July 11, 2024

GuLoader: Evolving Tactics in Latest Campaign Targeting European Industry

Cado Security Labs identified a GuLoader campaign targeting European industrial companies via spearphishing emails with compressed batch files. This malware uses obfuscated PowerShell scripts and shellcode with anti-debugging techniques to establish persistence and inject into legitimate processes, to deliver Remote Access Trojans. GuLoader's ongoing evolution highlights the need for robust security.

Written by

Tara Gould

Malware Research Lead

Inside the SOC

Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.

Written by

Tara Gould

Malware Research Lead

Jul 2024

Introduction: GuLoader

Researchers from Cado Security Labs (now part of Darktrace) recently discovered a campaign targeting European industrial and engineering companies. GuLoader is an evasive shellcode downloader used to deliver Remote Access Trojans (RAT) that has been used by threat actors since 2019 and continues to advance. 

Initial access

Cado identified a number of spearphishing emails sent to electronic manufacturing, engineering and industrial companies in European countries including Romania, Poland, Germany and Kazakhstan. The emails typically include order inquiries and contain an archive file attachment (iso, 7z, gzip, rar). The emails are sent from various email addresses including from fake companies and compromised accounts. The emails typically hijack an existing email thread or request information about an order. 

PowerShell  

The first stage of GuLoader is a batch file that is compressed in the archive from the email attachment. As shown in Image 2, the batch file contains an obfuscated PowerShell script, which is done to evade detection.

The obfuscated script contains strings that are deobfuscated through a function “Boendes” (in this sample) that contains a for loop that takes every fifth character, with the rest of the characters being junk. After deobfuscating, the functionality of the script is clearer. These values can be retrieved by debugging the script, however deobfuscating with Script 1 in the Scripts section, makes it easier to read for static analysis.

Deobfuscated Powershell — Figure 3 - Deobfuscated PowerShell

This Powershell script contains the function “Aromastofs” that is used to invoke the provided expressions. A secondary file is downloaded from careerfinder[.]ro and saved as “Knighting.Pro” in the user’s AppData/Roaming folder. The content retrieved from “Kighting.Pro” is decoded from Base64, converted to ASCII and selected from position 324537, with the length 29555. This is stored as “$Nongalactic” and contains more Powershell. 

Second Powershell script — Figure 4 - Second PowerShell script

Deobfuscated Secondary Powershell — Figure 5 - Deobfuscated Secondary PowerShell

As seen in Image 5, the secondary PowerShell is obfuscated in the same manner as before with the function “Boendes”. The script begins with checking which PowerShell is being used 32 or 64 bit. If 64 bit is in use, a 32 bit PowerShell process is spawned to execute the script, and to enable 32 bit processes later in the chain. 

The function named “Brevsprkkernes” is a secondary obfuscation function. The function takes the obfuscated hex string, converts to a byte array, applies XOR with a key of 173 and converts to ASCII. This obfuscation is used to evade detection and analysis more difficult. Again, these values can be retrieved with debugging; however for readability, using Script 2 in the Scripts section makes it easier to read. 

Deobfuscated PowersShell Strings — Figure 7 - Deobfuscated PowerShell Strings

Figure 8: Deobfuscated Process Injection

The second PowerShell script contains functionality to allocate memory via VirtualAlloc and to execute shellcode. VirtualAlloc is a native Windows API function that allows programs to allocate, reserve, or commit memory in a specified process. Threat actors commonly use VirtualAlloc to allocate memory for malicious code execution, making it harder for security solutions to detect or prevent code injection. The variable “$Bakteriekulturs” contains the bytes that were stored in “AppData/Roaming/Knighting.Pro” and converted from Base64 in the first part of the PowerShell Script. Marshall::Copy is used to copy the first 657 bytes of that file, which is the first shellcode. Marshall.Copy is a method that enables the transfer of data between unmanaged memory and managed arrays, allowing data exchange between managed and unmanaged code. Marshal.Copy is typically abused to inject or manipulate malicious payloads in memory, bypassing traditional detection by directly accessing and modifying memory regions used by applications. Marshall::Copy is used again to copy bytes 657 to 323880 as a second shellcode. 

The first shellcode includes multiple anti-debugging techniques that make static and dynamic analysis difficult. There have been multiple evolutions of GuLoader’s evasive techniques that have been documented [1]. The main functionality of the first shellcode is to load and decrypt the second shellcode. The second shellcode adds the original PowerShell script as a Registry Key “Mannas” in HKCU/Software/Procentagiveless for persistence, with the path to PowerShell 32 bit executable stored as “Frenetic” in HKCU\Environment; however, these values change per sample. 

Figure 10 - Registry Key created for PowerShell Script

PowerShell bit added to Registry — Figure 11 - PowerShell 32 bit added to Registry

The second shellcode is injected into the legitimate “msiexec.exe” process and appears to be reaching out to a domain to retrieve an additional payload, however at the time of analysis this request returns a 404. Based on previous research of GuLoader, the final payload is usually a RAT including Remcos, NetWire, and AgentTesla.[2]

Figure 12  - msiexec abused to retrieve additional payload

Key Takeaway

Guloader malware continues to adapt its techniques to evade detection to deliver RATs. Threat actors are continually targeting specific industries in certain countries. Its resilience highlights the need for proactive security measures. To counter Guloader and other threats, organizations must stay vigilant and employ a robust security plan.

