hvinternals.blogspot.com
https://github.com/gerhart01

Kevin Breen:  Symbol Generator & Public ISF Server, Cobalt Strike Plugin, Rich Header Plugin, and LastPass Credential Recovery Plugin

This submission includes a number of components that can help analysts with modern investigations. The submission includes the following 3 plugins that bring new or updated functionality to Volatility 3:

Password Managers: LastPass is a widely used password manager and thus provides a highly valuable forensics target. This submission ports a popular Volatility 2 plugin for extracting LastPass credentials that were stored in memory at the time of acquisition.

 

Rich Header Plugin: A common technique during investigations is to try and identify masquerading processes running on suspected systems. This plugin extracts the Rich header from PE files compiled with Visual Studio which can help identify masquerading processes or aid in wider threat hunting or incident response investigations.

 

Cobalt Strike Plugin: Cobalt Strike is one of the most popular frameworks used by modern attackers and is frequently encountered during investigations. This plugin scans processes for signs of a Cobalt Strike configuration block and provides the ability to extract relevant configuration information.  

In addition to the aforementioned plugins, the submission also provides tools to reduce the hurdles some people experience when analyzing Linux memory samples: 

A Linux symbol server with currently over 1000 Volatility 3 ISF symbol files: The server can be provided to Volatility 3 as a remote symbol server and, if a sample has a matching banner, it can automatically use the associated symbols for analysis. Individual symbol files can also be searched for either by banner or kernel name.


If a symbol file does not exist on the server, a separate Symbol Maker tool can be used to create a symbol file.  By specifying a supported distribution and an optional kernel, the tool will download the necessary files and use dwarf2json to create a symbol file that can be used with Volatility 3. The tool currently supports Ubuntu (Main, AWS, Azure and GCP Variants) and Debian (Main, AWS).

Related References:

https://twitter.com/kevthehermit
https://github.com/kevthehermit/volatility_plugins/blob/main/vol3/passwordmanagers/passwordmanagers.py
https://github.com/Immersive-Labs-Sec/volatility_plugins/tree/main/richheader
https://github.com/Immersive-Labs-Sec/volatility_plugins/tree/main/cobaltstrike
https://isf-server.techanarchy.net
https://github.com/kevthehermit/volatility_symbols

Leonardo Dias da Silva: MultiYara

Many investigators often use YARA to help detect suspicious activity in memory samples.  This submission was intended to help investigators optimize and automate their investigation workflows by making it easier to pull down updated rules from remote locations and leverage multiple YARA rules. 

Related References:

https://www.linkedin.com/in/leonardo-dias-silva

MoonGyu Lee, JeongToon Kang, HyeonDeok Jeongm JunSung Park, Mintaek Lim (BoB Tracer of Coin): CryptoScan

Cryptocurrency is becoming increasingly important during digital investigations ,and there aren’t many forensics tools focused on extracting cryptocurrency artifacts.  In Korea, malicious actors are leveraging hardware wallets to bypass government-required authentication and gain anonymity. This submission is a plugin to detect and extract cryptocurrency transaction records and artifacts related to hardware wallet usage.  In particular, their research explores the Ledger Nano and Trezor One hardware wallets.  By interfacing with several cryptocurrency websites, the plugin can also be used to support investigations related to tracking cryptocurrency transactions.

Related References:

https://github.com/BoB10th-BTC/CryptoScan/blob/master/cryptoscan.py


Here are some additional resources for previous contests and community-driven plugins:

Volatility Foundation Contest Home Page:  http://www.volatilityfoundation.org/contest

Volatility 2020 Plugin Contest Results: https://www.volatilityfoundation.org/2020
Volatility 2019 Plugin Contest Results: https://www.volatilityfoundation.org/2019
Volatility 2018 Plugin Contest Results: https://www.volatilityfoundation.org/2018
Volatility 2017 Plugin Contest Results: http://www.volatilityfoundation.org/2017
Volatility 2016 Plugin Contest Results: http://www.volatilityfoundation.org/2016 
Volatility 2015 Plugin Contest Results: http://www.volatilityfoundation.org/2015
Volatility 2014 Plugin Contest Results: http://www.volatilityfoundation.org/2014-cjpn
Volatility 2013 Plugin Contest Results: http://www.volatilityfoundation.org/2013-c19yz

Volatility Community GitHub Repository: https://github.com/volatilityfoundation/community3 

Tuesday, January 18, 2022

Malware and Memory Forensics Training in 2022!

Over the last few months, we have received many questions about when our Malware and Memory Forensics training would return to in-person learning. Given that a new year is nearly here, and the rate of inquiries has continued to increase, we wanted to document our plans going forward in a publicly available blog post, as opposed to only fielding questions individually.

Virtual Course Remains Available


We would like to start by saying that our course is currently available in virtual format to students across the globe. We announced this availability earlier this year, and since then have had many students successfully complete the course. 

Our online course is self-paced and includes the full material (pre-recorded lectures, copies of the slides, labs, lab guide, etc.) given and presented in the normal 5-day course. Students also have direct access to the instructors through a private Slack channel on the Volatility Foundation’s Slack server. The self-paced format of the course has received very positive feedback, particularly as students have benefited from being able to message and screen share with instructors for help and have the ability to re-listen to lectures to reinforce learning:
"The class is one of the most technical courses I've taken. The Labs provided real world examples and make you think of various aspects of incident response (Network, Disk, File, Memory Forensics). I would recommend this class for incident responders, defenders, and those looking to get a better understanding of memory forensics. I've taken other Level 600/Advanced classes from other vendors and this is right up there with the content and quality." ~ Carlos M.
"I don't say it lightly: this is the best course I've ever taken. The instructors are incredibly fast (and helpful) to answer any questions about the subject, be it directly related to a module or on a real life scenario. It's also not about running plugins blindly. The course has taught me priceless information on Windows Internals, why certain suspicious activities in memory are suspicious in the first place and, best of all, showed me a structured analysis framework I can apply to all my future investigations." ~ Alexandre S.
"I have waited five years to finally attend, participate in and complete this excellent course. I wasn't disappointed. Very relevant, very in depth and has left with me with the skills and enthusiasm to seek out and explore more." ~ Matthew K.

