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Cyber Security
Independent · Digital
Thehackingpost
CybersecurityAI-assisted

Blind Eagle Hackers Exploit Trust to Bypass Email Security Controls

BlindEagle threat actors are exploiting compromised internal email accounts to launch spear-phishing campaigns targeting Colombian government agencies with sophisticated multi-stage malware attacks, according to Zscaler ThreatLabz research.

BlindEagle threat actors are exploiting compromised internal email accounts to launch spear-phishing campaigns targeting Colombian government agencies with sophisticated multi-stage malware attacks, according to Zscaler ThreatLabz research.

The campaign, discovered in early September 2025, targets a government agency under Colombia’s Ministry of Commerce, Industry and Tourism (MCIT) using a phishing email sent from a compromised account within the same organization. This technique exploits inherent trust between colleagues and leverages internal email infrastructure to evade detection.

The attack begins with a phishing email sent to a shared IT team email address from another agency-shared account, making it appear legitimate. BlindEagle’s campaign leverages in-memory scripts, legitimate internet services like Discord, steganography, and the deployment of Caminho and DCRAT.

Security analysis of the email metadata revealed that both sender and receiver domains had properly configured DMARC, DKIM, and SPF protocols with no evident flaws. The email trajectory appeared completely legitimate, with all “Received” headers referencing Microsoft 365/Exchange servers. Notably, DMARC, DKIM, and SPF checks were not applied because the message remained entirely within the organization’s Microsoft 365 tenant.

The phishing lure employed a legal-themed design, referencing an authentic-sounding labor lawsuit case number and date to pressure recipients into immediate action. The email contained a clickable SVG image that, when activated, decoded a Base64-encoded HTML page mimicking an official Colombian judicial portal. This fraudulent portal automatically downloaded a JavaScript file.

The attack chain initiates a file-less execution sequence comprising three JavaScript snippets followed by a PowerShell command. The first two scripts deobfuscate embedded payloads, while the third script introduces Unicode-based comment obfuscation and employs Windows Management Instrumentation (WMI) to execute a PowerShell command through the Win32_Process.Create() method with hidden window properties. After isolating and decoding the payload, the script dynamically loads it as a .NET assembly, culminating in the invocation of the VAI method within the ClassLibrary1 home class.

ThreatLabz identified the loaded assembly as Caminho (also known as VMDetectLoader), a malware downloader first traced to May 2025. The PowerShell command downloads an image file from the Internet Archive, carving out a Base64-encoded payload. The configuration is encrypted using AES-256 with a symmetric key, including a certificate ensuring configuration integrity and enabling C2 server authentication.

Caminho’s primary method downloads a text file from a Discord CDN URL, which is obfuscated through Base64 encoding and reversal. The file never touches disk; instead, it loads directly into memory. Caminho then deobfuscates the content, executing it via process hollowing where a legitimate MSBuild.exe instance is hollowed out to host the malicious code. The injected payload is DCRAT, an open-source C# remote access trojan offering keylogging, disk access, and other capabilities.

ThreatLabz identified 24 hosts worldwide exposing certificates issued by the same source as the DCRAT sample. The campaign is attributed to BlindEagle with medium confidence based on multiple factors, including the use of Swedish IP addresses under ASN 42708 (GleSYS AB) for the DCRAT C2 domain and the use of Dynamic DNS services from ydns.eu, previously documented as a BlindEagle preference. Victimology strongly supports attribution, as Colombia remains BlindEagle’s primary target.

Indicator Description

961ebce4327b18b39630bfc4edb7ca34 MD5 hash of the JavaScript file.

3983a5b4839598ba494995212544da05087b811b SHA1 hash of the JavaScript file.

This technique exploits inherent trust between colleagues and leverages internal email infrastructure to evade detection.
Aiden Sinclair · Thehackingpost

d0fe6555bc72a7a45a836ea137850e6e687998eb1c4465b8ad1fb6119ff882ab SHA256 hash of the JavaScript file.

d80237d48e1bbc2fdda741cbf006851a MD5 hash of the SVG attachment.

722a4932576734a08595c7196d87395e6ec653d7 SHA1 hash of the SVG attachment.

8f3dc1649150961e2bac40d8dabe5be160306bcaaa69ebe040d8d6e634987829 SHA256 hash of the SVG attachment.

c98eb5fcddf0763c7676c99c285f6e80 MD5 hash of the fraudulent web portal.

3ab2aa4e9a7a8abcf1ea42b51152f6bb15a1b3c5 SHA1 hash of the fraudulent web portal.

03548c9fad49820c52ff497f90232f68e044958027f330c2c51c80f545944fc1 SHA256 hash of the fraudulent web portal.

4284e99939cebf40b8699bed31c82fd6 MD5 hash of the PNG image.

21e95fed5fc5c4a10fafbc3882768cce1f6cd7af SHA1 hash of the PNG image.

08a5d0d8ec398acc707bb26cb3d8ee2187f8c33a3cbdee641262cfc3aed1e91d SHA256 hash of the PNG image.

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9799484e3942a6692be69aec1093cb6c MD5 hash of the Caminho instance.

b3fb8a805d3acc2eda39a83a14e2a73e8b244cf4 SHA1 hash of the Caminho instance.

c208d8d0493c60f14172acb4549dcb394d2b92d30bcae4880e66df3c3a7100e4 SHA256 hash of the Caminho instance.

bbb99dfd9bf3a2638e2e9d13693c731c MD5 hash of the text file.

4397920a0b08a31284aff74a0bed9215d5787852 SHA1 hash of the text file.

d139bfe642f3080b461677f55768fac1ae1344e529a57732cc740b23e104bff0 SHA256 hash of the text file.

97adb364d695588221d0647676b8e565 MD5 hash of the DCRAT instance.

38b0e360d58d4ddb17c0a2c4d97909be43a3adc0 SHA1 hash of the DCRAT instance.

e7666af17732e9a3954f6308bc52866b937ac67099faa212518d5592baca5d44 SHA256 hash of the DCRAT instance.

hXXps://archive[.]org/download/optimized_msi_20250821/optimized_MSI.png Download URL for the PNG image.

startmenuexperiencehost[.]ydns.eu DCRAT C2 domain.

Based on reporting by GBHackers.

AI transparency. This article was produced with the assistance of artificial intelligence and published under human editorial oversight. AI systems can make mistakes. Read how we use AI (EU AI Act, Art. 50).
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