Scripts

Script 1 to deobfuscate junk characters 

import re 
import argparse 
import os 
 
def deobfuscate_powershell(input_file, output_file): 
  try: 
      with open(input_file, 'r', encoding='utf-8') as f: 
          text = f.read() 
 
      function_name_match = re.search(r"function\s+(\w+)\s*\(", text) 
      if not function_name_match: 
          print("Could not find the obfuscation function name in the file.") 
          return 
      
      function_name = function_name_match.group(1) 
      print(f"Detected obfuscation function name: {function_name}") 
 
      obfuscated_pattern = rf"(?<={function_name} ')(.*?)(?=')" 
      matches = re.findall(obfuscated_pattern, text) 
 
      for match in matches: 
          deobfuscated = match[4::5] 
          full_obfuscated_call = f"{function_name} '{match}'" 
          text = text.replace(full_obfuscated_call, deobfuscated) 
 
      with open(output_file, 'w', encoding='utf-8') as f: 
          f.write(text) 
 
      print(f"Deobfuscation complete. Output saved to {output_file}") 
 
  except Exception as e: 
      print(f"An error occurred!: {e}") 
 
if __name__ == "__main__": 
  parser = argparse.ArgumentParser(description="Deobfuscate an obfuscated PowerShell file.") 
  parser.add_argument("input_file", help="Path to the obfuscated PowerShell file.") 
  parser.add_argument("output_file", nargs='?', help="Path to save the deobfuscated file. Default is 'deobfuscated_powershell.ps1' in the same directory.", default=None) 
 
  args = parser.parse_args() 
 
  if args.output_file is None: 
      output_file = os.path.splitext(args.input_file)[0] + "_deobfuscated.ps1" 
  else: 
      output_file = args.output_file 
 
  deobfuscate_powershell(args.input_file, output_file)

Script 2 to deobfuscate hex strings obfuscation (note this will need values changed based on sample)

import re 
import argparse 
 
def brevsprkkernes(spackle): 
  if not all(c in '0123456789abcdefABCDEF' for c in spackle): 
      return f"Invalid hex: {spackle}" 
  
  paronomasian = 2 
  polyurethane = bytearray(len(spackle) // 2) 
 
  for forstyrrets in range(0, len(spackle), paronomasian): 
      try: 
          polyurethane[forstyrrets // 2] = int(spackle[forstyrrets:forstyrrets + 2], 16) 
          polyurethane[forstyrrets // paronomasian] ^= 173 
      except ValueError: 
          return f"Error processing hex: {spackle}" 
 
  return polyurethane.decode('ascii', errors='ignore') 
 
def process_file(input_file, output_file): 
  with open(input_file, 'r') as infile: 
      content = infile.read() 
 
  def replace_function(match): 
      hex_string = match.group(1).strip() 
      result = brevsprkkernes(hex_string) 
      return f"Brevsprkkernes '{result}'" 
 
  updated_content = re.sub(r"Brevsprkkernes\s*['\"]?([0-9A-Fa-f]+)['\"]?", replace_function, content) 
 
  with open(output_file, 'w') as outfile: 
      outfile.write(updated_content) 
 
if __name__ == "__main__": 
  parser = argparse.ArgumentParser(description="Process a PowerShell file and replace hex strings.") 
  parser.add_argument("input_file", help="Path to the input file.") 
  parser.add_argument("output_file", help="Path to save the deobufuscated file.") 
  
  args = parser.parse_args() 
 
  process_file(args.input_file, args.output_file)

Indicators of compromise (IoCs)

GuLoader scripts

ZW_PCCE-010023024001.bat 36a9a24404963678edab15248ca95a4065bdc6a84e32fcb7a2387c3198641374

ORDER_1ST.bat 26500af5772702324f07c58b04ff703958e7e0b57493276ba91c8fa87b7794ff

IMG465244247443 GULF ORDER Opmagasinering.cmd 40b46bae5cca53c55f7b7f941b0a02aeb5ef5150d9eff7258c48f92de5435216

EXSP 5634 HISP9005 ST MSDS DOKUME74247linierelet.bat e0d9ebe414aca4f6d28b0f1631a969f9190b6fb2cf5599b99ccfc6b7916ed8b3

LTEXSP 5634 HISP9005 ST MSDS DOKUME74247liniereletbrunkagerne.bat 4c697bdcbe64036ba8a79e587462960e856a37e3b8c94f9b3e7875aeb2f91959

Quotation_final_buy_order_list_2024_po_nos_ART125673211020240000000000024.bat661f5870a5d8675719b95f123fa27c46bfcedd45001ce3479a9252b653940540

