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20 Aug 2021

Defeating BlackMatter's string obfuscation


This blog post focuses on the various string obfuscation methods employed by the still relatively new ransomware BlackMatter and presents ways to decode those strings by leveraging Ghidra’s scripting capabilities and the usage of the Unicorn engine for CPU emulation. The corresponding Ghidra scripts published on Github aim to aid future analyses of BlackMatter-samples, which employ the following string obfuscation techniques:

Encoded strings and PE sections: The strings are stored in the .data-section and were XORed with a stream of pseudo-random numbers, which are generated by a linear congruential generator (LCG). This LCG is seeded with the first dword of the .rsrc-section. All strings are preceeded by their length, which is specified as a dword and is stored immediately before the encoded string.

Stack strings: Besides this, strings are constructed by placing dwords at adjacent memory regions on the stack. Those are decoded via a simple multi-byte XOR-operation with a constant dword.


The threat actor behind BlackMatter is still up to mischief and extorted several companies as the actor’s leak site suggests 1. In the last blog post the API hashing mechanism of the new ransomware BlackMatter has been explained and defeated by the utilization of Ghidra’s scripting capabilities and pre-computed hashlists. However, reconstructing the imported function calls of a malicious program is just one of the obstacles to uncover its behaviour and the author’s intent. Another important step is to de-obfuscate all hidden strings, which are contained in the binary. As this is an important part of malware analysis, which can provide valueable insight 2, this post describes BlackMatter’s string obfuscation mechanisms and provides scripts to automate its decoding.


This write-up deals with a BlackMatter-sample with SHA256-hash


This was already the subject of the previous blogpost. It has a compilation timestamp of 23rd of July 2021 20:51:18 UTC and was published on the 2nd of August 2021 at MalwareBazaar 3. This blog post deals only with the string obfuscation mechanisms employed by BlackMatter and describes ways of automated de-obfuscation.

Encoded strings in the .data-section

Identification of the string deobfuscation function

There are 25 calls of a function at address 004064cc which receives a byte array as an argument. All the data buffers passed to this function are global variables and live in the program’s .data-section. A quick peek into those memory areas showed, that all the content seems to have very high-entropy and is garbage to the human eye. An indicator that the .data-section houses strings, was that the function calls often happen just before API-calls like RegCreateKeyExW and others.

Encoding scheme

A closer look at the a/m function at address 004064cc – illustrated in fig. 1 – revealed, that within in its body the following steps were performed:

  1. Retrieve the size \(n\) of the given buffer by looking at the preceding dword
  2. Allocate \(n\) bytes of memory
  3. If the allocation succeded
    • Copy the content of the given buffer to the newly allocated memory chunk
    • Call actual decoding function at 00401713
  4. Return the pointer to the newly allocated (and now decoded) buffer
Figure 1: Setup for string decoding at 004064cc

As this is just a setup function, which prepares the decoding, a closer look at the function at address 00401713 – here labeled decodeWithLCG – is needed. Within this function a loop iterates over the buffer with a stepsize of four bytes. In each iteration a pseudo-random dword is generated, which is used to XOR the current four bytes as fig. 2 illustrates.

Figure 2: XOR-decoding at 00401713

The interesting thing here is the way the pseudo-random numbers are generated, which is done by utilizing the linear congruential generator (LCG) algorithm, which is implemented in the function at address 00401769 – here labeled getRandViaLCG. This is a fast and easy to implement method for generating a sequence of pseudo-randomized numbers. On Wikipedia the formula which defines the LCG is defined by recurrence relation as follows:

\(X_{n+1} = \left( a X_n + c \right)\bmod m\)

where \(X\) is the sequence of pseudorandom values, and

\(m, 0 < m\) — the modulus

\(a,0 < a < m\) — the “multiplier”,

\(c, 0 \le c < m\) — the “increment”

\(X_0, 0 \le X_0 < m\) — the “seed” 4

The implementation in the BlackMatter-binary matches this formal description very well as fig. 3 illustrates:

Figure 3: Implementation of the linear congruential generator at 00401769

Looking at the decompiled representation after having performed some variable renaming, the similarity is even more striking:

Figure 4: Decompiled linear congruential generator at 00401769

\(m\) is here 0x8088405, \(c\) is 1 and the seed \(X_0\) is hard-coded as the first dword of the .rsrc-section. The only difference from the a/m formula is, that instead of doing a modulo-operation a binary shift by 32 bits is used to get the “remainder” after performing the previous calculations with 64-bit integers.

