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Karma Ransomware | An Emerging Threat With A Hint of Nemty Pedigree

18 October 2021 at 16:43

Karma is a relatively new ransomware threat actor, having first been observed in June of 2021. The group has targeted numerous organizations across different industries. Reports of a group with the same name from 2016 are not related to the actors currently using the name. An initial technical analysis of a single sample related to Karma was published by researchers from Cyble in August.

In this post, we take a deeper dive, focusing on the evolution of Karma through multiple versions of the malware appearing through June 2021. In addition, we explore the links between Karma and other well known malware families such as NEMTY and JSWorm and offer an expanded list of technical indicators for threat hunters and defenders.

Initial Sample Analysis

Karma’s development has been fairly rapid and regular with updated variants and improvements, oftentimes building multiple versions on the same day. The first few Karma samples our team observed were:

Sample 1: d9ede4f71e26f4ccd1cb96ae9e7a4f625f8b97c9
Sample 2: a9367f36c1d2d0eb179fd27814a7ab2deba70197
Sample 3: 9c733872f22c79b35c0e12fa93509d0326c3ec7f

Sample 1 was compiled on 18th, June 2021 and Samples 2 and 3 the following day on the 19th, a few minutes apart. Basic configuration between these samples is similar, though there are some slight differences such as PDB paths.

After Sample 1, we see more of the core features appear, including the writing of the ransom note. Upon execution, these payloads would enumerate all local drives (A to Z) , and encrypt files where possible.

Further hunting revealed a number of other related samples all compiled within a few days of each other. The following table illustrates compilation timestamps and payload size across versions of Karma compiled in a single week. Note how the payload size decreases as the authors’ iterate.

Ransom Note is not Created in Sample 1.

Also, the list of excluded extensions is somewhat larger in Sample 1 than in both Samples 2 and 3, and the list of extensions is further reduced from Sample 5 onwards to only exclude “.exe”, “.ini”, “.dll”, “.url” and “.lnk”.

The list of excluded extensions is reduced as the malware authors iterate

Encryption Details

From Sample 2 onwards, the malware calls CreateIoCompletionPort, which is used for communication between the main thread and a sub thread(s) handling the encryption process. This specific call is key in managing efficiency of the encryption process (parallelization in this case).

Individual files are encrypted by way of a random Chacha20 key. Once files are encrypted, the malware will encrypt the random Chacha20 key with the public ECC key and embed it in the encrypted file.

Chacha Encryption

Across Samples 2 to 5, the author removed the CreateIoCompletionPort call, instead opting to create a new thread to manage enumeration and encryption per drive. We also note the “KARMA” mutex created to prevent the malware from running more than once. Ransom note names have also been updated to “KARMA-ENCRYPTED.txt”.

Diving in deeper, some samples show that the ChaCha20 algorithm has been swapped out for Salsa20. The asymmetric algorithm (for ECC) has been swapped from Secp256k1 to Sect233r1. Some updates around execution began to appear during this time as well, such as support for command line parameters.

A few changes were noted in Samples 6 and 7. The main difference is the newly included background image. The file “background.jpg” is written to %TEMP% and set as the Desktop image/wallpaper for the logged in user.

Desktop image change and message

Malware Similarity Analysis

From our analysis, we see similarities between JSWorm and the associated permutations of that ransomware family such as NEMTY, Nefilim, and GangBang. Specifically, the Karma code analyzed bears close similarity to the GangBang or Milihpen variants that appeared around January 2021.

Some high-level similarities are visible in the configurations.

We can see deeper relationships when we conduct a bindiff on Karma and GangBang samples. The following image shows how similar the main() functions are:

The main() function & argument processing in Gangbang (left) and Karma

Victim Communication

The main body of the ransom note text hasn’t changed since the first sample and still contains mistakes. The ransom notes are base64-encoded in the binary and dropped on the victim machine with the filename “KARMA-AGREE.txt” or, in later samples, “KARMA-ENCRYPTED.txt”.

Your network has been breached by Karma ransomware group.
We have extracted valuable or sensitive data from your network and encrypted the data on your systems.
Decryption is only possible with a private key that only we posses.
Our group's only aim is to financially benefit from our brief acquaintance,this is a guarantee that we will do what we promise.
Scamming is just bad for business in this line of work.
Contact us to negotiate the terms of reversing the damage we have done and deleting the data we have downloaded.
We advise you not to use any data recovery tools without leaving copies of the initial encrypted file.
You are risking irreversibly damaging the file by doing this.
If we are not contacted or if we do not reach an agreement we will leak your data to journalists and publish it on our website.