MEC20241022001.bat 33ed102236533c8b01a224bd5ffb220cecc32900285d2984d4e41803f1b2b58d

nMEC20241022001.iso 9617fa7894af55085e09a06b1b91488af37b8159b22616dfd5c74e6b9a081739

Gescanneerde lijst met artikelen nr. 654398.bat f5feabf1c367774dc162c3e29b88bf32e48b997a318e8dd03a081d7bfe6d3eb5

DHL_Shipping_Invoices_Awb_BL_000000000102220242247820020031808174Global180030010222024.cmd f78319fcb16312d69c6d2e42689254dff3cb875315f7b2111f5c3d2b4947ab50

Order Confirmation.bat 949cdd89ed5fb2da03c53b0e724a4d97c898c62995e03c48cbd8456502e39e57

SKM_0001810-01-2024-GL-3762.bat 9493ad437ea4b55629ee0a8d18141977c2632de42349a995730112727549f40e

21102024_0029_18102024_SKM_0001810-01-2024-GL-3762.iso 535dd8d9554487f66050e2f751c9f9681dadae795120bb33c3db9f71aafb472c

\Device\CdRom1\MARSS-FILTRY_ZW015010024.BAT e5ebe4d8925853fc1f233a5a6f7aa29fd8a7fa3a8ad27471c7d525a70f4461b6

Myologist.cmd 51244e77587847280079e7db8cfdff143a16772fb465285b9098558b266c6b3f

SKU_0001710-1-2024-SX-3762.bat 643cd5ba1ac50f5aa2a4c852b902152ffc61916dc39bd162f20283a0ecef39fe

Stamcafeernes.cmd 54b8b9c01ce6f58eb6314c67f3acb32d7c3c96e70c10b9d35effabb7e227952e

C:\Users\user\AppData\Local\Temp\j4phhdbc.lti\Bank details Form.bat c1f810194395ff53044e3ef87829f6dff63a283c568be4a83088483b6c043ec8

SKGCRO COMANDA FAB SRL M60_647746748846748347474.bat 8dd5fd174ee703a43ab5084fdaba84d074152e46b84d588bf63f9d5cd2f673d1

DHL_Shipping_Invoices_Awb_BL_000000000101620242247820020031808174Global180030010162024.bat bde5f995304e327d522291bf9886c987223a51a299b80ab62229fcc5e9d09f62

Ciwies.cmd b1be65efa06eb610ae0426ba7ac7f534dcb3090cd763dc8642ca0ede7a339ce7

Zamówienie Agotech Begyndelsesord.cmd 18c0a772f0142bc8e5fb0c8931c0ba4c9e680ff97d7ceb8c496f68dea376f9da

SKM_0001810-01-2024-GL-3762.iso 4a4c0918bdacd60e792a814ddacc5dc7edb83644268611313cb9b453991ac628

C:\Users\user\AppData\Local\Temp\Stemmeslugerens.bat 8bedbdaa09eefac7845278d83a08b17249913e484575be3a9c61cf6c70837fd2

Agotech Zamówienie Fjeldkammes325545235562377.bat ff6c4c8d899df66b551c84124e73c1f3ffa04a4d348940f983cf73b2709895d3

Agotech Zamówienie Fjeldkammes3255452355623.bat f3e046a7769b9c977053dd32ebc1b0e1bbfe3c61789d2b8d54e51083c3d0bed5

SKU_0001710-1-2024-SX-3762.iso 0546b035a94953d33a5c6d04bdc9521b49b2a98a51d38481b1f35667f5449326

SKU_0001710-1-2024-SX-3762.bat 4f1b5d4bb6d0a7227948fb7ebb7765f3eb4b26288b52356453b74ea530111520

DOKUMENTEN_TOBIAS.bat 038113f802ef095d8036e86e5c6b2cb8bc1529e18f34828bcf5f99b4cc012d6a

IMEG238668289485293885823085802835025Urfjeld.bat 6977043d30d8c1c5024669115590b8fd154905e01ab1f2832b2408d1dc811164

SKM_C250i24100408500.iso 6370cbcb1ac3941321f93dd0939d5daba0658fb8c85c732a6022cc0ec8f0f082

SKU_0001710-1-2024-SX-3762.iso 7f06382b781a8ba0d3f46614f8463f8857f0ade67e0f77606b8d918909ad37c2

\Device\CdRom1\ORDINE ELECTRICAS BC CORP PO EDC0969388.BAT e98fa3828fa02209415640c41194875c1496bc6f0ca15902479b012243d37c47

Quote Request #2359 Bogota.msg 0f0dfe8c5085924e5ab722fa01ea182569872532a6162547a2e87a1d2780f902

ORDER.1ST.bat 48dca5f3a12d3952531b05b556c30accafbf9a3c6cda3ec517e4700d5845ab61

Fortryl105.cmd f43b78e4dc3cba2ee9c6f0f764f97841c43419059691d670ca930ce84fb7143b

SMX-0002607-1-2024-UP-3762.iso a60dbbe88a1c4857f009a3c06a2641332d41dfd89726dd5f2c6e500f7b25b751

