Shedding light on AceCryptor and its operation

On this blogpost we study the operation of AceCryptor, initially documented by Avast. This cryptor has been round since 2016 and since – all through its existence – it has been used to pack tens of malware households, many technical components of this malware have already been described. You may have already got examine this cryptor, which is variously generally known as the DJVU obfuscation, SmokeLoader’s stage 1, RedLine stealer’s stage 1, 2 and 3, simple and popular packer, and so forth… Many (however not all) of the revealed blogposts don’t even acknowledge this cryptor as a separate malware household, so allow us to join all of the dots for you, offering not solely a technical evaluation of its variants but additionally an summary of the malware households that may be discovered packed by it and the way prevalent AceCryptor is within the wild.

For malware authors, defending their creations towards detection is a difficult job. Cryptors are the primary layer of protection for malware that will get distributed. Though risk actors can create and keep their very own customized cryptors, for crimeware risk actors it usually could also be a time-consuming or technically tough job to take care of their cryptor in a so-called FUD (absolutely undetectable) state. Demand for such safety has created a number of cryptor-as–a-service (CaaS) choices that pack malware. These cryptors can embrace a number of anti-VM, anti-debugging ,and anti-analysis methods mixed to realize concealment of the payload.

Statistics and packed households overview

Because the first identified appearances of AceCryptor again in 2016, many malware authors have used the companies of this cryptor, even the best-known crimeware like Emotet, again when it didn’t use its personal cryptor. Throughout 2021– 2022 ESET detected greater than 80,000 distinctive samples of AceCryptor. Due to the excessive variety of completely different malware households packed inside, we assume that AceCryptor is offered someplace as a CaaS. If we think about the variety of distinctive recordsdata detected: regardless that we don’t know the precise pricing of this service, we assume that the good points to the AceCryptor authors aren’t negligible.

Due to the excessive quantity of samples over previous years, the next stats are primarily based solely on samples detected throughout 2021 and 2022. As could be seen in Determine 1, detection hits have been distributed fairly evenly all through these two years, which is to be anticipated from malware utilized by a lot of risk actors who don’t synchronize their campaigns.

After wanting on the malware packed by AceCryptor, we discovered over 200 ESET detection names. Now, in fact one malware household could have a number of detection names throughout the person variants, due to updates or adjustments in obfuscation – e.g., MSIL/Spy.RedLine.A and MSIL/Spy.RedLine.B are each detections for the RedLine Stealer malware. Detection names for another malware are usually not by household, however by class (e.g., ClipBanker or Agent), as a result of plenty of unpacked malware samples are generic clipboard stealers, trojans, and so forth that aren’t that widespread and/or are simply barely modified variants of different identified malware revealed in varied public repositories. After grouping, we are able to conclude that after unpacking, among the many malware households discovered are SmokeLoader, RedLine Stealer, RanumBot, Raccoon Stealer, STOP ransomware, Amadey, Fareit, Pitou, Tofsee, Taurus, Phobos, Formbook, Danabot, Warzone, and lots of extra… Determine 2 reveals an summary of the portions of samples belonging to a number of the well-known and prevalent malware households packed by  AceCryptor.

Monitoring actions of CaaS suppliers akin to AceCryptor is useful for monitoring of malware that makes use of their companies. For instance, take into account a RedLine Stealer that was first seen in Q1 2022. As could be seen in Determine 3, RedLine Stealer distributors used AceCryptor because the starting of RedLine Stealer’s existence and nonetheless proceed to take action. Thus, with the ability to reliably detect AceCryptor (and different CaaS) not solely helps us with visibility of recent rising threats, but additionally with monitoring the actions of risk actors.

Victimology

As ought to be anticipated from the number of malware packed inside AceCryptor and the range of pursuits of various malware authors, AceCryptor is seen all over the place on this planet. Throughout 2021 and 2022, ESET telemetry detected over 240,000 detection hits of this malware, which quantities to over 10,000 hits each month. In Determine 4 you may see the international locations with the very best numbers of detections throughout 2021 and 2022.