If a ransom is payed we will provide the decryption key and proof that we deleted you data.
When you contact us we will provide you proof that we can decrypt your files and that we have downloaded your data.

How to contact us:

{[email protected]}
{[email protected]}
{[email protected]}

Each sample observed offers three contact emails, one for each of the mail providers onionmail, tutanota, and protonmail. In each sample, the contact emails are unique, suggesting they are specific communication channels per victim. The notes contain no other unique ID or victim identifier as sometimes seen in notes used by other ransomware groups.

In common with other operators, however, the Karma ransom demand threatens to leak victim data if the victim does not pay. The address of a common leaks site where the data will be published is also given in the note. This website page appears to have been authored in May 2021 using WordPress.

The Karma Ransomware Group’s Onion Page


Karma is a young and hungry ransomware operation. They are aggressive in their targeting, and show no reluctance in following through with their threats. The apparent similarities to the JSWorm family are also highly notable as it could be an indicator of the group being more than they appear. The rapid iteration over recent months suggests the actor is investing in development and aims to be around for the foreseeable future. SentinelLabs continues to follow and analyze the development of Karma ransomware.

Indicators of Compromise

Karma Ransomware

Sample 1: d9ede4f71e26f4ccd1cb96ae9e7a4f625f8b97c9
Sample 2: a9367f36c1d2d0eb179fd27814a7ab2deba70197
Sample 3: 9c733872f22c79b35c0e12fa93509d0326c3ec7f
Sample 4: c4cd4da94a2a1130c0b9b1bf05552e06312fbd14
Sample 5: bb088c5bcd5001554d28442bbdb144b90b163cc5
Sample 6: 5ff1cd5b07e6c78ed7311b9c43ffaa589208c60b
Sample 7: 08f1ef785d59b4822811efbc06a94df16b72fea3
Sample 8: b396affd40f38c5be6ec2fc18550bbfc913fc7ea

Gangbang Sample 

Karma Desktop image

Victim Blog (TOR)

Ransom Note Email Addresses
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]

T1485 Data Destruction
T1486 Data Encrypted for Impact
T1012 Query Registry
T1082 System Information Discovery
T1120 Peripheral Device Discovery
T1204 User Execution
T1204.002 User Execution: Malicious File

AlphaGolang | A Step-by-Step Go Malware Reversing Methodology for IDA Pro

21 October 2021 at 14:12

The increasing popularity of Go as a language for malware development is forcing more reverse engineers to come to terms with the perceived difficulties of analyzing these gargantuan binaries. The language offers great benefits for malware developers: portability of statically-linked dependencies, speed of simple concurrency, and ease of cross-compilation. On the other hand, for analysts, it’s meant learning the inadequacies of our tooling and contending with a foreign programming paradigm. While our tooling has generally improved, the perception that Go binaries are difficult to analyze remains. In an attempt to further dispel that myth, we’ve set out to share a series of scripts that simplify the task of analyzing Go binaries using IDA Pro with a friendly methodology. Our hope is that members of the community will feel inspired to share additional resources to bolster our collective analysis powers.

A Quick Intro to the Woes of Go Binary Analysis

Go binaries present multiple peculiarities that make our lives a little harder. The most obvious is their size. Due to the approach of statically-linking dependencies, the simplest Go binary is multiple megabytes in size and one with proper functionality can figure in the 15-20mb range. The binaries are then easily stripped of debug symbols and can be UPX packed to mask their size quite effectively. That bulky size entails a maze of standard code to confuse reverse engineers down long unproductive rabbit holes, steering them away from the sparse user-generated code that implements the actual functionality.

Hello World source code
Mach-o binary == 2.0mb
UPX compressed == 1.1mb

To make things worse, Go doesn’t null-terminate strings. The linker places strings in incremental order of length and functions load these strings with a reference to a fixed length. That’s a much safer implementation but it means that even a cursory glance at a Go binary means dealing with giant blobs of unrelated strings.

“Hello World!” string lost in a sea of unrelated strings.

Even the better disassemblers and decompilers tend to display these unparsed string blobs confusing their intended purpose.

Autoanalysis output

Manually fixed disassembly

That’s without getting into the complexities of recovering structures, accurately portraying function types, interfaces, and channels, or properly tracing argument references.