Quotation_final_buy_order_list_2024_po_nos_ART1256731610202400000000000.cmd efd80337104f2acde5c8f3820549110ad40f1aa9b494da9a356938103bda82e7

a60dbbe88a1c4857f009a3c06a2641332d41dfd89726dd5f2c6e500f7b25b751.iso 0327db7b754a16a7ae29265e7d8daed7a1caa4920d5151d779e96cd1536f2fbe

MARSS-FILTRY_ZW015010024.iso c415127bde80302a851240a169fff0592e864d2f93e9a21c7fd775fdb4788145

SKM_C250i24100408500.bat 36c464519a4cce8d0fcdb22a8974923fd51d915075eba9e62ade54a9c396844d

UPM-0002607-1-2024-UP-3762.iso e9fc754844df1a7196a001ac3dfbcf28b80397a718a3ceb8d397378a6375ff62

Comanda KOMARON TRADE SRL 435635Lukketid.bat 1bf09bcb5bfa440fc6ce5c1d3f310fb274737248bf9acdd28bea98c9163a745a

311861751714730477170144.bat f87448d722e160584e40feaad0769e170056a21588679094f7d58879cdb23623

Estimate_buy_product_purchase_order_import_list_10_10_2024_000000101024.cmd f20670ed0cdc2d9a2a75884548e6e6a3857bbf66cfbfb4afe04a3354da9067c9

PAYMENT TERM.bat 4c90504c86f1e77b0a75a1c7408adf1144f2a0e3661c20f2bf28d168e3408429

Arbitrre.cmd 8ef4cb5ad7d5053c031690b9d04d64ba5d0d90f7bf8ba5e74cb169b5388e92c5

KZЗапрос продукта SKM_32532667622352352Arvehygiejnikernes.bat 4ddd3369a51621b0009b6d993126fcb74b52e72f8cacd71fcbc401cda03108cb

Order_AP568.bat fda4e04894089be87f520144d8a6141074d63d33b29beb28fd042b0ecc06fbbc

C:\Users\user\Documents\ConnectWiseControl\Temp\Blodprocenternes.cmd e5f5d9855be34b44ad4c9b1c5722d1a6dff2f4a6878a874df1209d813aea7094

Productivenesses.cmd a7268e906b86f7c1bb926278bf88811cb12189de0db42616e5bbb3dc426a4ef5

Doktriner.cmd 74d468acd0493a6c5d72387c8e225cc0243ae1a331cd1e2d38f75ed8812347dd

final_buy_product_purchase_order_import_list_11_10_2024_000000111024.cmd a2127d63bc0204c17d4657e5ae6930cab6ab33ae3e65b82e285a8757f39c4da9

ORDER_U769.bat b45d9b5dbe09b2ca45d66432925842b0f698c9d269d3c7b5148cc26bdc2a92d0

Beschwerde-Rechtsanwalt.bat 229c4ce294708561801b16eed5a155c8cfe8c965ea99ac3cfb4717a35a1492f3

upit nr5634 10_08_2024.cmd 5854d9536371389fb0f1152ebc1479266d36ec4e06b174619502a6db1b593d71

C:\Users\user\AppData\Local\Temp\Doktriner.cmd 140dcb39308d044e3e90610c65a08e0abc6a3ac22f0c9797971f0c652bb29add

Fedtsyresammenstning.cmd 0b1c44b202ede2e731b2d9ee64c2ce333764fbff17273af831576a09fc9debfa

HENIKENPLANT PROJECT PROPOSAL BID_24-0976·pdf.cmd 31a72d94b14bf63b07d66d023ced28092b9253c92b6e68397469d092c2ffb4a6

MAIN ORDER.bat 85d1877ceda7c04125ca6383228ee158062301ae2b4e4a4a698ef8ed94165c7c

Narudzba ACH0036173.bat 8d7324d66484383eba389bc2a8a6d4e9c4cb68bfec45d887b7766573a306af68

Sludger.cmd 45b7b8772d9fe59d7df359468e3510df1c914af41bd122eeb5a408d045399a14

Glasmester.bat b0e69f895f7b0bc859df7536d78c2983d7ed0ac1d66c243f44793e57d346049d

PERMINTAAN ANGGARAN (Universitas IPB) ID177888·pdf.cmd 09a3bb4be0a502684bd37135a9e2cbaa3ea0140a208af680f7019811b37d28d6

C:\Users\user\Documents\ConnectWiseControl\Temp\Bidcock.cmd 0996e7b37e8b41ff0799996dd96b5a72e8237d746c81e02278d84aa4e7e8534e

PO++380.101483.bat a9af33c8a9050ee6d9fe8ce79d734d7f28ebf36f31ad8ee109f9e3f992a8d110

Network IOCs

91[.]109.20.161

137[.]184.191.215

185[.]248.196.6

hxxps://filedn[.]com/lK8iuOs2ybqy4Dz6sat9kSz/Frihandelsaftalen40.fla

hxxps://careerfinder[.]ro/vn/Traurigheder[.]sea

hxxp://inversionesevza[.]com/wp-includes/blocks_/Dekupere.pcz

hxxps://rareseeds[.]zendesk[.]com/attachments/token/G9SQnykXWFAnrmBcy8MzhciEs/?name=PO++380.101483.bat