Plans for Public, In-Person Training


As for in-person, public trainings, we are currently evaluating offering these in the Summer or Fall of 2022. We decided against hosting the training in Spring 2022 given the ongoing uncertainty surrounding the pandemic, including restrictions on travel from governments and companies, as well as constantly changing local regulations—particularly in cities we have historically held training events. Our aim is to return to in-person training as soon as the uncertainty clears and as the pandemic allows. 

Private, In-Person Training Beginning in 2022


We will be available for private trainings starting in 2022 within the United States. At this time, this seems the most reasonable option given that a single organization would control the pandemic-related parameters of the training. If your company is interested in a private, in-person training, please contact us. Private trainings can be customized, including modifying the number of days or focusing the course on the areas most critical to the particular organization’s success and goals. 

Keep in Touch!


We hope this update addresses any questions you may have, but if not then please let us know. We also hope to be back presenting at conferences in-person next year along with being able to host our training at public events. 

-- The Volatility Team

Friday, October 15, 2021

Memory Forensics R&D Illustrated: Detecting Mimikatz's Skeleton Key Attack

In this blog post, we are going to walk you through the research and development process that leads to new and powerful memory analysis capabilities. We are often asked about what this workflow looks like, and how the abuse of an API by malware or a new code injection technique can be successfully uncovered by a Volatility plugin. To showcase this process, we are going to analyze the Skeleton Key feature of Mimikatz, and then develop a brand-new Volatility 3 plugin that can successfully detect this backdoor technique across memory samples. While Volexity Volcano customers have had this capability, we wanted to contribute this back to the Volatility community, since there was no publicly available plugin. This post will also reveal a number of entirely new features.

To reach this goal, we will first study the relevant Mimikatz source code; then we will reverse engineer the API that Mimikatz uses to locate its victim data structure; and then we will write a plugin that can replicate this search and look for signs of tampering. As you will see shortly, the new Skeleton Key detection plugin is fully documented and shows how to perform a wide range of tasks using the APIs of Volatility 3. 

Our hope with this blog post is to inspire more members of the community to challenge themselves to develop their own new capabilities, and to experience what real-world malware and operating systems investigations entail. If you find this work interesting and decide to develop your own plugin(s), please consider submitting them to our 2021 Volatility Plugin Contest and take a chance at winning several prizes, including cash or a free spot in our popular Malware and Memory Forensics training

Skeleton Key Background


The Skeleton Key technique was first detected in the wild by the DFIR team at SecureWorks. Their blog post walks through the steps taken by the malware sample they uncovered. They also worked with Microsoft on a follow-up paper about the attack type and its variations. The implementation in Mimikatz is very similar to the one described in their research. 

The idea behind the Skeleton Key technique is to backdoor the authentication subsystem of Windows Active Directory domain controllers. This is accomplished by injecting code into the running lsass.exe process, and then hooking the routines used when verifying a domain account’s credentials. With the hooks in place, attackers are able to authenticate as any valid user in an AD domain by using a hard-coded password (termed the Skeleton Key by SecureWorks). 

The ability of attackers to log in as any user makes several traditional incident remediation procedures largely ineffective. As an example, it is very common during incidents to temporarily disable accounts that attackers are/were using or to at least force a password reset of these accounts. When a Skeleton Key is active, these procedures are not helpful, since any account can be used. This problem is also compounded by the fact that all user accounts in a domain could potentially be abused by attackers, and a significant amount of log review (assuming logs are available) is necessary to trace the abuse of accounts and any associated lateral movement. This ability to authenticate as any user to any system is incredibly powerful and significantly expands the scope of DFIR engagements. 

Analyzing the Skeleton Key Capability of Mimikatz 


Activating the Skeleton Key attack of Mimikatz requires using its misc::skeleton command after running the usual privilege::debug command. There are many great blog posts that document this process by showing the related Mimikatz output and other related information, such as here, here, and here. Cycraft also documented malware from the Chimera APT group that used a significant amount of code from misc::skeleton to implement its own Skeleton Key attack. The end result of this command is a Skeleton Key attack being active on the system; the attacker is able to authenticate with the malware-controlled credentials. 

Running the misc::skeleton command will lead to the kuhl_m_misc_skeleton function being called inside the active Mimikatz instance. This function is responsible for patching the needed code and data inside the lsass.exe process to make the Skeleton Key active. The first steps in this process are shown in the following image:


In this code, Mimikatz first gets the process ID of lsass.exe and stores it in the processId variable. Next, it calls OpenProcess to obtain a handle to the lsass.exe process. This handle gives the ability to read and write the memory of the lsass.exe process from the calling process. Mimikatz then calls kull_m_memory_open, which is an internal Mimikatz function that stores the handle for later use. 

After Mimikatz is able to read and write memory of the lsass.exe process, it then searches for the Kerberos-Newer-Keys string in memory so that it can find the data structure related to AES-based authentication. It then manipulates this structure so that authentication is downgraded to the weaker RC4 without the use of a salt. Note that this is the same approach described in the previously linked Cycraft report. Older reports, such as the one from Microsoft, describe how malware can also achieve the same result by hooking the SamIRetrieveMultiplePrimaryCredentials function and forcing it return an error when the Kerberos-Newer-Keys package is used. The end result of both methods is the same: all authentication attempts to an infected domain controller will use the weaker RC4 algorithm.

After downgrading the domain controller to RC4, Mimikatz will then attempt to locate and patch the data structure that handles RC4-based authentication. The following image shows the beginning of this code:

















First, the module information is gathered for the “cryptdll.dll” module loaded within the lsass.exe process. This module is responsible for implementing the different encryption packages, which are also known as systems. Next, the address of where cryptdll.dll is loaded within the Mimikatz process is gathered by calling GetModuleHandle. 

