# Everything you need to know about the LummaC2 stealer: Leveraging IDA Python and Unicorn to deobfuscate Windows API Hashing

**[outpost24.com/blog/everything-you-need-to-know-lummac2-stealer](https://outpost24.com/blog/everything-you-need-to-know-lummac2-stealer)**

Everything you need to know about the LummaC2 stealer: Leveraging IDA Python and
Unicorn to deobfuscate Windows API Hashing

05.Apr.2023

Alberto Marín, KrakenLabs Malware Sandbox Lead

[Threat Intelligence](https://outpost24.com/blog?tags=335)

In this blog post, the KrakenLabs team will take a deep dive into a malware sample classified
as LummaC2, an information stealer written in C language that has been sold in
underground forums since December 2022. We assess LummaC2’s primary workflow, its
different obfuscation techniques (like Windows API hashing and encoded strings) and how to
overcome them to effectively analyze the malware with ease. We will also analyze how
networking communications with the C2 work and summarize LummaC2’s MITRE
Adversarial Tactics, Techniques and Common Knowledge.

There is a huge spike in the popularity and use of information-stealing malware in
underground markets. With more stealing capabilities, simplified administration, and ability to
remain undetected, stealers are available to anyone with basic computer knowledge. This is
evident in the price evolution of different stealer malware families from 2018-2022. In a
[recent report, Outpost24’s Threat Intelligence team, KrakenLabs, revealed a change in the](https://outpost24.com/resources/whitepapers/the-rising-threat-of-traffers)
pricing model with more bad actors offering subscription-based access to their malware,
instead of a single payment model.

In this blog post, the KrakenLabs team will take a deep dive into a malware sample classified
as LummaC2, an information stealer written in C language that has been sold in
underground forums since December 2022. We assess LummaC2’s primary workflow, its
different obfuscation techniques (like Windows API hashing and encoded strings) and how to


-----

overcome them to effectively analyze the malware with ease. We will also analyze how
networking communications with the C2 work and summarize LummaC2’s MITRE
Adversarial Tactics, Techniques and Common Knowledge.

This malware family is an evolution of its predecessor LummaC, developed by the same
[threat actor, and sold since August 2022 on underground forums.](https://www.accenture.com/us-en/blogs/security/information-stealer-malware-on-dark-web?tlaAppCB)

## New stealer for sale: LummaC2

The information stealer is offered for sale in several underground forums and via the official
shop lumma[.]site by the threat actor "Shamel" using the alias “Lumma”, who is also
[responsible for the sales of the 7.62mm stealer. Outpost24 KrakenLabs analysts have also](https://ke-la.com/information-stealers-a-new-landscape/)
found advertisements in other forums by the alias “LummaStealer”, which is presumably a
reseller of the stealer.

Figure 1. Dark Web Post for LummaC2 Stealer

As we will see in detail later, this malware targets crypto wallets, browser extensions, twofactor authentication (2FA) and steals sensitive information from the victim’s machine.

LummaC2 is offered at the following prices depending on the features offered:

Experienced US$250;
Professional US$500;
Corporate account US$1,000.

[An earlier version of the website seen in a screenshot on Cyble's article indicates that it was](https://blog.cyble.com/2023/01/06/lummac2-stealer-a-potent-threat-to-crypto-users/)
also possible to purchase the stealer and panel source code for a price of US$20,000.


-----

The purchase of the stealer can be processed through the well-known cryptocurrency
exchange Coinbase from a wide range of cryptocurrencies to choose from.

Figure 2. Screenshot obtained from LummaC2 shop (information automatically translated
from Russian to English).

### Deobfuscating LummaC2

**LummaC2 Windows API call Obfuscation**

**LummaC2 makes use of API hashing, which is a common technique seen in malware in**
order to hide their functionality from tools relying on static information and to obfuscate the
code, which makes it harder for an analyst to understand what the malware does.

The following picture shows an example of how Windows API calls are performed:

Figure 3. Example of an obfuscated call to NtClose for LummaC2


-----

The malware executes a function that receives a DLL name string in EDX register(e.g
“ntdll.dll”) and an input hash (in ECX register). This function internally resolves
kernel32!LoadLibraryA to load the desired .dll (in this case “ntdll.dll”) and proceeds to parse
its Export Table. It hashes each export name until it finds one that matches the input hash.
This way it is able to resolve any Windows API Call, saving the address found in EAX
register as a result. Then a call eax instruction will finally execute the desired Windows API
call.