Throughout 2021 and 2022, ESET merchandise detected and blocked malware variants packed by AceCryptor on greater than 80,000 clients’ computer systems. We additionally found over 80,000 distinctive samples of AceCryptor. Now, in fact that any pattern may very well be detected at a number of computer systems or one laptop was protected a number of instances by ESET software program, however the variety of distinctive hashes simply reveals how actively the authors of AceCryptor work on its obfuscation and detection evasion. We are going to dive deeper into the technical particulars of AceCryptor’s obfuscations within the Technical evaluation a part of this blogpost.

What’s value mentioning right here is that regardless that the variety of distinctive samples of AceCryptor may be very excessive, the variety of distinctive samples packed inside is fewer than 7,000. This reveals the diploma to which many malware authors depend on the companies of a cryptor and the way handy it’s for them to pay for this type of service fairly than make investments their time and sources to implement their very own cryptor resolution.

Distribution

As a result of AceCryptor is utilized by a number of risk actors, malware packed by it’s also distributed in a number of other ways. In line with ESET telemetry, gadgets have been uncovered to AceCryptor-packed malware primarily through trojanized installers of pirated software program, or spam emails containing malicious attachments.

One other method that somebody could also be uncovered to AceCryptor-packed malware is through different malware that downloaded new malware protected by AceCryptor. An instance is the Amadey botnet, which we’ve noticed downloading an AceCryptor-packed RedLine Stealer.

We’d like to notice that this works each methods and a number of the malware households protected by AceCryptor may also obtain new, further malware.

Technical evaluation

At the moment AceCryptor makes use of a multistage, three-layer structure. There are two identified variations of the primary layer which might be at the moment in use – a model that makes use of TEA (Tiny Encryption Algorithm) to decrypt the second layer and a model that makes use of a linear congruential generator (LCG) from Microsoft Visible/Fast/C++ to decrypt the second layer. The second layer is shellcode that performs defensive tips, then decrypts and launches the third layer. Lastly, the third layer is extra shellcode that additionally performs some anti-investigation tips, and its job is to launch the payload. There are two identified variations of the third layer: one model performs course of hollowing, whereas the opposite makes use of a reflective loader and overwrites its personal picture with the PE of the ultimate payload.

Layer 1

Though there are two variations of Layer 1, they work very equally. Their essential duties could be summarized as follows:

1. Load encrypted Layer 2 into allotted reminiscence.
2. Decrypt Layer 2.
3. Name or bounce to Layer 2.

A very powerful a part of this stage is the obfuscations. All through the years, new obfuscations have been added – to the purpose the place virtually each a part of the binary is in some way randomized and obfuscated. This may trigger huge issues for somebody attempting to provide you with YARA guidelines or static detections.

Loops

The authors leverage loops for a number of obfuscations. The primary and most easy approach is to make use of loops with junk code simply to make evaluation harder. We have now seen utilization of junk code since 2016 once we registered the primary samples of AceCryptor. These loops are full of many API calls that not solely decelerate analysts who don’t know what is going on, but additionally overwhelm the logs of sandboxes that hook API calls, thereby making them ineffective. The loops could include many MOV directions and math operations, once more simply to confuse analysts and thereby lengthen the time of study.

The second utilization of loops is to achieve delay. We have now noticed that some variations of AceCryptor launch Layer 2 virtually instantly, however others include loops which might be so time demanding that it may well decelerate the execution even for tens of minutes: delaying the execution of some components of malware is a identified approach, however utilization of API calls like Sleep could already increase some flags. Even when not, some sandboxes like Cuckoo Sandbox implement sleep-skipping methods to keep away from the delay and proceed to the attention-grabbing components. Implementing delays through loops and execution of junk code can also be a complication throughout dynamic evaluation, as a result of the analyst has to determine which loops are junk loops and thus could be skipped.