Automated decoding

Having reversed this scheme and knowing, that the function at address 004064cc is used as a setup function, it is relatively easy to decode all strings within the binary. @sysopfb published a Python2-script to automatically decode the .data-section and print the results to stdout, which can be found here. Altough this is very helpful, it does not aid much during the reversing process, because the code references are lost. Therefore a Ghidra-script was created, which decrypts all buffers, that are passed in the “setup function” at address 004064cc, which initiiates the decoding process. The script is hosted on Github as BlackMatterDecodeLCG.java. It performs the following steps to decode all obfuscated scripts:

  1. Ask the user for the name of the function, which sets up the buffer for the decoding
  2. Retrieve the seed by reading the first dword of the .rsrc-section of the PE-file
  3. Find all calls of the specified function
  4. For each call:
    • Retrieve the address of the passed buffer
    • Read the preceding dword, which specifies the length \(n\) of the data to decode
    • Generate the $n$-element sequence of pseudo-random numbers and perform the XOR-operations
    • Update the buffer’s bytes in the memory

After running this script all the strings in the .data-section are stored in cleartext and are accessible during further analysis. Unfortunately, there is more string obfuscation, encoded stackstrings to be specific, which are discussed in the next section.

Encoded stackstrings

Another string obfuscation technique used by BlackMatter is the construction of strings on the stack. Those stackstrings, as they are commonly called 5, are constructed by moving each character into adjacent stack addresses at runtime. BlackMatter goes beyond that and stores dword-values on the stack and decodes those values via an XOR-operation with a constant value. The following assembly code at 00407b6b illustrates this procedure.

Figure 5: Decoding of stackstrings at 00407b6b

While there are various ways to counter those XORed stackstrings, the usage of code emulation seems to be a comfortable and interesting solution. FireEye’s FLARE team released an IDA Pro script, which provides this functionality 6 and builds up on flare-emu, a library, which “marries a supported binary analysis framework, such as IDA Pro or Radare2, with Unicorn’s emulation framework”.

Given that Ghidra is not supported until now, it seemed to be a worthwhile endeavour to prepare the usage of the Unicorn engine from within a Ghidra script written in Java.

Setting up Unicorn for the use with Ghidra

Obviously, it is needed to install the Unicorn Engine on the system first:

# Clone Unicorn's repo
git clone https://github.com/unicorn-engine/unicorn.git

# Compile the code and install Unicorn's engine
cd unicorn
sudo ./make.sh install

Then build and install Unicorn’s Java-bindings, which are already included in Unicorn’s repository:

cd unicorn/bindings/java
# This path change was needed for me
sed -i 's@#include "unicorn_Unicorn.h"@#include "unicorn/unicorn_Unicorn.h"@' unicorn_Unicorn.c
# Assemble the .jar and the .so
make jar
make lib

# Install the shared lib
sudo cp libunicorn_java.so /usr/lib/jni/

There are several options to add the unicorn.jar to Ghidra’s build path as it is discussed in this Github issue. Personally, I prefer to place the external .jar-files to use in my scripts inside the directory ${GHIDRA_INSTALL_DIR}/Ghidra/patch, like so:

# Add unicorn.jar to Ghidra's build path
sudo mv unicorn.jar ${GHIDRA_INSTALL_DIR}/Ghidra/patch

If you need code completion in Eclipse, add the .jar-file to your build path by right clicking on it and choosing “Add to build path”.