Detection

Yara rule

rule GuLoader_Obfuscated_Powershell 
{ 
   meta: 
       description = "Detects Obfuscated GuLoader Powershell Scripts" 
       author = "[email protected]" 
       date = "2024-10-14" 
   strings: 
      $hidden_window = { 7374617274202f6d696e20706f7765727368656c6c2e657865202d77696e646f777374796c652068696464656e2022 } 
      $for_loop = /for\s*\(\s*\$[a-zA-Z0-9_]+\s*=\s*\d+;\s*\$[a-zA-Z0-9_]+\s*-lt\s*\$[a-zA-Z0-9_]+\s*;\s*\$[a-zA-Z0-9_]+\s*\+=\s*\d+\s*\)/ 
   condition: 
      $for_loop and $hidden_window

MITRE ATT&CK

T1566.001 Phishing: Malicious Attachment

T1055 Process Injection

T1204.002 User Execution: Malicious File

T1547.001 Boot or Logon Autostart Execution: Registry Run Keys / Startup Folder

T1140 Deobfuscate/Decode Files or Information

T1622 Debugger Evasion

T1001.001 Junk Code

T1105 Ingress Tool Transfer

T1059.001 Command and Scripting Interpreter: Powershell

T1497.003  Virtualization/Sandbox Evasion: Time Based Evasion

T1071.001 Application Layer Protocol: Web Protocols

References:

[1] https://www.crowdstrike.com/en-us/blog/guloader-dissection-reveals-new-anti-analysis-techniques-and-code-injection-redundancy/

[2] https://www.checkpoint.com/cyber-hub/threat-prevention/what-is-malware/guloader-malware/

‍

Written by

Tara Gould

Malware Research Lead

Inside the SOC

Written by

Tara Gould

Malware Research Lead

VP, Security & AI Strategy, Field CISO

•

May 1, 2026

Kiri Addison

Senior Director of Product

Watch the NIS2 Webinar

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Network

May 6, 2026

When Trust Becomes the Attack Surface: Supply-Chain Attacks in an Era of Automation and Implicit Trust

Software supply-chain attacks in 2026

Software supply-chain attacks now represent the primary threat shaping the 2026 security landscape. Rather than relying on exploits at the perimeter, attackers are targeting the connective tissue of modern engineering environments: package managers, CI/CD automation, developer systems, and even the security tools organizations inherently trust.

These incidents are not isolated cases of poisoned code. They reflect a structural shift toward abusing trusted automation and identity at ecosystem scale, where compromise propagates through systems designed for speed, not scrutiny. Ephemeral build runners, regardless of provider, represent high‑trust, low‑visibility execution zones.

The Axios compromise and the cascading Trivy campaign illustrate how quickly this abuse can move once attacker activity enters build and delivery workflows. This blog provides an overview of the latest supply chain and security tool incidents with Darktrace telemetry and defensive actions to improve organizations defensive cyber posture.

1. Why the Axios Compromise Scaled

On 31 March 2026, attackers hijacked the npm account of Axios’s lead maintainer, publishing malicious versions 1.14.1 and 0.30.4 that silently pulled in a malicious dependency, plain‑crypto‑[email protected]. Axios is a popular HTTP client for node.js and processes 100 million weekly downloads and appears in around 80% of cloud and application environments, making this a high‑leverage breach [1].

The attack chain was simple yet effective:

A compromised maintainer account enabled legitimate‑looking malicious releases.
The poisoned dependency executed Remote Access Trojans (RATs) across Linux, macOS and Windows systems.
The malware beaconed to a remote command-and-control (C2) server every 60 seconds in a loop, awaiting further instructions.
The installer self‑cleaned by deleting malicious artifacts.

All of this matters because a single maintainer compromise was enough to project attacker access into thousands of trusted production environments without exploiting a single vulnerability.

A view from Darktrace

Multiple cases linked with the Axios compromise were identified across Darktrace’s customer base in March 2026, across both Darktrace / NETWORK and Darktrace / CLOUD deployments.

In one Darktrace / CLOUD deployment, an Azure Cloud Asset was observed establishing new external HTTP connectivity to the IP 142.11.206[.]73 on port 8000. Darktrace deemed this activity as highly anomalous for the device based on several factors, including the rarity of the endpoint across the network and the unusual combination of protocol and port for this asset. As a result, the triggering the "Anomalous Connection / Application Protocol on Uncommon Port" model was triggered in Darktrace / CLOUD. Detection was driven by environmental context rather than a known indicator at the time. Subsequent reporting later classified the destination as malicious in relation to the Axios supply‑chain compromise, reinforcing the gap that often exists between initial attacker activity and the availability of actionable intelligence. [5]

Additionally, shortly before this C2 connection, the device was observed communicating with various endpoints associated with the NPM package manager, further reinforcing the association with this attack.

Figure 1: Darktrace’s detection of the unusual external connection to 142.11[.]206[.]73 via port 8000.