The undocumented CDLocateCSystem function is then called with an argument of KERB_ETYPE_RC4_HMAC_NT and the address to store the resulting lookup (&pCrypt). CDLocateCSystem determines the address of the data structure that handles the given (KERB_ETYPE_RC4_HMAC_NT) authentication system and copies its contents into the passed-in pCrypt address. In this instance, it will be for the system that implements RC4-based authentication, and it will contain the information shown below:



The actual data structure definition for this type is not documented by Microsoft, so the above image is directly from the Mimikatz source code. The arrows point to the members relevant to our plugin, which include the encryption type; the function pointers for the initialization, encryption, decryption, and finish operation handlers; and the pointer to the string name of the system.

After finding the _KERB_ECRYPT instance for RC4 through the use of CDLocateCSystem, Mimikatz then hooks the legitimate initialization (Initialize) and decryption (Decrypt) members of the structure. These hooks point the handlers to Mimikatz’s malicious handlers (kuhl_misc_skeleton_rc4_init and kuhl_misc_skeleton_rc4_init_decrypt) instead of the legitimate ones. The malicious handlers are injected into the address space of lsass.exe through the use of the WriteProcessMemory function. Combined, these malicious handlers are what implement the Skeleton Key attack, as they give Mimikatz control over all future authentication attempts to the infected domain controller.

Devising a Detection Strategy


Now that we understand how Mimikatz implements its attack—forcing a downgrade to RC4 followed by hooking the RC4 initialization and decryption routines—we can devise a strategy to detect the attack in memory.

We could start by attempting to detect the RC4 downgrade, but this has a few limitations. First, the string needed to find this data structure (Kerberos-Newer-Keys) is zeroed out as part of the attack, removing the possibility of a scanning-based approach to finding it. Second, attempting to detect that this string has been zeroed out would lead to many false positives due to paging of data out to disk, as well as the possibility of the page holding the string being smeared. Third, there are other methods to force a downgrade to RC4 without directly altering this string (as discussed in earlier references), meaning several approaches would be needed to completely detect it. Finally, finding proof of the downgrade only gives a clue that a Skeleton Key attack might have been performed, but it does not offer direct evidence. 

On the other hand, by examining the RC4 data structure directly, we can inspect the handlers for the initialization and decryption routines and determine if they were altered at runtime. This not only definitively tells us if a Skeleton Key attack occurred, but it also tells us exactly where the malicious handlers are inside of the infected lsass.exe process. Given that this approach gives direct evidence of the attack, as well as directly points out the malicious code, inspecting these handlers was chosen as the detection method for our plugin.

Reverse Engineering CDLocateCSystem


Before we can locate the handlers to then verify them, we need to be able to find the RC4 data structure in a repeatable and consistent manner. As shown previously, Mimikatz locates the address of the RC4 data structure by calling CDLocateCSystem. This tells us that if we can replicate the algorithm of CDLocateCSystem—or at least build an algorithm that is equal—we can reliably locate the RC4 structure to then verify its handlers. 

Since cryptdll.dll contains the CDLocateCSystem function implementation and is closed source, we will need to reverse engineer the function to determine its algorithm. As you will see next, this function is pretty simple, so do not panic if you have never reverse engineered before; the concept will be straightforward.

The following images show the IDA Pro decompiler and graph view of CDLocateCSystem:





As seen above, the function is pretty small and simple. It begins (the first instruction of CDLocateCSystem in the disassembly view) by copying the current value of cCSystems global variable into the r8d register, which is an alias for the lower 32 bits of 64-bit r8 register. It then tests (CDLocateCSystem+22) if the value is zero and bails with an error (+27) if it is. If the value is anything but zero, then it moves to basic block, starting at offset +9. This basic block begins by decrementing the r8d register (+9). This code pattern of storing a variable, checking if it is zero, and then decrementing the value tells us that this is likely a counter for looping (iterating) through a data structure. Looking ahead, the red line leading from +20 back to +22 in the graph confirms this, as the code at +22 will be evaluated every time the basic block starting at +9 fails to exit. This is exactly how loops look in IDA Pro and other basic block graphing tools. 

Further studying the basic block starting at +9, we see the address of the CSystems global variable copied into the r9 register. Next (+13), the value in r8d is copied into eax, and then rax is shifted left by 7
(+16), which is the same thing as being multiplied by 128 (2 to the 7th power). This computed value is then stored into r9, and the data r9 points to is compared with the value in ecx (+1D). If this comparison matches, then the function returns. Otherwise, the flow starting at +9 repeats. 

Breaking this down, the code is using the current value of r8d multiplied by 128 (shifted left by 7) as an index into CSystems. This is exactly what iterating through an array looks like. As each array element is stored contiguously in memory, by knowing the size and count, you can successfully locate each element. This understanding of the code now tells us two things: 
  1. cCSystems holds the number of elements in CSystems. 
  2. The size of each CSystems element is 128 bytes.

For the basic block at +9, the only remaining parts to understand are which values are being compared at +1D and the purpose of that comparison. Since the loop breaks dependent on that comparison, it is likely critical to the function’s overall purpose. Looking at the two values, [r9] and ecx, we know a few things. First, the comparison will be comparing two 32-bit values, as that is the size of ecx, which is the lower 32 bits of rcx. Second, the brackets around r9 mean to treat the value of r9 as an address in memory and then retrieve the value at that address, which is known as dereferencing an address (pointer). From our previous discussion, we know that r9 holds the address of the current CSystems element being inspected. Dereferencing it as [r9] is equivalent to dereferencing [r9+0], which tells us that the first 4 bytes (32 bits) of the referenced structure are being accessed. 

As for ecx, the instruction at +1D is the first time ecx (or any of rcx) is referenced. This means the value must have been set before the function was called. Consulting the Microsoft documentation on function-calling conventions, we see that the rcx register is used on 64-bit systems to store the first parameter sent to a function. Earlier, when we examined how Mimikatz called CDLocateCSystem, we noted that the first argument was the KERB_ETYPE_RC4_HMAC_NT constant, which is defined in NTSecAPI.h of the Windows SDK as 0x17 hex (23 decimal). This means that CDLocateCSystem will be searching for an element of CSystems that has 0x17 (23) as the first integer. 