The issue of arguments should prove disturbing for our dynamic analyst friends who were hoping that a debugger would spare them the trouble of manual analysis. While a debugger certainly helps determine arguments at runtime, it’s important to understand how indirect that runtime is. Go is peculiar in allocating a runtime stack owned by the caller function that will in turn handle arguments and allow for multiple return values. For us it translates into a mess of runtime function prologues before any meaningful functionality. Good luck navigating that without symbols.

Improving the Go Reversing Experience

With all those inadvertent obstacles in mind, it’s perfectly understandable that reversers dreaded analyzing Go binaries. However, the situation has changed in the past few years and we should reassess our collective abhorrence of Go malware. Different disassemblers and decompilers have stepped up their Go support. BinaryNinja and Cerbero have improved their native support for Go and there are now standalone frameworks like GoRE that offer good functionality depending on the Go compiler version and can even help support other projects like radare2.

We’ll be focusing on IDA Pro. With the advent of version 7.6, IDA’s native support of Go binaries is drastically better and we’ll be building features on top of this. For folks stuck on v7.5 or lower, we’ll also provide some help by way of scripts but the full functionality of AlphaGolang is unlocked with 7.6 due to some missing APIs (specifically the ida_dirtree API for the new folder tree view). This isn’t the first time brave souls in the community have attempted to improve the Go reversing experience for IDA. However, those projects were largely monolithic labors of love by their original authors and given the fickle nature of IDA’s APIs, once those authors got busy, the scripts fell into disrepair. We hope to improve that as well.


With AlphaGolang, we wanted to tackle two disparate problems simultaneously– the brittleness of IDApython scripts for Go reversing and the need for clear steps to analyze these binaries.

We can’t expect everyone to be an expert in Go in order to understand their way around a binary. Less so to have to fix the tooling they’re attempting to rely on. While engineers might be tempted to address the former by elaborating a complex framework, we figured we’d swing in the opposite direction and break up the requisite functionality into smaller scripts. Those smaller digestible scripts allow us to part out a relatable methodology in steps so that analysts can pick and choose what they need as they advance in their reversing journey.

Additionally, we hope the simplicity of the project and a forthrightness about its current limitations will inspire others to contribute new steps, fixes, and additional functionality.

At this time, the first five steps are as follows–

Step 0: Identifying Go Binaries

Go Build ID Regex

By popular request, we are including a simple YARA rule to help identify Go binaries. This is a quick and scrappy solution that checks for PE, ELF, and Mach-O file headers along with a regex for a single Go Build ID string.

Step 1: Recreate pcln Table

Recreating the Go pcln table

Dealing with stripped Golang binaries is particularly awful. Reversers unfamiliar with Go are disheartened to see that despite having all of the original function names within the binary, their disassembler is unable to connect those symbols as labels for their functions. While that might seem like a grand coup for malware developers attempting to confuse and frustrate analysts, it isn’t more than a temporary inconvenience. Despite being stripped, we have enough information available to reconstruct the Go pcln table and provide the disassembler with the information it needs.

Two noteworthy points before continuing:

  • The pcln table is documented as early as Go v1.12 and has been modified as of v1.16.
  • IDA Pro v7.6+ handles Go binaries very well and is unlikely to need this step. This script will prove particularly valuable for folks using IDA v7.5 and under when dealing with stripped Go binaries.

Depending on the file’s endianness, the script will locate the pcln table’s magic header, walk the table converting the data to DWORDs (or QWORDs depending on the bitness), and create a new segment with the appropriate ‘.gopclntab’ header effectively undoing the stripping process.

Step 2: Discover Missing Functions and Restore Function Names

Function discovery and renaming

Immediately after recreating the pcln table (or in unfortunate cases where automatic disassembly fails), function names are not automatically assigned and many functions may have eluded discovery. We are going to fix both of these issues in one simple go.

We know that the pcln table is pointing us at every function in the binary, even if IDA hasn’t recognized them. This script will walk the pcln table, check the offsets therein for an existing function, and instruct IDA to define a function wherever one is missing. The resulting number of functions can be drastically greater even in cases where disassembly seems perfect.

Additionally, we’ll borrow Tim Strazzere’s magic from the original Golang Loader Assist in order to label all of our functions. We ported the plugin to Python 3, made it compatible with the new IDA APIs, and refactored it. The functionality is now part of two separate steps (2 and 4) and easier to maintain. In this step, Strazzere’s magic will help us associate all of our functions with their original names in an IDA friendly format.