Within Axios cases observed within Darktrace / NETWORK customer environments, activity generally focused on the use of newly observed cURL user agents in outbound connections to the C2 URL sfrclak[.]com/6202033, alongside the download of malicious files.

In other cases, Darktrace / NETWORK customers with Microsoft Defender for Endpoint integration received alerts flagging newly observed system executables and process launches associated with C2 communication.

Figure 2: A Security Integration Alert from Microsoft Defender for Endpoint associated with the Axios supply chain attack.

2. Why Trivy bypassed security tooling trust

Between late February and March 22, 2026, the threat group TeamPCP leveraged credentials from a previous incident to insert malicious artifacts across Trivy’s distribution ecosystem, including its CI automation, release binaries, Visual Studio Code extensions, and Docker container images [2].

While public reporting has emphasized GitHub Actions, Darktrace telemetry highlights attacker execution within CI/CD runner environments, including ephemeral build runners. These execution contexts are typically granted broad trust and limited visibility, allowing malicious activity within build automation to blend into expected operational workflows, regardless of provider.

This was a coordinated multi‑phase attack:

75 of 76 of trivy-action tags and all setup‑trivy tags were force‑pushed to deliver a malicious payload.
A malicious binary (v0.69.4) was distributed across all major distribution channels.
Developer machines were compromised, receiving a persistent backdoor and a self-propagating worm.
Secrets were exfiltrated at scale, including SSH keys, Kuberenetes tokens, database passwords, and cloud credentials across Amazon Web Service (AWS), Azure, and Google Cloud Platform (GCP).

Within Darktrace’s customer base, an AWS EC2 instance monitored by Darktrace / CLOUD appeared to have been impacted by the Trivy attack. On March 19, the device was seen connecting to the attacker-controlled C2 server scan[.]aquasecurtiy[.]org (45.148.10[.]212), triggering the model 'Anomalous Server Activity / Outgoing from Server’ in Darktrace / CLOUD.

Despite this limited historical context, Darktrace assessed this activity as suspicious due to the rarity of the destination endpoint across the wider deployment. This resulted in the triggering of a model alert and the generation of a Cyber AI Analyst incident to further analyze and correlate the attack activity.

TeamPCP’s continued abused of GitHub Actions against security and IT tooling has also been observed more recently in Darktrace’s customer base. On April 22, an AWS asset was seen connecting to the C2 endpoint audit.checkmarx[.]cx (94.154.172[.]43). The timing of this activity suggests a potential link to a malicious Bitwarden package distributed by the threat actor, which was only available for a short timeframe on April 22. [4][3]

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Figure 3: A model alert flagging unusual external connectivity from the AWS asset, as seen in Darktrace / CLOUD .

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While the Trivy activity originated within build automation, the underlying failure mode mirrors later intrusions observed via management tooling. In both cases, attackers leveraged platforms designed for scale and trust to execute actions that blended into normal operational noise until downstream effects became visible.

Quest KACE: Legacy Risk, Real Impact

The Quest KACE System Management Appliance (SMA) incident reinforces that software risk is not confined to development pipelines alone. High‑trust infrastructure and management platforms are increasingly leveraged by adversaries when left unpatched or exposed to the internet.

Throughout March 2026, attackers exploited CVE 2025-32975 to authentication on outdated, internet-facing KACE appliances, gaining administrative control and pushing remote payloads into enterprise environments. Organizations still running pre-patch versions effectively handed adversaries a turnkey foothold, reaffirming a simple strategic truth: legacy management systems are now part of the supply-chain threat surface, and treating them as “low-risk utilities” is no longer defensible [3].

Within the Darktrace customer base, a potential case was identified in mid-March involving an internet-facing server that exhibited the use of a new user agent alongside unusual file downloads and unexpected external connectivity. Darktrace identified the device downloading file downloads from "216.126.225[.]156/x", "216.126.225[.]156/ct.py" and "216.126.225[.]156/n", using the user agents, "curl/8.5.0" & "Python-urllib/3.9".

The timeframe and IoCs observed point towards likely exploitation of CVE‑2025‑32975. As with earlier incidents, the activity became visible through deviations in expected system behavior rather than through advance knowledge of exploitation or attacker infrastructure. The delay between observed exploitation and its addition to the Known Exploited Vulnerabilities (KEV) catalogue underscores a recurring failure: retrospective validation cannot keep pace with adversaries operating at automation speed.

The strategic pattern: Ecosystem‑scale adversaries

The Axios and Trivy compromises are not anomalies; they are signals of a structural shift in the threat landscape. In this post-trust era, the compromise of a single maintainer, repository token, or CI/CD tag can produce large-scale blast radiuses with downstream victims numbering in the thousands. Attackers are no longer just exploiting vulnerabilities; they are exploiting infrastructure privileges, developer trust relationships, and automated build systems that the industry has generally under secured.

Supply‑chain compromise should now be treated as an assumed breach scenario, not a specialized threat class, particularly across build, integration, and management infrastructure. Organizations must operate under the assumption that compromise will occur within trusted software and automation layers, not solely at the network edge or user endpoint. Defenders should therefore expect compromise to emerge from trusted automation layers before it is labelled, validated, or widely understood.