Figure 4. LummaC2 parsing Export Table and hashing with MurmurHash2 to resolve
Windows API calls

The hashing algorithm that LummaC2 uses to resolve Windows API calls is MurmurHash2
with 32 as seed value.


-----

Figure 5. Decompiled view (excerpt) of MurmurHash2 routine using 32 as seed value

### Defeating LummaC2 Windows API call Obfuscation

The following lines are aimed at removing the call obfuscation scheme for LummaC2 now
that we know how it resolves Windows API calls. The idea is to automatically resolve all
Windows API calls used in the code so that we have a better picture of the malware
capabilities without the need of debugging and entering in every single path the malware can
take to resolve all its possible calls.

To do so, we will generate a dictionary containing all the Windows API calls from a given set
of Windows .dll files and their respective MurmurHash2. With this dictionary, we can then
get every hash sent to the function resolving Windows Api calls and figure out which function
is being resolved.

**Preparing Windows API call hash dictionary**

We could try and find a public implementation or MurmurHash2 but it is possible that the
algorithm the malware uses may be altered in the future so that the standard implementation
[does not work. For this reason, another good approach is to use Unicorn, as it allows us to](https://www.unicorn-engine.org/)
emulate the exact instructions that the malware executes.

**Unicorn**

The hashing routine is a “standalone” routine that we can extract easily from the binary and
does not have any calls or jumps to other locations apart from the hashing routine itself.
Which means we can run this shellcode in an emulated environment without previous


-----

patching to ensure everything is linked properly (with the exception of the last return
instruction, which we should ignore for the emulation).

In this scenario, the malware hashing algorithm expects to have the string with the Windows
API call in ECX register and the result hash (which we will read) is finally stored in EAX
register.

Figure 6. Call graph view for LummaC2 MurmurHash2 implementation

As we now have all the data we need to emulate the binary, the last step for this part is to
build the emulation environment for our code to run on. To accomplish this, we will use the
[open-source Unicorn Engine.](https://www.unicorn-engine.org/)

The first thing we want to do is initializing Unicorn for the architecture we want to emulate
(x86 architecture), and map some memory to use. Next, we will write our shellcode to our
memory space and initialize ECX pointing to our Export Name string. With all this in place,
we are ready to run emulation and read the resulting hash in EAX register afterwards.

The following Python function uses Unicorn to emulate the hashing algorithm LummaC2
uses to resolve Windows API Calls:


-----

```
  import unicorn

def emulate_murmurhash2(data, seed=32):

  code =
"\x56\x57\x8B\xF9\x8B\xD7\x8D\x4A\x01\x8A\x02\x42\x84\xC0\x75\xF9\x2B\xD1\x8B\xF2\x83
\xF6\x20\x83\xFA\x04\x7C\x4D\x53\x8B\xDA\xC1\xEB\x02\x6B\xC3\xFC\x03\xD0\x0F\xB6\x4F\
x03\x0F\xB6\x47\x02\xC1\xE1\x08\x0B\xC8\x69\xF6\x95\xE9\xD1\x5B\x0F\xB6\x47\x01\xC1\x
E1\x08\x0B\xC8\x0F\xB6\x07\xC1\xE1\x08\x83\xC7\x04\x0B\xC8\x69\xC9\x95\xE9\xD1\x5B\x8
B\xC1\xC1\xE8\x18\x33\xC1\x69\xC8\x95\xE9\xD1\x5B\x33\xF1\x83\xEB\x01\x75\xBF\x5B\x83
\xEA\x01\x74\x1C\x83\xEA\x01\x74\x0E\x83\xEA\x01\x75\x1D\x0F\xB6\x47\x02\xC1\xE0\x10\
x33\xF0\x0F\xB6\x47\x01\xC1\xE0\x08\x33\xF0\x0F\xB6\x07\x33\xC6\x69\xF0\x95\xE9\xD1\x
5B\x8B\xC6\xC1\xE8\x0D\x33\xC6\x69\xC8\x95\xE9\xD1\x5B\x5F\x5E\x8B\xC1\xC1\xE8\x0F\x3
3\xC1"

  CODE_OFFSET = 0x1000000

  mu = unicorn.Uc(unicorn.UC_ARCH_X86, unicorn.UC_MODE_32)

  mu.mem_map(CODE_OFFSET, 4*1024*1024)

  mu.mem_write(CODE_OFFSET, code)

  libname = 0x7000000

  mu.mem_map(libname, 4*1024*1024)

  mu.mem_write(libname, data)

  stack_base = 0x00300000

  stack_size = 0x00100000

  mu.mem_map(stack_base, stack_size)

  mu.mem_write(stack_base, b"\x00" * stack_size)

  mu.reg_write(unicorn.x86_const.UC_X86_REG_ESP, stack_base + 0x800)

  mu.reg_write(unicorn.x86_const.UC_X86_REG_EBP, stack_base + 0x1000)

  mu.reg_write(unicorn.x86_const.UC_X86_REG_ECX, libname)

  mu.emu_start(CODE_OFFSET, CODE_OFFSET + len(code))

  result = mu.reg_read(unicorn.x86_const.UC_X86_REG_EAX)

  return result


