LummaC2 stealer: Everything you need to know

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 this blog post, we 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.

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. Advertisements hav been seen 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, two-factor 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 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 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.

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\xE1\x08\x0B\xC8\x0F\xB6\x07\xC1\xE1\x08\x83\xC7\x04\x0B\xC8\x69\xC9\x95\xE9\xD1\x5B\x8B\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\x5B\x8B\xC6\xC1\xE8\x0D\x33\xC6\x69\xC8\x95\xE9\xD1\x5B\x5F\x5E\x8B\xC1\xC1\xE8\x0F\x33\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.

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

Figure 8. Example of result from executing the previous script. Windows API calls are commented now Malware Internals - Figure 9. Cross-Referencing the Windows API resolution routine can help identifying malware capabilities 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:

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

Figure 10. Example before (left) and after (right) the script execution. Binary is patched to call the real function Malware Internals - Figure 11. Example of how arguments cannot be easily recognized at first by IDA decompiler yet

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:

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

Figure 14. Decompiled view of Network Exfiltration routine without patching the binary Malware Internals - Figure 15. Decompiled view of Network Exfiltration routine after patching the binary with our script

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

Strings Obfuscation

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.

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 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

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

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

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
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.

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).

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
Defense Evasion	T1027	Obfuscated Files or Information
Credential Access	T1539	Steal Web Session Cookie
Credential Access	T1555	Credentials from Password Stores
Credential Access	T1552	Unsecured Credentials
Discovery	T1083	File and Directory Discovery
Discovery	T1082	System Information Discovery
Discovery	T1033	System Owner/User Discovery
Collection	T1560	Archive Collected Data
Collection	T1119	Automated Collection
Collection	T1005	Data from Local System
Exfiltration	T1041	Exfiltration over C2 Channel
Exfiltration	T1020	Automated Exfiltration
Command and Control	T1071	Application Layer Protocol
Command and Control	T1132	Data Encoding

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 and 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.

IOCs

Hash

LummaC2 sample:

277d7f450268aeb4e7fe942f70a9df63aa429d703e9400370f0621a438e918bf

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/ [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-stealer-malware-on-dark-web [Accessed March 20, 2023]

LummaC2 stealer: Everything you need to know

Comments

More from this blog

Analyzing LummaC2 stealer’s novel Anti-Sandbox technique: Leveraging trigonometry for human behavior detection

New stealer for sale: LummaC2

Deobfuscating LummaC2

LummaC2 Windows API call Obfuscation

Defeating LummaC2 Windows API call Obfuscation

Preparing Windows API call hash dictionary

Unicorn

Resolving obfuscated Windows API calls

Patching the binary and removing obfuscation

Strings Obfuscation

LummaC2 Workflow

Information Gathering

Steal Important Files

Targeted Software

Network Data Exfiltration

MITRE ATT&CK

Conclusion

IOCs

References

Command Palette

Comments

More from this blog

New stealer for sale: LummaC2

Deobfuscating LummaC2

LummaC2 Windows API call Obfuscation

Defeating LummaC2 Windows API call Obfuscation

Preparing Windows API call hash dictionary

Unicorn

Resolving obfuscated Windows API calls

Patching the binary and removing obfuscation

Strings Obfuscation

LummaC2 Workflow

Information Gathering

Steal Important Files

Targeted Software

Network Data Exfiltration

MITRE ATT&CK

Conclusion

IOCs

References