The future of supply‑chain defense lies in continuous behavioral visibility, autonomous detection across developer and build environments, and real‑time anomaly identification.

As AI increasingly shapes software development and security operations, defenders must assume adversaries will also operate with AI in the loop. The defensive edge will come not from predicting specific compromises, but from continuously interrogating behavior across environments humans can no longer feasibly monitor at scale.

Credit to Nathaniel Jones (VP, Security & AI Strategy, FCISCO), Emma Foulger (Global Threat Research Operations Lead), Justin Torres (Senior Cyber Analyst), Tara Gould (Malware Research Lead)

Edited by Ryan Traill (Content Manager)

Appendices

References:

1) https://www.infosecurity-magazine.com/news/hackers-hijack-axios-npm-package/

2) https://thehackernews.com/2026/03/trivy-hack-spreads-infostealer-via.html

3) https://thehackernews.com/2026/03/hackers-exploit-cve-2025-32975-cvss-100.html

4) https://www.endorlabs.com/learn/shai-hulud-the-third-coming----inside-the-bitwarden-cli-2026-4-0-supply-chain-attack

5) https://socket.dev/blog/axios-npm-package-compromised?trk=public_post_comment-text

IoCs

- 142.11.206[.]73 – IP Address – Axios supply chain C2

- sfrclak[.]com – Hostname – Axios supply chain C2

- hxxp://sfrclak[.]com:8000/6202033 - URI – Axios supply chain payload

- 45.148.10[.]212 – IP Address – Trivy supply chain C2

- scan.aquasecurtiy[.]org – Hostname - Trivy supply chain C2

- 94.154.172[.]43 – IP Address - Checkmarx/Bitwarden supply chain C2

- audit.checkmarx[.]cx – Hostname - Checkmarx/Bitwarder supply chain C2

- 216.126.225[.]156 – IP Address – Quest KACE exploitation C2

- 216.126.225[.]156/32 - URI – Possible Quest KACE exploitation payload

- 216.126.225[.]156/ct.py - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/n - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/x - URI - Possible Quest KACE exploitation payload

- e1ec76a0e1f48901566d53828c34b5dc – MD5 - Possible Quest KACE exploitation payload

- d3beab2e2252a13d5689e9911c2b2b2fc3a41086 – SHA1 - Possible Quest KACE exploitation payload

- ab6677fcbbb1ff4a22cc3e7355e1c36768ba30bbf5cce36f4ec7ae99f850e6c5 – SHA256 - Possible Quest KACE exploitation payload

- 83b7a106a5e810a1781e62b278909396 – MD5 - Possible Quest KACE exploitation payload

- deb4b5841eea43cb8c5777ee33ee09bf294a670d – SHA1 - Possible Quest KACE exploitation payload

- b1b2f1e36dcaa36bc587fda1ddc3cbb8e04c3df5f1e3f1341c9d2ec0b0b0ffaf – SHA256 - Possible Quest KACE exploitation payload

Darktrace Model Detections

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous File / EXE from Rare External Location

Anomalous File / Script from Rare External Location

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous Server Activity / Rare External from Server

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

Device / New User Agent

Device / Internet Facing Device with High Priority Alert

Anomalous File / New User Agent Followed By Numeric File Download

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

Blog

Email

May 6, 2026

How email-delivered prompt injection attacks can target enterprise AI – and why it matters

What are email-delivered prompt injection attacks?

As organizations rapidly adopt AI assistants to improve productivity, a new class of cyber risk is emerging alongside them: email-delivered AI prompt injection. Unlike traditional attacks that target software vulnerabilities or rely on social engineering, this is the act of embedding malicious or manipulative instructions into content that an AI system will process as part of its normal workflow. Because modern AI tools are designed to ingest and reason over large volumes of data, including emails, documents, and chat histories, they can unintentionally treat hidden attacker-controlled text as legitimate input.

At Darktrace, our analysis has shown an increase of 90% in the number of customer deployments showing signals associated with potential prompt injection attempts since we began monitoring for this type of activity in late 2025. While it is not always possible to definitively attribute each instance, internal scoring systems designed to identify characteristics consistent with prompt injection have recorded a growing number of high-confidence matches. The upward trend suggests that attackers are actively experimenting with these techniques.

Recent examples of prompt injection attacks

Two early examples of this evolving threat are HashJack and ShadowLeak, which illustrate prompt injection in practice.

HashJack is a novel prompt injection technique discovered in November 2025 that exploits AI-powered web browsers and agentic AI browser assistants. By hiding malicious instructions within the URL fragment (after the # symbol) of a legitimate, trusted website, attackers can trick AI web assistants into performing malicious actions – potentially inserting phishing links, fake contact details, or misleading guidance directly into what appears to be a trusted AI-generated output.

ShadowLeak is a prompt injection method to exfiltrate PII identified in September 2025. This was a flaw in ChatGPT (now patched by OpenAI) which worked via an agent connected to email. If attackers sent the target an email containing a hidden prompt, the agent was tricked into leaking sensitive information to the attacker with no user action or visible UI.