Looking at the end of the function (+2E -> +33), we see that r9 is stored into the address pointed to by rdx; the previous Microsoft documentation tells us rdx stores the second parameter to a function. We know for CDLocateCSystem that this is the address of where the calling code (Mimikatz) wants Windows to store the address of the requested authentication system (RC4).

Seeing that the address of the CSystems element found in the loop is directly returned to the caller tells us that the data structure returned is also of type _KERB_ECRYPT, since we know that is the type of the second parameter to CDLocateCSystem. This then tells us that the integer at offset 0, that is compared in the loop, is actually the EncryptionType member of structure. This makes sense, since it holds the integer value for the particular authentication system type. It also means that the elements in CSystems are the ones actually used by Windows during the authentication process, since these are the ones directly targeted by Skeleton Key attacks.

In summary, reverse engineering has showed us that the active RC4 authentication system structure can be located by enumerating CSystems and then looking for the element that has an EncryptionType of 0x17 (23). This precisely matches how CDLocateCSystem uses its first parameter to determine which element of the CSystems array to return to the caller. It also tells us that the type of each element is KERB_ECRYPT, which is very handy since we already have the definition for this type.

Reverse Engineering the RC4 Structure Origin


After learning how CDLocateCSystem operated, the next analysis step taken was to determine if the RC4 structure inside the CSystems array could be found directly. While enumerating the array is not difficult nor time consuming, in memory forensics research we aim to find the most direct path to data to avoid analysis issues that can be caused by smear. 

To begin this analysis, we wanted to determine how elements of CSystems were registered, with particular interest in the RC4 system. Examining cross-references (meaning, finding code that references), CSystems showed only a few locations inside of cryptdll.dll. Of these, the CDRegisterCSystem function sounded the most promising, as it would hopefully lead us to RC4 being registered. 

The following image shows the decompiled view of this function:



As can be seen, this is a pretty simple function that first (line 6) checks against the maximum number of registered systems (0x18), and then bails if already at the maximum. Next, the function determines the offset into CSystems (line 9) by using cCSystems shifted by 7. This matches our understanding of cCSystems and the shifting by 7 from earlier. The function then simply copies in the values from the passed in data structure (a1) into the correct offsets of CSystems. In summary, whatever values are in the system being registered are copied separately inside of CSystems, duplicating them in memory.

Following cross-references to CDRegisterCSystem leads us to many references inside of LibAttach; a decompiled view is shown below:



This function is exactly what we were looking for, as we can see all the different systems being registered. We also see our system of interest, csRC4_HMAC, being registered on line 5. If we examine the data at this address, we can verify this with seeing 0x17 (23) as the first integer. We learned earlier that this is the EncryptionType targeted by Mimikatz.



As seen above, not only is the 0x17 (23) present at the first offset, but a little further down we also see the string defined for the system (RSADSI RC4-HMAC), as well as the handlers for events the system must support. Looking at the list of functions, we find the legitimate handlers for the initialization (rc4HmacInitialize) and decryption (rc4HmacDecrypt) routines that Mimikatz targets. This gives us the specific symbol names that should correspond to the handlers we find inside of analyzed memory samples.

In summary, this reverse-engineering effort to find the origin structure led us to two import conclusions. First, even though we know the symbol name of the static RC4 structure (csRC4_HMAC), we cannot analyze this directly, as a copy of its values will be placed inside of CSystems. This means we will still need to enumerate CSystems to get the “active” values, but it also means that we can potentially choose to leverage the duplicate, original data in our plugin. Second, by knowing the symbol names of the legitimate initialization and decryption handlers, we can make the sanity checks performed by our plugins as specific as possible.

With these two reversing efforts complete, we can now start to develop our plugin!

Designing the windows.skeleton_key_check Plugin


Our previous analysis gave us all the information we need to design and implement our plugin; we saw exactly how the operating system retrieves our desired data structure. As a direct approach, this would include the following steps:
  1. Find the address of CSystems
  2. Walk each element to find the active RC4 system
  3. Compare its initialization and decryption handlers to the known-good symbols

After the handlers are processed, the plugin would then report whether the handler’s value is legitimate or if a Skeleton Key attack has been performed. 

Creating a New Plugin


To start, we must create a base Volatility 3 plugin that is capable of processing Windows samples. A major goal of Volatility 3 was to have significant and always-up-to-date documentation for both users and developers. This documentation is stored on the Volatility 3 page of readthedocs. There is also a section specifically on writing a basic plugin here

At a high level, all plugins must define their requirements, a run method, and a generator method. The run method executes first and calls the generator method to create the data sets that will be displayed on the terminal (or output in whatever format other interfaces support). For more information, please see the documentation above.

For our Skeleton Key plugin, we use the basic starting form to then implement the steps listed previously. Note that the plugin being described in this blog post is already available in Volatility 3 here. Since line numbers change after each new commit, we instead will be referencing portions of the plugin by the function name. Also, we will be showing screenshots of code portions being discussed with the line numbers starting at 1. This will guide the discussion in a consistent manner. 

Implementation - Writing the run Function


The run function is called first when a plugin’s execution begins. The expected return value is a TreeGrid that the calling user interface will then display for the analyst. The following image shows the run function, along with the process filter from our Skeleton Key plugin:



On line 12, the return statement begins with the construction of the required TreeGrid instance. The first parameter to the TreeGrid constructor is the list of columns that the plugin will display. Each column is specified with its name and type. For this plugin, we have chosen to display the process ID and name of analyzed lsass.exe instances; whether or not a Skeleton Key attack was found; and the addresses of the initialization and decryption handlers. Note that the handlers are listed by their address in memory, which Volatility 3 will automatically print in hexadecimal due to the format_hints.Hex specifier. This is similar to the [addrpad] specifier of Volatility 2.

Next, the generator function is called. For plugins that operate on data not made available by another plugin, the generator function will be called with no arguments. For Skeleton Key, since we only want to analyze lsass.exe processes, we can leverage list_processes to perform the filtering for us. This filtering occurs through the use of the filter_func argument, which specifies a callback that evaluates if a process object should be yielded to the caller. Our filtering function, lsassproc_filter, is very simple; it only needs to evaluate if the process name is lsass.exe. 

