Record summary
A quick snapshot of what this page covers.
Attack context
How this AI attack works in practice.
Adversaries may impersonate a trusted person or organization in order to persuade and trick a target into performing some action on their behalf. For example, adversaries may communicate with victims (via Phishing, or Spearphishing via Social Engineering LLM) while impersonating a known sender such as an executive, colleague, or third-party vendor. Established trust can then be leveraged to accomplish an adversary's ultimate goals, possibly against multiple victims.
Adversaries may target resources that are part of the AI DevOps lifecycle, such as model repositories, container registries, and software registries.
- ATLAS ID
- AML.T0073
- ATT&CK external ID
- T1656
- Priority score
- 130
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
Poisoned Postmark MCP Server Email Exfiltration
A bad actor successfully exfiltrated emails from users of the Postmark’s MCP server via a supply chain attack. Postmark is an email delivery service that allows organizations to send marketing and transactional emails via API. The Postmark MCP server allows users to interact with Postmark via AI agents.
The bad actor impersonated Postmark, by registering the postmark-mcp package name on npm. They initially published the legitimate versions of the MCP server. After the package became popular and reached over 1,000 downloads per week, the bad actor performed a rugpull and uploaded a malicious version of the package. The malicious version added the bad actor’s email address in the BCC line of all emails sent by the MCP tool. Users who upgraded to this version and continued to use the tool would have all emails exfiltrated to the bad actor.
LAMEHUG: Malware Leveraging Dynamic AI-Generated Commands
In July 2025, Ukrainian authorities reported the emergence of LAMEHUG, a new AI-powered malware attributed to the Russian state-backed threat actor APT28 (also tracked as Forest Blizzard or UAC-0001). LAMEHUG uses a large language model (LLM) to dynamically generate commands on the infected hosts.
The campaign began with a phishing attack leveraging a compromised government email account to deliver a malicious ZIP archive disguised as Appendix.pdf.zip. The archive contained the LAMEHUG malware, a Python-based executable, packed with PyInstaller. When executed, the malware, makes calls to an LLM endpoint to generate malicious from natural language prompts. Dynamically generated commands may make the malware harder to detect. LAMEHUG was configured to collect files from the local system and exfiltrate them.
ProKYC: Deepfake Tool for Account Fraud Attacks
Cato CTRL security researchers have identified ProKYC, a deepfake tool being sold to cybercriminals as a method to bypass Know Your Customer (KYC) verification on financial service applications such as cryptocurrency exchanges. ProKYC can create fake identity documents and generate deepfake selfie videos, two key pieces of biometric data used during KYC verification. The tool helps cybercriminals defeat facial recognition and liveness checks to create fraudulent accounts.
The procedure below describes how a bad actor could use ProKYC’s service to bypass KYC verification.
Live Deepfake Image Injection to Evade Mobile KYC Verification
Facial biometric authentication services are commonly used by mobile applications for user onboarding, authentication, and identity verification for KYC requirements. The iProov Red Team demonstrated a face-swapped imagery injection attack that can successfully evade live facial recognition authentication models along with both passive and active liveness verification on mobile devices. By executing this kind of attack, adversaries could gain access to privileged systems of a victim or create fake personas to create fake accounts on banking or cryptocurrency apps.
Organization Confusion on Hugging Face
threlfall_hax, a security researcher, created organization accounts on Hugging Face, a public model repository, that impersonated real organizations. These false Hugging Face organization accounts looked legitimate so individuals from the impersonated organizations requested to join, believing the accounts to be an official site for employees to share models. This gave the researcher full access to any AI models uploaded by the employees, including the ability to replace models with malicious versions. The researcher demonstrated that they could embed malware into an AI model that provided them access to the victim organization's environment. From there, threat actors could execute a range of damaging attacks such as intellectual property theft or poisoning other AI models within the victim's environment.
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