Using Unicorn to decode stackstrings

After completing the above mentioned steps, you should be able to import Unicorn in your Java-classes by adding

import unicorn.*

and be able to create Ghidra scripts, which emulate code in the binary to analyze.

For a full but rather simple Python example refer to this script called bm_stackstrings.py. The general structure of the code is adapted from Jason Reaves’ blog post on the decryption of BazaarLoader-strings with the help of Unicorn’s Python-library 7, but the regex-pattern was adapted to BlackMatter’s code blocks 8 and the script was ported to Python 3.

I took this approach as a base for the creation of a Ghidra script which emulates either the instructions in the code range selected in Ghidra’s UI or specified by a start and an end address queried via a dialog from the user. After the region of interest to emulate is defined, memory has to be allocated for the code segment and the stack segment. Afterwards the memory region marked in Ghidra’s UI is copied to the code segment and the stack segment is filled with zeros. Then the emulation of the code is kicked off. When the emulation has finished, the stack is read and scanned for the existance of a printable string. If one was found, it will be set as a comment just before the start address. If you want to try it yourself or just have a peek into a full example of Unicorn’s usage in a Ghidra script, refer to



This blog post detailed the string obfuscations employed by the new ransomware BlackMatter.

To obfuscate strings in the .data-segment, multi-byte XOR-operations with a stream of pseudo-random numbers are performed in the function at address 00401713. Those pseudo-random numbers are generated by a linear congruential generator, which is seeded by the first dword in the .rsrc-section of the binary.

In addition to that BlackMatter employs stackstrings, which are XORed with a constant dword value. To decode those stackstrings quickly, it is fairly comfortable to use the CPU-emulator Unicorn. The installation and usage of Unicorn’s Java bindings from within a Ghidra script is described at detail in this write-up.

If you have any notes, errata, hints, feedback, etc., please send a mail to jan _at___ digital-investigations d0t info.

Appendix: Recovered strings

Here are some of the recovered strings for the crawler’s of the search engines.

Recovered stack strings

Times New Roman
Control Panel\Desktop

Recovered strings from the .data-section

BlackMatter Ransomware encrypted all your files!
To get your data back and keep your privacy safe,
you must find %s file
and follow the instructions!


Accept: */*
Connection: keep-alive
Accept-Encoding: gzip, deflate, br
Content-Type: text/plain

SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon
bcdedit /set {current} safeboot network
bcdedit /deletevalue {current} safeboot
bootcfg /raw /a /safeboot:network /id 1
bootcfg /raw /fastdetect /id


Tags: TI REM
04 Aug 2021

Understanding BlackMatter's API Hashing


The ransomware BlackMatter aims at stepping into the void, which was left by REvil’s and DarkSide’s (temporary) retreat. At this point in time this new ransomware seems to pose a serious threat. In this blogpost BlackMatter’s API hashing mechanism is described in detail and a Ghidra-script is supplied 1 to aid future analyses.

The API hashing algorithm: To calculate the API hashes for function names, each and every character is added up, while a binary rotation to the right by 13 bits is performed in each iteration. The hash of the housing module, which was calculated in the same manner before, serves as a seed value for the respective hash.

Storing addresses: Notably, BlackMatter does not store the function addresses in clear after resolving them. Instead it uses an array of “trampoline-pointers”, which point to small, dynamically allocated assembly blocks (12 bytes in size), which perform the XOR-decoding of the encoded version of the respective function address, that was placed in there during import resolution and call it afterwards.


A new ransomware gang named BlackMatter appeared in July 2021 and started to recruit affiliates at the underground forums Exploit and XSS2. They fill the void, which is left by DarkSide’s shutdown after the Colonial Pipeline attack3 and REvil’s disappearing in the mid of July after pwning Kaseya4.

On the 2nd of August 2021 an interview of D. Smilyanets (The Record) with the alleged threat actor behind BlackMatter was published5. Within this interview, the actor states, that he is neither the successor of DarkSide nor REvil, instead he proclaims, that BlackMatter tries to unify the best of the ransomwares LockBit, REvil and DarkSide, which all have their individual strengths in the opinion of the alleged actor behind “the new ransomware on the block”.