```
We can now easily write a script to walk through files inside a directory where we have
Windows .dlls and, for each .dll, parse its Exports and calculate its MurmurHash2 using the
previous function. This could be an example of the implementation using pefile:


-----

```
  import pefile

def dump_hash_dlls():

  '''

  This function uses pefile to get the export names from .dlls and apply the
hashing

  algorithm to them.

  '''

  dlls_dir = 'dlls/'  # Directory where we have Windows .DLL files

  for (dirpath, dirnames, filenames) in os.walk(dlls_dir):

    for filename in filenames:

      if filename.endswith('.dll'):

        pe = pefile.PE('{}'.format(dlls_dir+filename))

        for exp in pe.DIRECTORY_ENTRY_EXPORT.symbols:

          export_name = exp.name

          if not export_name:

            # "Ignoring export without name..."

            continue

          try:

            export_hash = emulate_murmurhash2(export_name)

          except Exception as err:

            print 'Exception occurred while emulating murmurhash2 with
export_name: {}. Error: {} '.format(export_name, err)

            continue


```
The results can then be saved as we prefer. In this case, we need a Python dictionary that
we can use in IDA Python script when analyzing the malware. We can save the result
dictionary in a .json file like the following one:


-----

```
  {

  "1002323769": "kernel32_MoveFileTransactedA",

  "1002333354": "ntdll_ZwSetTimer",

  "1003390208": "ntdll_memmove_s",

  "1003407985": "advapi32_LsaDelete",

  "1004879971": "ntdll_NtReadOnlyEnlistment",

  "100560003": "kernel32_GetThreadId",

  "1006629348": "kernel32_ReadConsoleOutputW",

  "1007338292": "kernel32_GetProcessPreferredUILanguages",

  "1007695856": "user32_GetClassInfoExW",

  "1008342899": "shell32_SHCreateStdEnumFmtEtc",

  "1008627276": "advapi32_QueryTraceW",

  "1008723271": "ntdll_NtDisableLastKnownGood",

  "1009340263": "advapi32_RegSaveKeyW",

  "1009496315": "kernel32_FlushViewOfFile",

  "1009939290": "shlwapi_PathFindSuffixArrayW",

  "101018728": "ntdll_ZwOpenTransactionManager",

  "1010305366": "user32_SetWindowLongA",

  "1010398495": "shlwapi_PathUnExpandEnvStringsW",

  "101074275": "kernel32_EnumDateFormatsExW",

  "1011639982": "user32_AppendMenuA",

  "1012134009": "user32_CharToOemBuffW",

  "1012436811": "ntdll_NtCreateNamedPipeFile",

  "1013083577": "shell32_ShellExec_RunDLL",

  "1014808818": "ntdll_NtUnmapViewOfSection",

  "1016118817": "ntdll_NtQueryInformationThread",

  "1017169715": "shell32_ILCloneFirst",

  "1017424400": "user32_ReleaseCapture",

(…)


```
**Resolving obfuscated Windows API calls**

Now that we have all the possible exports that the malware may use, it is time to create an
**IDA Python script to help us reverse engineer LummaC2.**

This script is going to be divided in 2 parts. From one side, we are going to resolve every
Windows API call (checking the hash set in ECX against our big dictionary) and create an
IDA comment staying the final Windows API call being made. In the end, we will execute
another script while debugging LummaC2 to patch all these calls.

This will help us to easily understand how the malware operates and its capabilities to ease
reverse engineering without the need of debugging and executing every possible path the
malware can take.

