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AI Security Technique

Code Repositories - AI Security Technique

Adversaries may search public code repositories for information about a victim or victim system that can be used during targeting. Victims may store code or artifacts related to their AI systems in repositories on various third-party websites such as GitHub, GitLab, SourceForge, and BitBucket. Adversaries may search code repositories of common AI tools, frameworks, models, or agentic systems that are used--but not...

AI Security Techniquedemonstrated

Record summary

A quick snapshot of what this page covers.

Tactics0Attacker goals connected to this method.
Mitigations0Defenses that may help against this attack.
AI risks0Research-backed risks connected to this topic.

Attack context

How this AI attack works in practice.

Adversaries may search public code repositories for information about a victim or victim system that can be used during targeting. Victims may store code or artifacts related to their AI systems in repositories on various third-party websites such as GitHub, GitLab, SourceForge, and BitBucket. Adversaries may search code repositories of common AI tools, frameworks, models, or agentic systems that are used--but not owned--by the victim.

Public code repositories can often be a source of various information about victims, such as commonly used AI frameworks, libraries, models, datasets, agents, and agent tools, as well as the names of employees. Adversaries may also identify more sensitive data, including accidentally leaked credentials or API keys (ex: Credentials from AI Agent Configuration). Information from these sources may reveal opportunities for other forms of Reconnaissance (ex: Gather RAG-Indexed Targets), establishing operational resources (ex: Acquire Public AI Artifacts), Discovery (ex: Discover AI Agent Configuration) and/or Initial Access (ex: Valid Accounts or Phishing).

ATLAS ID
AML.T0095.000
ATT&CK external ID
T1593.003
Priority score
30
Maturity: demonstrated

Mitigations

Defenses that may help against this attack.

No connected defenses. No defense is connected to this attack in the current data.

Case studies

Examples from public reports and exercises.

OpenClaw Command & Control via Prompt Injection

exercise
Date2026-02-03

Researchers at HiddenLayer demonstrated how a webpage can embed an indirect prompt injection that causes OpenClaw to silently execute a malicious script. Once executed, the script plants persistent malicious instructions into future system prompts, allowing the attacker to issue new commands, turning OpenClaw into a command and control agent.

What makes this attack unique is that, through a simple indirect prompt injection attack into an agentic lifecycle, untrusted content can be used to spoof the model’s control scheme and induce unapproved tool invocation for execution. Through this single inject, an LLM can become a persistent, automated command & control implant.

Source

Where this page information comes from.