At this point in time it seems to be a valid assumption, that BlackMatter will keep the DFIR-community and the law enforcement agencies busy for the next few weeks, therefore initial analyses might be helpful to get to know the threat imposed by this probably rebranded actor.


For this analysis the BlackMatter-sample with SHA256


was used. It has a compilation timestamp of 23rd of July 2021 20:51:18 UTC and was published on the 2nd of August 2021 at MalwareBazaar6.

This blog post deals with the API hashing found in this sample and shows a way to defeat it with the help of Ghidra scripting. Resolving the hidden imports, is the main prerequisite for a static analysis of BlackMatter-binaries. However, further steps, like the decoding of its eventually available config data, are not in scope of this blog post.


Directly after the entry point of the executable, the function at address 00405e5c, which is responsible for initializing the import resolution is called, as the following figure of the decompiled code illustrates.

Figure 1: Decompilation of the setup function at 00405e5c, which kicks off the import resolution

At l. 10 and l. 15 of this function the actual import resolution is started by calling another function at 0040581d, named resolveHashedImport in the figure above. In this function all the heavy lifting required to resolve symbols is performed, e.g. the loaded modules are traversed in memory by utilizing the doubly-linked list named InLoadOrderModuleList of the PEB_LDR_DATA-struct and so on.

The goal of those initial calls is to retrieve HeapCreate and HeapAlloc (l. 10 and 15) at first. This is only possible since the called function at 0040581d ensures that LoadLibraryA and GetProcAddress are loaded on the first invocation by recursive calls to itself, as it is shown in the following figure exemplary for LoadLibraryA.

Figure 2: Recursive call from within 0040581d to load LoadLibraryA on the first invocation

So how is the hash, which is passed to the function called at 00405844 calculated by BlackMatter?

Hash calculation

For the calculation of the API hash each character is added up one by another. In each iteration a seeded ROR-13-operation is performed, as the following figure illustrates.

Figure 3: Algorithm to calculate the API hash

Because of the fact, that the hash of the module name is used as a seed, a two step process has to be employed to construct the final API hash for a single function.

First, the module name is hashed in a similar manner with a seed of 0. This happens in the function at 004010bb, which is not shown here. It is looped over the characters, which are transformed to lower case. In each iteration a rotation by 13 bits of the dword value resulting from the previous iteration is performed and the current character value is added. This leads to the following Python implementation:

def calc_mod_hash(modname):
    mask = 0xFFFFFFFF
    h = 0
    for c in modname + "\x00":
        cc = ord(c)
        if (0x40 < cc and cc < 0x5b):
            cc = (cc | 0x20) & mask
        h = (h >> 0xd) | (h << 0x13)
        h = (h + cc) & mask

    return h

The resulting hash of the module name is then used as a seed for the similar but simpler function presented at fig. 3, which finally calculates the actual function hash. The following Python code shows the logic found in this function at 00401096:

def calc_func_hash(modhash, funcname):
    mask = 0xFFFFFFFF
    h = modhash
    for c in funcname + "\x00":
        cc = ord(c)
        h = (h >> 0xd) | (h << 0x13)
        h = (h + cc) & mask

    return h

Note: It is important to add the nullbyte, so that for a function name of n characters, n+1 ROR-operations are performed.7

In summary this leads to the following calculation of a function hash as it is used by BlackMatter:

def get_api_hash(modname, funcname):
    return calc_func_hash(calc_mod_hash(modname), funcname)

Let’s test it:

mn = "kernel32.dll"
fn = "GetProcAddress"
print(hex(get_api_hash(mn, fn)))

mn = "kernel32.dll"
fn = "LoadLibraryA"
print(hex(get_api_hash(mn, fn)))
: 0xbb93705c
: 0x27d05eb2

Indeed, both hashes can be found in the binary, as fig. 3 shows:

Figure 4: Function hashes of LoadLibraryA and GetProcAdress

Actually only 0x5d6015f ^ 0x22065fed, wich results in 0x27d05eb2 can be found, since all API hashes are stored XORed with 0x22065fed and are XORed again with this value before a comparison with the calculated hash.