A 3rd obfuscation approach utilizing loops consists of hiding essential operations in them. Among the many junk loops, there are some that watch for a sure iteration and simply throughout that iteration one thing occurs. Normally, an API is loaded utilizing GetProcAddress, which is later used or some fixed just like the offset of the encrypted knowledge is unmasked. If that individual iteration of a loop doesn’t occur, the pattern will later crash. This, together with junk code, signifies that to dynamically analyze the malware, one first has to research which loops or iterations of loops could be skipped, and which can’t. Because of this the analyst can both spend time analyzing junk code or ready till all of the junk code is executed. In Determine 6 you may see two loops the place the primary incorporates an operation essential for subsequent execution, and the opposite is simply filled with junk code. After all, this is probably not (and it’s not within the majority of the samples) that simply seen amongst all of the loops, particularly if the loops with the essential operations additionally include junk code.

Randomization – Thou shalt not YARA

One other essential a part of the primary layer is randomization. Junk code and the loops talked about beforehand are randomized in every pattern, in such a method that:

• the variety of iterations adjustments,
• API calls change,
• the variety of API calls change, and
• junk arithmetic or MOV directions change.

All this randomization may also fairly complicate identification of the decryption algorithm and keys. In Determine 7 and Determine 8 you may see the unique, unobfuscated and the obfuscated model of the TEA algorithm. Within the obfuscated model there are usually not solely junk arithmetic directions, but additionally some components of the algorithm are outlined into subroutines and identified constants (sum and delta in Determine 7) are masked, simply to make right identification of the algorithm unlikely or actually harder.

Code shouldn’t be the one factor that’s randomized. The encrypted Layer 2 and its decryption key are at the moment normally saved within the .textual content or .knowledge part, however they’re hidden utilizing some offsets that change between the samples. Additionally, after efficiently decrypting Layer 2: in some samples the code of Layer 2 is firstly of the decrypted knowledge, however there are samples the place you find yourself with a block of random knowledge firstly and you could know the proper offset to search out the start of Layer 2’s code.

AceCryptor authors additionally randomize the next traits:

• The PDB path all the time begins with C:, however the remainder of the trail is random.
• Sources with random names and content material, as could be seen in Determine 9. The authors of AceCryptor fill samples with randomly generated sources containing randomly generated knowledge. We assume that that is achieved to make samples much less suspicious and make finding the precise encrypted knowledge harder. Sources can include:
  • String tables
  • Menus
  • Bitmaps
  • Binary knowledge
• Strings used within the code.
• Icons – regardless that icons which might be utilized in many samples look related, they’re simply barely modified/randomized to be distinctive.
• Random dummy part names.
• Reminiscence allocation features for Layer 2 knowledge – GlobalAlloc, LocalAlloc, and VirtualAlloc.
• Utilization of some APIs essential to code execution – they could be statically imported or obtained through GetModuleHandleA and GetProcAddress.

Earlier variations

Over time, the authors of AceCryptor received more adept at creating malware and the cryptor modified and developed. Though there have been many smaller adjustments, updates, and enhancements, a number of the attention-grabbing options of the older variations of Layer 1 included the next:

• Throughout 2016 AceCryptor used a model of Layer 1 with XTEA encryption algorithm.
• Throughout 2017–2018 AceCryptor used yet one more Layer 1 model, the place the encryption algorithm used was RC4.
• The primary (X)TEA and LCG variations of Layer 1 appeared in 2016. In contrast to the LCG model, the XTEA model shortly fell into disuse and was changed with the TEA model.
• In older variations, the encrypted Layer 2 was within the sources hidden in a BMP picture. This picture was randomly generated with random width and peak, and the center of the picture was minimize out and changed with encrypted knowledge. Knowledge needed to be discovered on the right offset.