The first thing to do is to place our .json file (the one with the big Python dictionary storing all
the Windows API calls and respective hashes) in our analysis VMs where IDA Python script
is going to be executed. Then the script must be able to read and save its contents for further
analysis:


-----

```
  import json

hashes_dict = {}

def setup(hashes_dict_file):

  global hashes_dict

  try:

    with open(hashes_dict_file, 'rb') as fd:

      hashes_dict = json.load(fd)

  except Exception as err:

    print 'Error while readning hashes dict. file: {}'.format(err)


```
Now that we have our dictionary ready. Let’s examine the different patterns that are used
when resolving a Windows API call. We know that ECX register must have the hash, but this
can be achieved in the code through different ways:

Figure 7. Example of different scenarios where Windows API call resolution is made

As we can see, in the end ECX always contains the hash to be resolved. However, the
instruction that sets the specified hash can move it into a different register before being in
ECX. The last two patterns use EDI and ESI registers respectively.

With this information, we should be able to go through all cross references to the call
“ResolveApiByHash”, retrieve the hash being used and resolve the Windows API call using
our big hash dictionary. The following Python function implements this. It only expects to
receive the address of the call “ResolveApiByHash” as its only argument.


-----

```
  import idautils

import idc

def resolve_all_APIs(resolve_ea):

  patches = []

  total_apis_found = 0

  total_apis_resolved = 0

  global hashes_dict

  if resolve_ea is None:

    print('[!] Resolve failed.')

    return

  for ref in idautils.CodeRefsTo(resolve_ea, 1):

    total_apis_found += 1

    curr_ea = ref

    API_hash = 0

    for _ in range(30): # Search maximum the last 30 instructions before the call
to ResolveApiByHash

      prev_instruction_ea = idc.PrevHead(curr_ea)

      instruction = idc.GetDisasm(prev_instruction_ea)

      # Possible scenarios

      '''

      .text:0040214B B9 73 10 FF E8             mov   ecx,
0E8FF1073h

      .text:00402150 E8 7E 61 00 00             call  
ResolveApiByHashWrapper

      or

      .text:004074F6 BF 29 A1 D3 5F             mov   edi,
5FD3A129h

      .text:004074FB BA C8 4B 42 00             mov   edx,
offset aWininet_dll ; "wininet.dll"

      .text:00407500 8B CF                      mov  
ecx, edi

      .text:00407502 E8 CC 0D 00 00             call  
ResolveApiByHashWrapper

      or

      .text:00407D8C BE 30 E2 95 3D             mov   esi,
3D95E230h

```

-----

```
      .text:00407D91 8B D3                     mov  
edx, ebx

      .text:00407D93 6A 00                     push  0

      .text:00407D95 8B CE                     mov  
ecx, esi

      .text:00407D97 E8 37 05 00 00             call  
ResolveApiByHashWrapper

      '''

      instruction_cut = instruction.replace(' ', '')

      if 'movecx' in instruction_cut or 'movedi' in instruction_cut or 'movesi'
in instruction_cut:

        API_hash = idc.GetOperandValue(prev_instruction_ea, 1)

        if API_hash < 0x10:

          # Avoid intermediate movs, when the target is in edi (e.g mov
ecx, edi)

          curr_ea = prev_instruction_ea

          continue

        API_hash_idx = str(API_hash)

        if API_hash_idx in hashes_dict:

          print('API hash: {} {} {}'.format(hex(prev_instruction_ea),
hex(API_hash), hashes_dict[API_hash_idx]))

          apicall = hashes_dict[API_hash_idx].split('_')[-1] #
"kernel32_AddAtomA" -> "AddAtomA"

          idc.MakeComm(ref, apicall)

          patch_info = (ref, hashes_dict[API_hash_idx])

          patches.append(patch_info)

          total_apis_resolved += 1

        else:

          print("Hash not found!")

        break

      curr_ea = prev_instruction_ea

  print('Total APIs found: {} Total APIs resolved: {}'.format(total_apis_found,
total_apis_resolved))

  return patches



```

-----

```
setup( c:\murmurhash2_hashes_dict.json ) # Big Python dict. With hashes and export
names

patches = resolve_all_APIs(0x004082D3) # Use address of “ResolveApiByHash”


```
The return value is a list of tuples (addr, apicall) that we will use later to patch the binary.
After executing the script, we can see how now we have comments for every Windows API
call resolution and have a better understanding of what the malware can do. We can also
use xref view to quickly see all the Windows API calls (with their resolved name as a
comment) the malware can use.