Implementation – Leveraging PDBs

 
Our reverse-engineering effort showed us that four symbols—cSystems, cCSystems, rc4HmacInitialize, rc4HmacDecrypt—hold the key data we need to write a complete plugin. Luckily, one of the new features of Volatility 3 is the ability to automatically download and incorporate PDB (symbol) files into the analysis flow of plugins. This is accomplished by locating the PE file (.exe, .dll, .sys) of interest and parsing it with the PDB utility API. Since cryptdll.dll holds the symbols our plugins need, the first step is to find the DLL within the address of lsass.exe:










The above image shows that _find_cryptdll—a function that receives the process object for lsass.exe—iterates through its memory regions (line 12), retrieves the filename for the current region (line 13), and checks for the file of interest (13-15). Once cryptdll.dll is found, its base address and size are returned (16-17). 

Once cryptdll.dll has been located, its information can then be passed to the PDB utility APIs:



As shown, calling into the PDB API is straightforward, but this actually triggers quite a bit of activity inside the core of Volatility 3. First, the memory range specified for the PE file is scanned to find its GUID, which is unique identifier for the file. Next, the local Volatility cache is checked to see if the PDB for this GUID has already been downloaded and processed during previous plugin runs. If so, then the cached file is parsed and returned to the caller.

If the GUID is not in the cache, then Volatility will attempt to download the PDB file from the Microsoft symbol server.  If successful, then the PDB will be parsed, converted to Volatility’s symbol table format, and stored within the cache. 

Assuming the PDB is successfully downloaded and parsed, then our plugin has direct access to the offsets of the needed symbols within the particular version of cryptdll.dll. This allows us to trivially find their values within a particular memory sample:



The code shown gathers the runtime address for each of the four desired symbols. For the handlers, we only need their address in memory to compare to the ones in the active RC4 system. For cCSystems, we treat it separately, as we do not want processing to fail simply because the page holding the count is unavailable. 

We also treat CSystems separately, as we need to construct an array type to cleanly enumerate its elements. Constructing this object requires not only the address of where CSystems is in memory, but also the structure definition for the array elements. Unlike the PDB file for the kernel, which includes both symbol offsets and type information, the PDB file for cryptdll.dll only includes the symbol offsets. This means we need to manually inform Volatility of the data structure layout. This is performed in Volatility 3 by creating a JSON file that describes the data structure(s) a plugin requires. You can view this file for the _KERB_ECRYPT structure here, which was based on the definition from Mimikatz discussed earlier.

Once the array is constructed, it can then be enumerated as shown below:




Volatility has built-in support for enumerating arrays, so the for loop will walk each element, creating the csystem variable as the _KERB_ECRYPT type. Before processing an element, it is checked for being valid (mapped) into the process address space (lines 2-3). Next, the EncryptionType value is compared with our type of interest (lines 6-7). To determine if a Skeleton Key is present, we compare the Initialize and Decrypt members of the system found in memory to the expected values from the PDB file. If either of these have been modified, then a Skeleton Key attack has occurred, or at a minimum, a modification has occurred that an analyst would want to know about. 

With all of the values computed, displaying the results to the analyst requires just a simple yield of the data. This can be seen in lines 13-17 and will result in the process name and PID, presence of a Skeleton Key, and handler addresses being displayed. This immediately informs the analyst if a Skeleton Key was found, and if so, where the malicious handler values are in memory. 

The following image shows a run of our new plugin against an infected memory sample:


Adding Resiliency to windows.skeleton_key_check


So far, our plugin is able to successfully detect Skeleton Key attacks by leveraging the cryptdll.dll PDB file to determine where our four symbols of interest are located in memory. Unfortunately, real-world memory forensics is not always this straightforward, and the data we would like may not be memory resident or it may be smeared. Thus, it is also advantageous to consider other approaches.

In the case of leveraging a PDB file for analysis, there are a few situations that could prevent us from determining which PDB file is needed for analysis, as well as obtaining that PDB file.
  1. The page containing cryptdll.dll’s GUID could be paged out or smeared. 
  2. The analysis system may be offline and unable to download the PDB file from Microsoft’s symbol server.
  3. Although rare, Microsoft has published corrupt/broken PDB files for modules shipped with stable versions of Windows.

In these situations, we would still like to be able to detect Skeleton Key attacks, but we need a different approach to gather the required data. 

Finding CSystems Without a PDB File


Using knowledge gained from previous work on the plugin, we know that the CDLocateCSystem function directly references two of the four symbols we need: CSystems and cCSystems.  This means that by performing static binary analysis of CDLocateCSystem, we should be able to determine the address of these symbols, since the function’s instructions will reference the addresses themselves. This is a common tactic in memory analysis and reverse engineering tasks to find symbols that are not exported or where a symbol file cannot be obtained. 

To attempt to find CDLocateCSystem without the use of a PDB file, we parse the export directory of cryptdll.dll, since it exports CDLocateCSystem by name. The following image shows how this is performed in Volatility 3:





First, a reference is obtained to the type information for PE files (lines 1-7). Next, a Volatility 3 PE file object is constructed starting at the base address of cryptdll.dll (lines 9-11). This object contains a number of convenience methods for accessing common data, such as the data directories. This is leveraged on line 15 to parse the export directory, and then loop through its exported symbols starting on line 22. The body of this loop then looks for CDLocateCSystem, and when found, attempts to read the bytes (opcodes of the instructions) from its location in memory. 

If these bytes can be read, then the _analyze_cdlocatecsystem function is called, which leverages capstone to perform the static disassembly necessary to locate both symbols. After locating them, it will construct the array object using the same method as described when the PDB file symbols were used. 

Assuming the export table and opcodes for CDLocationCSystem are present, this method will successfully find CSystems and allow us to locate the RC4 structure as we did previously. 