(Re)storing imports

After the a/m and absolutely required functions like HeapAlloc, LoadLibraryA, etc. have been loaded. BlackMatter resolves blocks of hashed functions stored as dwords in global memory (2nd arg to function8) and stores pointers to dynamically allocated “structs” in global memory as well (1st arg to function9):

Figure 5: Resolving array of hashes (here for Kernel32.dll)

Line 18 in fig. 1 already showed this code in a decompiled representation.

Fig. 6 shows the decompilation of the called function beginning at 00405a86. Within there, it is looped over the array of function hashes until the value 0xCCCCCCCC is reached. This serves as an indicator of the end of the list of function hashes, so the loop stops in l. 19, when this value is read.

Figure 6: Storing XORed function address with Assembly instructions

Line 29 ff. looks very interesting here. To further complicate analysis, BlackMatter does not store the function address itself in the result array. Instead it stores a pointer to 12 bytes of dynamically allocated memory. In these 12 bytes it does not store the function address in clear. Instead the results of XOR-operations (here XORed with 0x22065fed) are stored together with assembly instructions to decode the real function address on the fly, when the function is called as fig. 6 suggests.

So the global array of pointers which is passed as a buffer to hold the results of the import resolution (e.g. l. 18 ff. in fig. 1 and fig. 5) acts as trampoline, so that on each call, it is jumped to a 12 byte “function-struct”, which is comprised of the following opcode sequence on the heap, where the questionmarks resemble the XORed-function address in question:

B8 ?? ?? ?? ?? 35 ED 5F 06 22 E0 FF

Upon execution, these instructions load the XORed-function address into EAX and perform the XOR-operation again to reverse it and to finally call the decoded function address, so that the actual libary-call is performed without storing the function-addresses in memory.

Import resolution with Ghidra scripting

The labelling of the a/m “trampoline-pointers”, whose call ultimately leads to the execution of the a/m opcode-sequence should be automated with Ghidra’s scripting capabilities. To accomplish this, have a look at the following Java-code in my Gist:


Note, that this Ghidra-script is based on L. Wallenborn’s and J. Hüttenhain’s template code10. (Thank you guys for your invaluable teaching!)

Upon execution the script asks for the name of the resolving function (the one called in fig. 5), which takes the two pointers to global memory regions (here at 00405a86). In the next GUI-dialog, that pops up, the XOR-key has to be specified (here 0x22065fed). Afterwards you have to choose the file, containing the precomputed hashes, which should be used for name resolution. This list can be found at my Gist as well:


If you stumble upon a BlackMatter-sample, that uses the same ROR-13-hashing, this script might help to get you started quickly with the analysis.


This blog post detailed the API-hashing mechanism employed by the new ransomware BlackMatter.

To hash a function name, BlackMatter employs a seeded ROR13 in an iterative manner. That is a rotation of the dword by 13 bits to the right. The name of the housing module, hashed in the same way, but with an initial value of 0, is used as a seed for this trivial hashing algorithm. It has to be noted, that due to the implementation with a do-while-loop, for a function name of length n (terminating zero-byte excluded) n+1 ROR-operations will be performed. The API hashes are initially stored as dwords in global arrays XORed with 0x22065fed.

Interestingly, the imported function addresses are stored in a dynamically allocated memory region. To further complicate analysis, BlackMatter does not store the function address itself, but the result of an XOR-operation (here again XORed with 0x22065fed) together with assembly instructions to decode it on the fly, when the function is called by a pointer to this memory location housing these instructions.

During the import resolution-routine at 00405a86, which is called multiple times with different arrays of API hashes, pointers to those opcode-sequences are stored in a global array, which is then referenced for executing the single functions, when needed.

If you have any notes, errata, hints, feedback, etc., please send a mail to ca473c19fd9b81c045094121827b3548 at digital-investigations.info.


Tags: TI REM
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