Figure 8. Example of result from executing the previous script. Windows API calls are
commented now


-----

Figure 9. Cross-Referencing the Windows API resolution routine can help identifying
malware capabilities now

**Patching the binary and removing obfuscation**

The last part of this script would allow us to patch the binary to remove all of LummaC2
Windows API call obfuscation. This way we can focus only in the relevant instructions and
help IDA decompiler to easily recognize the arguments that are being passed to these
functions.

With the current implementation, we can easily see which Windows API calls are being used
(as we have comments in every call that resolves a function). However, we still have many
unnecessary instructions in the code, and the call is still being done with the instruction “call
eax”, which does not help us much with analysis, cross-referencing, etc.

The goal of the following Python function for IDA is to patch the “call ResolveApiByHash” for
the real Windows API call that is going to be called after resolution. From the previous script,
we have a list of tuples. Each element of the tuple consists of an address (where the call
resolution is made) and the Windows API call that is going to be called.


-----

```
  def patch_apicall_wrapper(patches):

  total_apis_patched = 0

  for item in patches:

    # item : (addr, apicall) (e.g (0xCAFEBABE, "kernel32_AddAtomA"))

    try:

      addr = item[0]

      apicall = item[1]

      success_patch = patch_apicall(addr, apicall)

      if success_patch:

        total_apis_patched += 1

    except Exception as err:

      print('Error patching call: {}'.format(err))

  print('Total APIs patched: {}'.format(total_apis_patched))

```
The function “patch_apicall” is going to be the responsible for retrieving the address of a
Windows API call and patching the “call ResolveApiByHash” for the call to the expected
export.

One drawback that we may find, is that we cannot resolve the address of an export from a
Windows .dll that has not been loaded in the address space from the debugged process yet.
To overcome this issue, we can make use of IDA “Appcall” feature. With Appcall we can
execute LoadLibraryA to load any missing .dll so that we can resolve all exports just from
the Entry Point. (Otherwise, we would have to wait until the malware loads the library for the
first time; which would not allow us to automate everything from the Entry Point as we are
doing now).

Once we have the address, we need to get the relative offset and then we can use 0xE8
opcode with this relative offset to patch the call “ResolveApiByHash”. Finally, we append two
**nop instructions after to overwrite the proceeding “call eax”.**


-----

```
  def patch_apicall(addr, apicall):

  '''

  If it cannot be resolved, it is possible that the malware has not loaded the
library yet (for example, wininet.dll). In this case, we can force the load of the
module using AppCall:

  loadlib = Appcall.proto("kernel32_LoadLibraryA", "int __stdcall loadlib(const
char *fn);")

  hmod = loadlib("wininet.dll")

  '''

  loadlib = Appcall.proto("kernel32_LoadLibraryA", "int __stdcall loadlib(const
char *fn);")

  print('apicall: {}'.format(apicall))

  apiaddr = idc.LocByName(apicall)

  if apiaddr == 0xFFFFFFFF:

    hmod = loadlib('{}.dll'.format(apicall.split('_')[0]))

    apiaddr = idc.LocByName(apicall)

    if apiaddr == 0xFFFFFFFF:

      return False

  # At this point, apiaddr has the address of the Api call that we want. (e.g
0x772AB880 for wininet_InternetOpenA)

  rel_offset = (apiaddr - addr - 5) & 0xFFFFFFFF

  idc.PatchDword(addr+1, rel_offset) # Patch “call ResolveApiByHash” -> “call
InternetOpenA”

  idc.PatchByte(addr+5, 0x90) # patch with nop

  idc.PatchByte(addr+6, 0x90) # patch with nop

  return

setup("c:\murmurhash2_hashes_dict.json") # Big Python dict. With hashes and export
names

patches = resolve_all_APIs(0x004082D3) # Use address of “ResolveApiByHash”

patch_apicall_wrapper(patches) # Patch the binary



```
With this implementation, we have now patched all the calls to the real exports that the
malware wants to use. However, IDA has now trouble identifying and displaying properly the
arguments of the functions in the decompiler view. An example of this can be seen in the
following picture:


-----

Figure 10. Example before (left) and after (right) the script execution. Binary is patched to
call the real function

Figure 11. Example of how arguments cannot be easily recognized at first by IDA decompiler
yet