What’s the risk of email-delivered prompt injection attacks?

Enterprise AI assistants often have complete visibility across emails, documents, and internal platforms. This means an attacker does not need to compromise credentials or move laterally through an environment. If successful, they can influence the AI to retrieve relevant information seamlessly, without the labor of compromise and privilege escalation.

The first risk is data exfiltration. In a prompt injection scenario, malicious instructions may be embedded within an ordinary email. As in the ShadowLeak attack, when AI processes that content as part of a legitimate task, it may interpret the hidden text as an instruction. This could result in the AI disclosing sensitive data, summarizing confidential communications, or exposing internal context that would otherwise require significant effort to obtain.

The second risk is agentic workflow poisoning. As AI systems take on more active roles, prompt injection can influence how they behave over time. An attacker could embed instructions that persist across interactions, such as causing the AI to include malicious links in responses or redirect users to untrusted resources. In this way, the attacker inserts themselves into the workflow, effectively acting as a man-in-the-middle within the AI system.

Why can’t other solutions catch email-delivered prompt injection attacks?

AI prompt injection challenges many of the assumptions that traditional email security is built on. It does not fit the usual patterns of phishing, where the goal is to trick a user into clicking a link or opening an attachment.

Most security solutions are designed to detect signals associated with user engagement: suspicious links, unusual attachments, or social engineering cues. Prompt injection avoids these indicators entirely, meaning there are fewer obvious red flags.

In this case, the intention is actually the opposite of user solicitation. The objective is simply for the email to be delivered and remain in the inbox, appearing benign and unremarkable. The malicious element is not something the recipient is expected to engage with, or even notice.

Detection is further complicated by the nature of the prompts themselves. Unlike known malware signatures or consistent phishing patterns, injected prompts can vary widely in structure and wording. This makes simple pattern-matching approaches, such as regex, unreliable. A broad rule set risks generating large numbers of false positives, while a narrow one is unlikely to capture the diversity of possible injections.

How does Darktrace catch these types of attacks?

The Darktrace approach to email security more generally is to look beyond individual indicators and assess context, which also applies here.

For example, our prompt density score identifies clusters of prompt-like language within an email rather than just single occurrences. Instead of treating the presence of a phrase as a blocking signal, the focus is on whether there is an unusual concentration of these patterns in a way that suggests injection. Additional weighting can be applied where there are signs of obfuscation. For example, text that is hidden from the user – such as white font or font size zero – but still readable by AI systems can indicate an attempt to conceal malicious prompts.

This is combined with broader behavioral signals. The same communication context used to detect other threats remains relevant, such as whether the content is unusual for the recipient or deviates from normal patterns.

Ask your email provider about email-delivered AI prompt injection

Prompt injection targets not just employees, but the AI systems they rely on, so security approaches need to account for both.

Though there are clear indications of emerging activity, it remains to be seen how popular prompt injection will be with attackers going forward. Still, considering the potential impact of this attack type, it’s worth checking if this risk has been considered by your email security provider.

Questions to ask your email security provider

What safeguards are in place to prevent emails from influencing AI‑driven workflows over time?
How do you assess email content that’s benign for a human reader, but may carry hidden instructions intended for AI systems?
If an email contains no links, no attachments, and no social engineering cues, what signals would your platform use to identify malicious intent?

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Visit the Darktrace / EMAIL product hub to discover how we detect and respond to advanced communication threats.

Learn more about securing AI in your enterprise.

About the author

Kiri Addison

Senior Director of Product

Your data. Our AI.

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Check out this article by Darktrace: Guloader: Evolving Tactics in Latest Campaign Targeting European Industry

GuLoader: Evolving Tactics in Latest Campaign Targeting European Industry

Introduction: GuLoader

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Key Takeaway

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Indicators of compromise (IoCs)

GuLoader scripts

Network IOCs

Detection

Yara rule

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References:

When Trust Becomes the Attack Surface: Supply-Chain Attacks in an Era of Automation and Implicit Trust

How email-delivered prompt injection attacks can target enterprise AI – and why it matters

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When Trust Becomes the Attack Surface: Supply-Chain Attacks in an Era of Automation and Implicit Trust

Software supply-chain attacks in 2026

1. Why the Axios Compromise Scaled

A view from Darktrace

2. Why Trivy bypassed security tooling trust

Quest KACE: Legacy Risk, Real Impact

The strategic pattern: Ecosystem‑scale adversaries

Appendices

References:

IoCs

Darktrace Model Detections

Blog

Email

May 6, 2026

How email-delivered prompt injection attacks can target enterprise AI – and why it matters

What are email-delivered prompt injection attacks?

Recent examples of prompt injection attacks

What’s the risk of email-delivered prompt injection attacks?

Why can’t other solutions catch email-delivered prompt injection attacks?

How does Darktrace catch these types of attacks?

Ask your email provider about email-delivered AI prompt injection

Questions to ask your email security provider

PowerShell  