Finding rc4HmacInitialize and rc4HmacDecrypt


So far, we have been able to locate the RC4 structure without the PDB file. Unfortunately, there are no direct references to the legitimate initialize and decrypt handlers that we can leverage. This leaves us with two options. The first option is to verify the memory region holding the handlers, which will be discussed in this section. The second option is to attempt to scan for the values, which is discussed in the next section. Each has advantages and drawbacks, as we will discuss.

Each memory region within a process’s address space is tracked by a virtual address descriptor (VAD). Information in the VAD includes the starting and ending address of the region; the initial protection of the region; and the number of committed pages. For executables, such as lsass.exe and cryptdll.dll, one VAD will track all regions of the executable, including its code and data. Knowing this, we can check if the values of the initialization and decryption handlers are within the region for cryptdll.dll. This is shown in the following image:


This simple check ensures that the value of the handler is within the starting and ending range of the VAD for cryptdll.dll.  Although this check is not as precise as having the exact, legitimate values from the PDB file, this method still detects all forms of Skeleton Key attacks found in the wild, as they all allocate new VADs to hold the shellcode of the malicious handlers.

Note: Theoretically, an in-memory code cave could be used to place redirection stubs within cryptdll.dll and this check would be bypassed, but no malware—in the wild or proof-of-concept—has leveraged this approach. Furthermore, the PDB-based method and the one described in the next section would still detect these, rendering them not particularly stealthy. These types of attacks are also much less portable to differing operating system versions, which is one of the reasons they are uncommon in the real world.

Adding Scanning as a Last Resort to windows.skeleton_key_check


We currently have two methods to gather the data needed for Skeleton Key attacks: PDB files and export table analysis. As discussed previously, the PDB file method can be unavailable for a number of reasons, and unfortunately, the export table method can be as well. The most common reason for this is the PE header metadata being paged out or the page(s) holding the export table information are paged out. The end result is that we cannot use the export table to tell us directly where to look for our needed information.

In these situations, there is a long history of memory forensic tools scanning for the data they need. Since we have access to all pages that are present within a process’s address space, we can simply scan them in hopes of finding what we need. In the case of our Skeleton Key plugin, we were able to develop a highly effective and efficient scanner to meet our needs.

To begin, we used our knowledge that the data we need is contained within cryptdll.dll. This means we only have to scan a very small space (the size of the DLL). Second, as shown before, the layout of the active structure starts with the integer for the encryption type, which we know is 0x17 for RC4. Other research showed that the second member, BlockSize, had a value of 1 in all of our test samples. Using this knowledge, we developed a scanner based on Volatility 3’s scanning API:




The scanner is configured to look for an 8-byte pattern of 0x17 followed by 1 in little-endian integer format. It attempts to instantiate a _KERB_ECRYPT type at each address where this pattern is found. To strengthen the check, we also verify that the Encrypt and Finish members our potential structure reference addresses are inside of cryptdll.dll. Neither of these are targeted by Skeleton Key attacks and validating their values provides a strong check against false positives.

The following shows the output of our plugin when the scanning method is used:


Note that there are two lines of output. This occurs beause the scanner finds both the active version of the RC4 structure and the version that is statically compiled into the application. Having both outputs provides some advantages: there is direct visual confirmation that the active structure is hooked, and the statically compiled version reveals the addresses for the legitimate handlers, even without PDB usage.

Wrap Up


In this blog post, we have walked through the entire process for memory forensics research and development. We analyzed a target (Mimikatz’s Skeleton Key attack), analyzed the subsystem it abuses (the authentication systems managed by cryptdll.dll), and developed a new Volatility plugin that can automatically analyze this subsystem for abuse. This is a common workflow used to develop Volatility plugins.

If you find this type of research interesting, please consider developing a new plugin and submitting it to our Volatility Plugin Contest. Note that your submission does not have to be anywhere near as thorough as the plugin presented here; even submitting a new capability with just one of the discovery methods (PDB files, export analysis, scanning) used would be sufficient for an entry. We showed the full range here to display many of Volatility 3's new capabilities, but we certainly do not expect all plugins to meet this level of complexity. 

We hope you have enjoyed this post. If you have any questions or comments, please let us know. You can find us on Twitter (@volatility) and our Slack server.

-- The Volatility Team

Wednesday, August 11, 2021

The 9th Annual Volatility Plugin Contest!

The 9th annual 2021 Volatility Plugin Contest is now open! We will be accepting submissions until December 31, 2021.

Volatility Plugin Contest

As in previous years, the 2021 Volatility Plugin Contest encourages research and development in the field of memory analysis. Your submissions provide an opportunity to get industry-wide visibility for your work, put groundbreaking capabilities immediately into the hands of investigators, and contribute back to the open source forensics community. By building on top of Volatility 3, your contributions also help lay the foundation for the next generation of memory forensics! 


And while getting visibility for your work and contributing to this important open-source project are great reasons to participate, there's also this: Winners will receive over 6000 USD in cash prizes! 

For more information: 2021 Volatility Plugin Contest 

And if you are looking for inspiration, check out the 2020 contest results.

Acknowledgements

We would like to thank Volexity and our other sustaining donors for their continued support.


Friday, May 7, 2021

Highlighting Research from the Next Generation of Memory Forensics Practitioners

Nearly 2 years ago, we published a blog post about our collaboration with Dr. Golden G. Richard III at the Louisiana State University (LSU) Center for Computation and Technology (CCT). We are very happy to report that this collaboration is still going strong, has been a huge success, and has helped the Applied Cybersecurity Lab at LSU flourish. The students from LSU who have finished their studies are now making a real impact in our industry, and those currently pursuing their degrees are continuing to push the state of the art in memory forensics and malware detection. 

Given that another semester has just wrapped up, and that the LSU Office of Research recently published a detailed article about our collaboration, we decided that it was time for us to write our own acknowledgement of these students and their efforts. As you might imagine, many of the students have focused their research on memory forensics and its use in real-world DFIR workflows. These research efforts have been strongly focused on "gaps" in current analysis techniques; significant improvements of existing techniques; and efforts to classify and test the accuracy and reliability of existing tools. For the remainder of this post, we will highlight several of these efforts, as well as introduce the students involved.