The last thing to do here is to remove the instructions related with the Windows API call
resolution (registry operations used to move the .dll string and the hash). This way the
decompiler will show the arguments and everything as expected, while the code will look
clean and show only the necessary instructions. This would be the final implementation of
the previous “patch_apicall” function:


-----

```
  def patch_apicall(addr, apicall):

  '''

  If it cannot be resolved, it is possible that the malware has not loaded the
library yet (for example, wininet.dll). In this case, we can force the load of the
module using AppCall:

  loadlib = Appcall.proto("kernel32_LoadLibraryA", "int __stdcall loadlib(const
char *fn);")

  hmod = loadlib("wininet.dll")

  '''

  loadlib = Appcall.proto("kernel32_LoadLibraryA", "int __stdcall loadlib(const
char *fn);")

  print('apicall: {}'.format(apicall))

  apiaddr = idc.LocByName(apicall)

  if apiaddr == 0xFFFFFFFF:

    hmod = loadlib('{}.dll'.format(apicall.split('_')[0]))

    apiaddr = idc.LocByName(apicall)

    if apiaddr == 0xFFFFFFFF:

      return False

  # At this point, apiaddr has the address of the Api call that we want. (e.g
0x772AB880 for wininet_InternetOpenA)

  rel_offset = (apiaddr - addr - 5) & 0xFFFFFFFF

  idc.PatchDword(addr+1, rel_offset) # Patch “call ResolveApiByHash” -> “call
InternetOpenA”

  idc.PatchByte(addr+5, 0x90) # patch with nop

  idc.PatchByte(addr+6, 0x90) # patch with nop

  curr_ea = addr

  for _ in range(20): # Search maximum the last 20 instructions before the call to
ResolveApiByHash

    prev_instruction_ea = idc.PrevHead(curr_ea)

    instruction = idc.GetDisasm(prev_instruction_ea)

    instruction_cut = instruction.replace(' ', '')

    if 'movecx' in instruction_cut or \

      'movedx' in instruction_cut:

      operand_type = idc.GetOpType(prev_instruction_ea, 1)

      param = idc.GetOperandValue(prev_instruction_ea, 1)

      # Only allow for:

      # 1- General Register

      # 2- Direct Memory reference (DATA)

      # 5- Immediate Value

```

-----

```
      if operand_type not in [1, 2, 5]:

        curr_ea = prev_instruction_ea

        continue

      if param < 0x10:

        if not 'eax' in instruction_cut: # Avoid patching result from
previous calls

          # This means the operand is a register (1-byte mov operation e.g
8B CE mov   ecx, esi)

          # NOP the instruction

          PatchNops(prev_instruction_ea, 2)


      else:

        # This means the operand is 4-byte length (e.g B9 D6 3F B0 78 mov  
ecx, 78B03FD6h)

        PatchNops(prev_instruction_ea, 5)

    curr_ea = prev_instruction_ea


  return True

def PatchNops(addr, size):

  for i in range(size):

    print("Patching NOP at addr: {}".format(hex(addr+i)))

    idc.PatchByte(addr+i, 0x90) #patch with nop


setup("c:\murmurhash2_hashes_dict.json") # Big Python dict. With hashes and export
names

patches = resolve_all_APIs(0x004082D3) # Use address of “ResolveApiByHash”

patch_apicall_wrapper(patches) # Patch the binary


```
With this in place, we can execute the script in a debugging session on Entry Point and
remove most of the code related to Windows API call obfuscation while patching the binary
to get an equivalent working sample easier to analyze and reverse engineer.

The following figure shows the result of patching the binary with our script:


-----

Figure 12. Example of how instructions related to Windows API call obfuscation have been
patched with nops

As we can see, we only have relevant instructions in our disassembly (nop instructions have
patched out Windows API call obfuscation scheme). And, as a result, it is easy for IDA
decompiler now to understand and properly display the arguments for our Windows API calls
in the first attempt:

Figure 13. Example before (up) and after (down) patching the binary with our last script.

Here we have the main networking exfiltration routine before applying any of our scripts and
**after patching the binary. As we can see, the result is a much clear and easy to understand**
routine:


-----

Figure 14. Decompiled view of Network Exfiltration routine without patching the binary

Figure 15. Decompiled view of Network Exfiltration routine after patching the binary with our
script

### Strings Obfuscation


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Figure 16. View of LummaC2 obfuscated strings in the binary

LummaC2 “obfuscates” most of the strings used in the malware in order to evade detection.
By stripping every occurrence of “edx765” from a given string, we can easily get the original
one. Most of these strings are used to walk through sensitive files inside directories.