Step 3: Surface User-Generated Functions

Automatically categorizing functions

Having recovered and labeled all of our functions, we are now faced with the daunting proposition of sifting through thousands of functions. Most of these functions are part of Go’s standard packages or perhaps functionality imported from GitHub repositories. To belabor a metaphor, we now have street signs but no map. How do we fix this?

IDA v7.5 introduced the concept of Folder Views, an easily missed Godsend for the anal-retentive reverser. This feature has to be explicitly turned on via a right-click on the desired subview –whether functions, structures, imports, etc. IDA v7.6 takes this a step further by introducing a thus-far undocumented API to interact with these folder views (A heartfelt thank you to Milan Bohacek for his help in effectively wielding this API). That should enable us to automate some extraordinary time-saving functionality.

While our malware may have 5,000 functions, the majority of those functions were not written by the malware developers, they’re publicly documented, and we need nothing more than a nominal overview to know what they do.

NOBELIUM GoldMax (a.k.a. SunShuttle)

By being clever about categorizing function packages, we can actually whittle down to a fraction of the overall functions that merit analyst time. Functions will be categorized by their package prefixes and further grouped as ‘Standard Go Packages’, unlabeled (‘sub_’), uncategorized (no package prefix), and Github imports. What remains are the ‘main’ package and any custom packages added by the malware developers. For a notable example, the NOBELIUM GoldMax (a.k.a. SunShuttle) malware can be reduced from a hulking 4,771 functions to a mere 22. This is the simplest and perhaps the most valuable step towards our goal of understanding the malware’s functionality.

Step 4: Fix String References

Accurately recasting strings by reference

Finally, there’s the issue of strings in Go. Unlike all C-derivative languages, strings in Go are not null terminated. Neither are they grouped together based on their source or functionality. Rather, the linker places all of the strings in order of incremental length, with no obvious demarcation as to where one string ends and the next begins. This works because whenever a function references a string, it does so by loading the string address and a hardcoded length. While this is a safer paradigm for handling strings, it makes for an unpleasant reversing experience.

So how do we overcome this hurdle? Here’s where another piece of Strazzere’s Golang Loader Assist can help us. His original plugin would check functions for certain string loading patterns and use these as a guide to fix the string assignments. We have once again (partially) refactored this functionality, made it compatible with Python 3, and IDA’s new APIs. We have also improved some of the logic for the string blobs in place (either by suspicious length or because a reference points to the middle of a blob) and added some sanity checks.

While this step is already a marked improvement, we are seeing new string loading patterns introduced with Go v1.17 that need adding and there’s definitely room for improved refactoring. We hope some of you will feel inclined to contribute.

Where Do We Go From Here?

Let’s take a step back and look at where we are after all of these steps. We have an IDB with all functions discovered, labeled, and categorized, and hopefully all of their string references are correctly annotated. This is an ideal we could seldom dream of with malware of a comparable size written in C or C++ without extensive analysis time and prior expertise.

Now that we have a clear view of the user-generated functionality, what more can we do? How else can we improve our hunting and analysis efforts?

The following are a series of ideas we’d recommend implementing in further steps–

  • How about auto-generating YARA rules for user-generated functions and their referred strings?
  • Want a better understanding of arguments as they’re passed between functions? How about automagically setting breakpoints at the runtime stack prologues and annotating the arguments back to our IDB?
  • For reversers that follow the Kwiatkowski school of rewriting the code to understand the program’s functionality, how about selectively exporting the Hex-Rays pseudocode solely for the user-generated functions?
  • How about reconstructing interfaces and type structs from runtime objects?

Got more ideas? Want to help? Head to our SentinelLabs GitHub repo for all of the scripts and contribute your own shortcuts and superpowers for analyzing Go malware!

We’d like to thank the following for their direct and indirect contributions– 

  • Tim Strazzere for his original Golang Loader Assist script, which we refactored and made compatible with Python3 and the new IDA APIs. 
  • Milan Bohacek (Avast Software s.r.o.) for his invaluable help figuring out the idatree API.
  • Joakim Kennedy (Intezer)
  • Ivan Kwiatkowski (Kaspersky GReAT) for making his Go reversing course available.
  • Igor Kuznetsov (Kaspersky GReAT) for his help understanding newer pcln tab versions.


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