Reliability of DFIR Tools

The efforts aimed at the reliability of DFIR tools began with the creation of systems for targeted fuzzing of DFIR frameworks. The first publication from this research was the Gaslight fuzzing architecture aimed at memory forensic frameworks. Gaslight was first presented at DFRWS 2017, and it showcased the ability to efficiently fuzz only the portions of a memory sample analyzed during common investigations. It successfully found numerous error conditions in Volatility 2 that have since been patched, making the framework more reliable in the face of smearing.

A second version of Gaslight was then developed that greatly improved performance and scalability. This was targeted at the Sleuthkit Framework and found several inputs that would cause the library and associated programs to crash or exhaust available resources. This was published in Computers and Security (COSE) in 2020 and served as the basis of Shravya Paruchuri's Master's thesis.

The final iteration of Gaslight involved modifying its architecture to support distributed and high performance processing. This effort was also aimed at Volatility 2 and found many plugin code paths that did not properly account for smear, and that were not successfully triggered during testing with the first architecture version. This project was performed in coordination with the High Performance Computing center at LSU, and their allowance of abundant HPC resources allowed for millions of plugin fuzzing runs. This work was performed by Arian Shahmirza and led to successful completion of her Master's Degree titled "High Performance Fuzz Testing of Memory Forensics Frameworks". 

Beyond fuzzing, there was also a major effort to automate testing the accuracy of memory analysis frameworks. Given that the data structures in memory samples are a constantly moving target due to new operating system and application versions, it is imperative that analysis tools are able to keep up with the rapid changes. This is currently an extremely time-consuming and manual process, so research was performed to automate the analysis and comparison of different module versions to provide for an efficient and tested workflow. Ryan Maggio led this effort, and it contributed to his successful Ph.D. defense. The paper describing this research was accepted at DFRWS 2021 and will be presented this summer. We are also very excited for Ryan's future in the DFIR industry, as he recently accepted an offer to join the research team at MIT's Lincoln Laboratory after graduation.

Bringing Emulation to Memory Forensics

The second portion of Ryan's Ph.D. work involved another major research effort performed by our team and the LSU students: bringing emulation to the forefront of memory analysis research. Existing methods for analyzing malicious code in memory, such as shellcode and API hook stubs, require an expert investigator to manually reverse engineer each code block. Given that an average modern memory sample has thousands of such hooks, most of which are benign, this is no longer a feasible approach. 

To alleviate this issue, research was performed that explored the use of emulation of code inside memory samples to generate automated decisions for commonly seen code blocks, stubs, and patterns. This was a group research effort and led to the creation of HookTracer, a system we built on top of Unicorn that integrates directly with Volatility. While Unicorn provides a bare emulator, HookTracer adds Windows-specific functionality to the emulation environment. This includes support for FS/GS access; per-process address spaces; recording of API calls and parameters for functions of interest; and the ability to record all basic blocks and VADs traversed by particular code paths. All of these features are directly accessible to Volatility plugins through the new HookTracer API.

This research and development of HookTracer led to several peer-reviewed publications, as well as contributed to several successful M.S. and Ph.D. defenses. The first of these was presented at DFRWS 2019 and focused on the automated analysis of API hooks. As shown in this paper, default Windows installations with no 3rd-party products and no malware present still have thousands of active API hooks to support backwards compatibility and other related features. Through the development of a new Volatility plugin that leveraged HookTracer's APIs, all of these benign hooks were successfully filtered, while malicious ones planted by malware were successfully reported.

The second effort focused on message hooks, which are often abused by malware to perform keylogging, copy/paste buffer snooping, and inject code into remote processes. To remove the burden of manual analysis of such hooks, a new HookTracer plugin was developed that could automatically determine if a message hook was legitimate or benign. Furthermore, for all Windows APIs called by such hooks, the plugin is able to report the parameters passed to functions of interest. The following picture shows this plugin in action against a malware sample from the infamous Turla group:


In the output, you can see that HookTracer has successfully emulated the malware's message hook. This includes revealing that the malware recorded the current timestamp, application window title, current key pressed, and current working directory. The malware then writes these to a file on disk (msimm.dat). In many ways, this aspect of HookTracer can be thought of as a sandbox-like execution environment for code in memory. The full details of this plugin and its analysis capabilities can be found in the research team's paper published in COSE in 2020.

There was also a research effort by Austin Sellers to automate the analysis of networking APIs called by memory resident code. Network-based IOCs, such as IP addresses and hostnames, are used throughout all phases of the DFIR workflow. Existing memory analysis techniques enhance this information by automatically determining which process(es) have communicated with the hosts/IPs of interest. These techniques then leave the investigator to manually find the code region(s) that actually perform the communication, which is a crucial task when the ability to decode encrypted packet data is needed or when the C2 protocol needs to be reverse engineered. Austin's work sought to automate the location of these code regions by finding the places where networking APIs are called and extracting the parameters to the APIs. This work was the foundation of his Master's thesis. Since graduation, Austin has worked with several of our team members at Volexity as a software engineer helping to build commercial memory analysis capabilities.

We also have a fourth effort related to HookTracer that is currently being finished and will soon be sent for publication. Given the double-blind nature of most academic security conferences and journals, we cannot say too much yet except that it will provide another great leap forward for the field. We expect a follow-on blog post once it is finished.

Finally, HookTracer was developed for Volatility 2 given that this project was started several years ago. As Volatility 3 stabilizes and approaches feature parity with Volatility 2, we plan to port HookTracer to the new version so that the entire community can build on it and benefit from its use.

Memory Analysis Gaps

Although the memory forensics field has seen substantial research efforts over the last decade, the sheer number of relevant operating systems, applications, and runtime environments means that many research efforts are still needed. The combined research group aimed to address several of the most interesting and relevant of these research "gaps".