As it can be seen, the obfuscation method is very simple and for this reason, it is probable
that we see changes in this implementation in future versions of the malware.

**LummaC2 Workflow**

The following diagram shows LummaC2 main workflow. This malware goes straight to the
point and only cares about exfiltrating stolen information. No persistence mechanisms are
used and there is no control on how many malware instances can run at the same time. One
difference regarding many information stealers is that this malware family does not care
about the machine being infected, while others avoid infecting machines coming from the
Commonwealth of Independent States.


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Figure 17. LummaC2 main workflow diagram

**Information Gathering**

LummaC2 gathers information from the victim system. This information is saved in a file
named “System.txt” prior to zip compression and exfiltration. The information gathered
from the infected machine includes the Username, Hardware ID, Screen Resolution and


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more:

Figure 18. System Information gathered from LummaC2

This information is obtained using the following Windows API calls respectively:

GetComputerNameA
GetUserNameA
GetCurrentHwProfileA
GetSystemMetrics
GetSystemDefaultLocaleName
cpuid
GetPhysicallyInstalledSystemMemory

**Steal Important Files**

LummaC2 will also steal files from the victim machine and save them under “Important
Files/Profile”. What happens to be considered here an “important file” is actually every *.txt
file under %userprofile%. This is done in a recursive call that traverses %userprofile% with a
maximum recursion depth of 2 directories.

Figure 19. LummaC2 code responsible for gathering system information and important files

**Targeted Software**

After gathering information from the infected machine and stealing important files, it
proceeds to steal crypto wallets for Binance, Electrum and Ethereum (in this order). Once
this is finished it exfiltrates data (ZIP compressed) to the C2 and continues the stealing
process.

**Crypto Wallets**


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Binance

Electrum

Ethereum

Table 1. LummaC2 targeted Crypto Wallets

After the first exfiltration to the Command and Control server, LummaC2 proceeds to steal
relevant Browsers information like Login Data, History, cookies, etc. Affected Browsers
are:

**Web Browsers**

Chrome

Chromium

Edge

Kometa

Vivaldi

Brave-Browser

Opera Stable

Opera GX Stable

Opera Neon

Mozilla Firefox

Table 2. LummaC2 targeted Web Browsers


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The malware also targets Crypto Wallets and two-factor authentication (2FA) browser
**extensions that may have been installed in the system. The following figure shows**
**LummaC2 searching for these elements:**

Figure 20. LummaC2 targeting Crypto Wallets and two-factor authentication (2FA)
extensions

The following Crypto Wallets and two-factor authentication (2FA) extensions are targeted
in LummaC2:

**Crypto Wallet Extensions**

Metamask BitApp Sollet Nash Extension

TronLink iWlt Auro Hycon Lite Client

Ronnin Wallet Wombat Polymesh ZilPay

Binance Chain Wallet MEW CX ICONex Coin98

Yoroi Guild Nabox Cyano

Nifty Saturn KHC Byone


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Math NeoLine Temple OneKey

Coinbase Clover TezBox Leaf

Guarda Liquality DAppPlay

EQUAL Terra Station BitClip

Jaxx Liberty Keplr Steem Keychain

Table 3. LummaC2 targeted Crypto Wallet extensions

**Two-Factor Authentication (2FA) Extensions**

Authenticator

Authy

EOS Authenticator

GAuth Authenticator

Trezor Password Manager

Table 4. LummaC2 targeted two-factor authentication (2FA) extensions

**Network Data Exfiltration**

Communication with the Command and Control server is one-way only. The malware does
not expect any response from its C2. As we can see from LummaC2 workflow diagram, the
malware contacts the C2 in different stealing phases. After each phase, stolen information is
sent to the C2 ZIP compressed.


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Figure 21. LummaC2 HTTP POST request headers

LummaC2 exfiltrates stolen information via HTTP POST request. These requests are made
to the resource “/c2sock” and use multipart/form-data (combines one or more sets of data
into a single body, separated by boundaries) to upload a compressed ZIP file containing the
stolen information along with more information (like the hardware id, obtained previously with
**GetCurrentHwProfileA). The following figure shows the different set of data sent to the C2:**

Figure 22. LummaC2 multipart/form-data fields used when exfiltrating information

First field (highlighted one) is the filed with name “file”. This includes the ZIP compressed
file with sensitive information being exfiltrated.