Layer 2

Layer 2 of AceCryptor appeared in 2019. Till then, AceCryptor launched Layer 3 straight from Layer 1. This layer serves as further encryption and safety of Layer 3 and, as Determine 11 illustrates, consists of three components:

• position-independent code,
• a customized construction that we named L2_INFO_STRUCT, containing details about Layer 3, and
• the information of Layer 3

As step one, AceCryptor makes use of a standard approach to acquire some API perform addresses. It resolves the GetProcAddress and LoadLibraryA features through the use of the PEB_LDR_DATA to traverse by way of loaded modules, and by evaluating the hash values of their export names towards hardcoded values. As a checksum perform, AceCryptor makes use of a shl1_add perform, already carried out in hashDb, which might make identification of resolved APIs sooner.

Determine 12. shl1_add hash implemented in Python

Then AceCryptor obtains a deal with for kernel32.dll utilizing LoadLibraryA and makes use of that and GetProcAddress to resolve extra APIs.

For the following steps, AceCryptor makes use of info from its customized construction L2_INFO_STRUCT (proven in Determine 13), which could be discovered proper on the finish of the position-independent code, as could be seen in Determine 11.

Determine 13. Overview of the L2_INFO_STRUCT from Layer 2

Within the subsequent steps, AceCryptor decrypts Layer 3, which is encrypted utilizing LCG from Microsoft Visible/QuickC/C++. Decryption occurs in place. If the compressionFlag is about, AceCryptor allocates reminiscence with the VirtualAlloc API and decompresses the decrypted knowledge with the LZO_1Z decompression algorithm. After this, execution jumps into the decrypted and optionally decompressed Layer 3.

Layer 3 – Course of hollowing

As step one, AceCryptor obtains the addresses of LoadLibraryA and GetProcAddress APIs the identical method as in ` 2 – traverse loaded modules, traverse exports, and use shl1_add checksums. Then AceCryptor obtains a number of API perform addresses and DLL handles.

Within the subsequent step, AceCryptor makes use of the API GetFileAttributesA and checks for file system attributes of a file referred to as apfHQ. Attributes are in comparison with a non-existing combination of flags 0x637ADF and if they’re equal, this system will find yourself in an infinite loop. As a result of that is used within the final layer, which is already properly hidden, and since this isn’t the one trick right here, we assume that this isn’t one other obfuscation approach, however fairly an undocumented anti-sandbox/anti-emulator trick towards an unknown however particular sandbox/emulator that returns this worth.

If this system continues efficiently, there’s yet one more anti-sandbox/anti-emulator test. Now AceCryptor makes use of the API RegisterClassExA to register a category with the category title saodkfnosa9uin. Then it tries to create a window with the title mfoaskdfnoa utilizing the CreateWindowExA API. Within the final step of this test, AceCryptor tries to make use of the APIs PostMessageA and GetMessageA to cross a message. As a result of these APIs are usually not used that often, this test helps to dodge sandboxes/emulators that haven’t carried out these APIs or the place the emulated APIs don’t perform correctly.

After passing these checks efficiently, AceCryptor makes use of the process hollowing technique the place it creates a brand new occasion of the present course of (GetCommandLineA, CreateProcessA), maps the ultimate payload into the newly created course of, and launches it.

Earlier variations

Anti-investigation trick utilizing RegisterClassExA, CreateWindowExA, PostMessageA, GetMessageA was in earlier variations (e.g., SHA-1: 01906C1B73ECFFD72F98E729D8EDEDD8A716B7E3) seen used at Layer 1 and later (when it was examined out and the structure of the cryptor developed) it was moved to Layer 3.

Layer 3 – Reflective loader

The primary stephis layer, just like Layer 2 and Layer 3 – Course of hollowing, obtains addresses of the GetProcAddress and LoadLibraryA API features. The distinction is that this time, for some purpose, the authors didn’t use the shl1_add checksum perform, however they get hold of first the GetProcAddress through traversing loaded modules, traversing exports, and evaluating strings. Then utilizing GetProcAddress they get hold of the LoadLibraryA perform. Utilizing these two APIs, AceCryptor hundreds addresses of some extra API features and a deal with to kernel32.dll.