This began with Nathan Lewis publishing a paper at DFRWS 2018 on his efforts to build memory analysis capabilities for Linux systems powered by version 1 of the Windows Subsystem for Linux (WSL).  Before this research, all of the WSL-controlled Linux processes, file descriptors, network activity, and other artifacts were not accessible in a structured manner. This led to Volatility plugins being 'blind' to WSL activity. Given the technical effort and usefulness of this research, Nathan's paper was chosen for Best Student Paper award at DFRWS.

There were also efforts to modernize userland analysis of macOS systems. The last major research of the userland runtimes for Swift and Objective-C was published in 2016 and has fallen behind modern versions. To update the support as well as add new analysis features, Ph.D student Modhuparna Manna worked on two major efforts. The first was the analysis of the macOS page queues, which "hide" many present pages in memory by marking them as invalid, even though they are actually in a given memory sample. Without the analysis of the queues, many pages in process memory are unavailable to analysis plugins and results are often very limited. Recovering these pages and making them accessible to Volatility plugins was the subject of a paper that the group published at DFRWS 2020. Modhu also updated the existing Volatility plugins for analysis of the macOS userland runtimes to support modern versions as well as recover several new artifact types. These are covered in a paper that has been accepted by the Digital Investigation journal and is awaiting publication. We will update this blog post with the link to the paper after publication.

Analysis of the Android userland runtimes was the focus of Sneha Sudhakaran's research, in collaboration with her co-advisor Aisha Ali-Gombe, a faculty member at Townson University. Given the popularity of Android devices, along with the amount of malware targeting the platform, this research is highly relevant to modern investigations. The results of this research were novel methods for analysis and recovery of Android application activity. There were several publications as a result of this effort, the first being AmpleDroid for analysis of Large Object Files. The second, DroidScraper, was published at the top-tier academic security conference RAID and focused on structured recovery of application data and artifacts. Sneha is currently working on a third related publication as she wraps up her Ph.D. studies.

Scholarships for Service

Before closing, we wanted to highlight that due to the efforts of Dr. Richard and his colleagues Dr. Ram, Dr. Sun, Dr. Mahmoud and Dr. Peng, LSU is now a part of the National Science Foundation's Scholarship for Service (SFS) program. The full details are on the corresponding LSU webpage, but briefly, this scholarship provides up to 3 years of full support to students. This support includes tuition; a very nice stipend ($25,000/year for undergrads and $34,000/year for graduate students); funding for professional training and certification; and internship opportunities. 

In exchange, students agree to work for a US government institution for the number of the years that the scholarship was used. Past examples of institutions students work for/with include 3-letter agencies, energy-related departments, and other high-profile organizations that operate major components of the country. These are jobs that would be of interest to many Computer Science graduates, and the SFS program provides a direct path to them.

If you know someone who may be interested in the SFS program, please see the link above. We are hoping that it will continue to attract motivated students to the LSU research group, and that we can continue to help mold the future practitioners and researchers of the DFIR industry. 

We also would like to note that no one on the Volatility Team has any involvement with selecting or screening students through this process. If you have any questions, the best place is the previously linked page.

Final Thoughts

In closing, we would again like to acknowledge the great amount of effort put forth by the students in the LSU research group. Please reach out to them if you have questions on their research, or if your organization may be hiring in the future. As a final note, we would also like to congratulate a current Master's student in the group, Raphaela Mettig, for excelling during the intern interview process at Tesla and being awarded a security team internship for this summer.

-- The Volatility Team

Tuesday, January 26, 2021

Malware and Memory Forensics Training Goes Virtual!


We are very excited to announce that our popular Malware and Memory Forensics with Volatility training is now available in a self-paced, online format!

Brought to you by members of the Volatility Team, this course gives you the opportunity to learn directly from the people behind the research and development of Volatility, and it offers you a chance to support our ongoing efforts.

The Course


Our course provides a deep examination of Windows internals, malware operations, attacker toolkits, DFIR workflows, and how memory forensics can be leveraged throughout all of your investigations. The end result of the lectures on these topics is a complete understanding of how memory analysis tools operate, along with the traces left behind by malicious actors and applications. You will gain knowledge that applies to all memory analysis investigations and frameworks, both now and in the future.

The Labs


You will get significant hands-on experience using Volatility against Windows, Linux, and MacOS samples during the training labs. These labs are mirrored directly to match our real-world investigations (1, 2, 3, 4, 5), and they are constantly updated as new threats emerge. After each lab, there is a pre-recorded walkthrough that shows you the precise steps taken to solve the lab as well as explains the rationale behind the workflow used. A written version of the walkthrough is also provided in the Lab Guide.

The Content


Signing up for the course provides you with 4 months of access to all material, including the pre-recorded lectures, demos, and lab walkthroughs. You will also be given a trial version of Surge Collect Pro, which provides reliable and secure memory acquisition across Windows, Linux, and Mac systems. For support, we have a dedicated channel on our Slack server and periodic office hours via Zoom; students can directly ask us questions about any portion of the course, with optional screen sharing. After the course ends, you may retain access to the written materials (Art of Memory Forensics, course slides, lab guide) and the course virtual machine. You are also welcome to stay in the student-only Slack channel and join our alumni-only mailing list.

For those wondering about Volatility 2 vs Volatility 3, our course currently uses Volatility 2 for demos and labs as it is the stable and fully featured version of The Volatility Framework. As mentioned previously, the skills learned in this course are transferable to any memory analysis framework – even those not based on Volatility. We plan to add training modules specific to Volatility 3 slowly over time as it stabilizes and further approaches feature- and plugin-parity with Volatility 2. In general, if you know how to use Volatility 2, then using Volatility 3 will be very simple in the future. As students of this course, you will be the first to gain access to Volatility 3 training material as it is released.

Sign Up!


You can request access to the course here. We provide discounts for military, law enforcement, and groups of students from the same organization. We also provide significant discounts to course alumni who wish to take the course again, as well as to full-time college students studying in a related field. Please inquire about these discounts if you meet the requirements. We can also accommodate private sessions for large group trainings.

We would like to thank the memory forensics community for their years of continued support. We are excited that our course is now available 24/7/365 for students around the world!

- The Volatility Team