The next field with name “hwid” contains the Hardware Id previously retrieved using
**GetCurrentHwProfileA**

The field with name “pid” can be understood as “petition Id”. It is the number of the
exfiltration attempt. As we saw earlier in the workflow diagram, LummaC2 can exfiltrate up to
3 different times after each stealing phase. From the analyzed sample, we can only expect to
see values 1, 2 or 3, which means that if we detect LummaC2 networking activity with
different “petition Id” we will be dealing with a newer/updated version of the malware.

Finally, the last field with name “lid” comes with the hardcoded value
“xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx”. This is known as the “Lumma ID” and it can be
understood as an identifier of the malware that may refer to the build or campaign id. This
field is also used when gathering system information (in the creation of “System.txt” file being
exfiltrated).


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Network data being transmitted to the C2 can be recognized in the following figure, where an
excerpt of the first HTTP POST exfiltration request (request with “pid” equals 1) using
multipart/form-data is sending a ZIP compressed file:

Figure 23. LummaC2 Hex view of HTTP POST request for exfiltration

### MITRE ATT&CK

**Tactic** **Technique ID** **Technique**

Defense Evasion T1140 [Deobfuscate/Decode Files or Information](https://attack.mitre.org/techniques/T1140/)

Defense Evasion T1027 [Obfuscated Files or Information](https://attack.mitre.org/techniques/T1027/)

Credential Access T1539 [Steal Web Session Cookie](https://attack.mitre.org/techniques/T1539/)

Credential Access T1555 [Credentials from Password Stores](https://attack.mitre.org/techniques/T1555/)

Credential Access T1552 [Unsecured Credentials](https://attack.mitre.org/techniques/T1552/)

Discovery T1083 [File and Directory Discovery](https://attack.mitre.org/techniques/T1083/)

Discovery T1082 [System Information Discovery](https://attack.mitre.org/techniques/T1082/)


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Discovery T1033 [System Owner/User Discovery](https://attack.mitre.org/techniques/T1033/)

Collection T1560 [Archive Collected Data](https://attack.mitre.org/techniques/T1560/)

Collection T1119 [Automated Collection](https://attack.mitre.org/techniques/T1119/)

Collection T1005 [Data from Local System](https://attack.mitre.org/techniques/T1005/)

Exfiltration T1041 [Exfiltration over C2 Channel](https://attack.mitre.org/techniques/T1041/)

Exfiltration T1020 [Automated Exfiltration](https://attack.mitre.org/techniques/T1020/)

Command and Control T1071 [Application Layer Protocol](https://attack.mitre.org/techniques/T1071/)

Command and Control T1132 [Data Encoding](https://attack.mitre.org/techniques/T1132/)

Table 5. LummaC2 MITRE ATT&CK matrix

### Conclusion

As it has been shown, LummaC2 behaves similar to other information stealers. By capturing
sensitive data from infected machines, including business credentials, it can do a lot of
damage. For example, compromised credentials can be used to achieve privilege escalation
[and lateral movement. Compromised business accounts can also be used to send spam and](https://outpost24.com/blog/an-analysis-of-a-spam-distribution-botnet)
further distribute the malware.

The fact the malware is being actively used in the wild indicates the professionalization in the
development of these products. Bad actors are willing to pay for these tools because they
prefer quality, and more features. In return, they expect to see more profit from the exfiltrated
data.

Outpost24’s KrakenLabs will continue to analyze new malware samples as part of our Threat
Intelligence solution, which can retrieve compromised credentials in real-time to prevent
unauthorized access to your systems.

### IOCs

**Hash**

LummaC2 sample:


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277d7f450268aeb4e7fe942f70a9df63aa429d703e9400370f0621a438e918bf

**C2**

LummaC2 Command and Control server:

195[.]123[.]226[.]91

### References

“LummaC2 Stealer: A Potent Threat to Crypto Users”, January, 2023. [Online]. Available:
[https://blog.cyble.com/2023/01/06/lummac2-stealer-a-potent-threat-to-crypto-users/](https://blog.cyble.com/2023/01/06/lummac2-stealer-a-potent-threat-to-crypto-users/)

[Accessed March 20, 2023]

“Popularity spikes for information stealer malware on the dark web”, December, 2022.

[Online]. Available: https://www.accenture.com/us-en/blogs/security/information-stealermalware-on-dark-web [Accessed March 20, 2023]


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