Within the code, there’s a trick (proven in Determine 17) the place AceCryptor mixes code with knowledge. AceCryptor controls a worth that’s on return handle after one name. This worth is by default set to zero and later AceCryptor writes there an handle of the entry level of the ultimate payload. If this system will get patched and the worth is about to a non-zero worth, this system will bounce to the handle pointed to by that worth and crash.

Within the subsequent step, AceCryptor performs a identified anti-VM check aimed towards Cuckoo sandbox, IDA Professional+Bochs, and Norman SandBox. In Determine 19 could be seen that flag SEM_NOALIGNMENTFAULTEXCEPT with the worth 0x04 all the time will get set by the Cuckoo sandbox, and due to that, the second name of SetErrorMode within the code from Determine 18 received’t return the identical worth because the one which was set by the earlier name.

Within the final steps, AceCryptor first checks if the ultimate payload has been compressed (once more) and in that case, it makes use of LZO_1Z decompression. Just like Layer 2, the Layer 3 reflective loader makes use of a customized construction, which we named ENCRYPTED_DATA_INFO_STRUCT (Determine 16), that may be discovered proper between the position-independent code and ultimate payload, containing info like compression flag, variety of sections of payload, (de)compressed dimension of payload, entry level handle, addresses of some directories, picture relocation desk handle, and so forth. AceCryptor makes use of this info (in contrast to Layer 3 – Course of hollowing, which parses the PE of the ultimate payload) to do a reflective code loading approach the place it remaps (map sections, rebase picture, …) its personal picture with the picture of the ultimate payload and launches the payload by calling its entry level.

Conclusion

AceCryptor is a long-lasting and prevalent cryptor-malware, distributed all around the globe. We anticipate that it’s offered someplace on darkish internet/underground boards as a CaaS. Providers of this malware have been utilized by tens of various malware households and lots of of them depend on this cryptor as their essential safety towards static detections.

Because the malware is utilized by many risk actors, anybody could be affected. Due to the range of packed malware, it’s tough to estimate how extreme the results are for a compromised sufferer. AceCryptor could have been dropped by different malware, already working on a sufferer’s machine, or if sufferer received straight bothered by, for instance, opening a malicious e-mail attachment, any malware inside might need downloaded further malware; thus it might be very tough to wash the compromised machine.

Though for now an attribution of AceCryptor to a specific risk actor shouldn’t be potential and we anticipate that AceCryptor will proceed to be extensively used, nearer monitoring will assist with prevention and discovery of recent campaigns of malware households full of this cryptor.

IOCs

Information

Observe: Listed recordsdata are an affordable choice of samples all through time, overlaying completely different variations of AceCryptor or packing completely different malware.

SHA-1	Filename	ESET detection title	Description
0BE8F44F5351A6CBEF1A54A6DE7674E1219D65B6	N/A	Win32/Kryptik.HPKJ	TEA model of Layer 1, with SmokeLoader packed inside.
0BE56A8C0D0DE11E0E97B563CAE6D1EE164F3317	N/A	Win32/Kryptik.GOFF	LCG model of Layer 1, with SmokeLoader packed inside, anti-investigation trick on Layer 2.
1E3D4230655411CB5F7C6885D7F947072B8F9F0F	N/A	Win32/Emotet.AW	RC4 model of Layer 1, with Emotet packed inside.
2FDD49A3F7D06FFFD32B40D35ABD69DEC851EB77	N/A	Win32/Smokeloader.F	TEA model of Layer 1, with SmokeLoader packed inside.
3AC205BE62806A90072524C193B731A1423D5DFD	N/A	Win32/Kryptik.GPCG	TEA model of Layer 1, with SmokeLoader packed inside.
6ABF731B90C11FFBD3406AA6B89261CC9596FEFD	N/A	Win32/Kryptik.HRHP	TEA model of Layer 1, with RedLine stealer packed inside.
8E99A5EC8C173033941F5E00A3FC38B7DEA9DCB3	N/A	Win32/Kryptik.FKYH	TEA model of Layer 1, with Filecoder.Q packed inside, subsequent layer in BMP picture.
15ADFFDA49C07946D4BD41AB44846EB673C22B2B	N/A	WinGo/RanumBot.B	TEA model of Layer 1, with RanumBot packed inside, obfuscation – random PDB path.
47DB52AB94B9A303E85ED1AA1DD949605157417E	N/A	Win32/Smokeloader.A	TEA model of Layer 1, with SmokeLoader packed inside, anti-emulator trick on Layer 1.
70BC8C2DC62CF894E765950DE60EC5BD2128D55B	N/A	Win32/Smokeloader.F	TEA model of Layer 1, with SmokeLoader packed inside.
88B125DDA928462FDB00C459131B232A3CD21887	N/A	Win32/Kryptik.GDTA	TEA model of Layer 1, with Hermes packed inside, obfuscation – masking values.
90A443787B464877AD9EB57536F51556B5BA8117	N/A	Win32/Kovter.C	XTEA model of Layer 1, with Kovter packed inside.
249BED77C1349BE7EC1FC182AFCCB1234ADFACDF	N/A	Win32/Smokeloader.F	TEA model of Layer 1, with SmokeLoader packed inside.
3101B17D73031384F555AE3ACD7139BBBAB3F525	N/A	Win32/TrojanDownloader.Amadey.A	TEA model of Layer 1, with Amadey packed inside.
8946E40255B57E95BAB041687A2F0F0E15F5FFCE	N/A	Win32/Kryptik.GKIN	LCG model of Layer 1, with GandCrab packed inside, obfuscation – named sections.
946082F225C76F2FFBE92235F0FAF9FB9E33B784	N/A	Win32/Filecoder.Locky.C	LCG model of Layer 1, with Locky packed inside.
A8ACF307EA747B24D7C405DEEF70B50B2B3F2186	N/A	MSIL/Spy.RedLine.B	LCG model of Layer 1, with RedLine Stealer packed inside.
F8039D04FF310CEF9CA47AC03025BD38A3587D10	N/A	Win32/Smokeloader.F	TEA model of Layer 1, with SmokeLoader packed inside.

Named objects

Object Kind	Object title
Class	saodkfnosa9uin
Window	mfoaskdfnoa

MITRE ATT&CK methods

This desk was constructed utilizing version 12 of the MITRE ATT&CK enterprise methods.

Tactic	ID	Title	Description
Execution	T1106	Native API	AceCryptor is ready to launch a course of utilizing the CreateProcessA API.
Protection Evasion	T1497.003	Virtualization/Sandbox Evasion: Time Based mostly Evasion	AceCryptor makes use of loops with arbitrary code to delay the execution of core performance.
	T1497.001	Virtualization/Sandbox Evasion: System Checks	AceCryptor makes use of a number of methods to detect sandboxes and emulators.
	T1140	Deobfuscate/Decode Information or Info	AceCryptor makes use of TEA, LCG, XTEA, or RC4 encryption and LZO_1Z compression to extract position-independent code and payloads.
	T1027	Obfuscated Information or Info	AceCryptor masks values like size of payload, identified constants of decryption algorithms, or decryption key.
	T1055.012	Course of Injection: Course of Hollowing	AceCryptor can create a brand new course of in a suspended state to unmap its reminiscence and exchange it with the hidden payload.
	T1620	Reflective Code Loading	AceCryptor can use a reflective loader to rewrite its picture and exchange it with a hidden payload (Home windows PE).

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