In a nutshell: AI agents fail to recognise trust boundaries between private and public resources, becoming an unintended bridge between sensitive internal systems and the public internet.
A prompt injection attack enables attackers to get GitHub’s Agentic Workflows to extract content from private repositories and publish it publicly. Security firm Noma Security has identified the attack as a fundamental architectural problem with AI agents that have privileged access.
Attack mechanism: Researchers from Noma Security demonstrated that an unauthenticated attacker can submit a GitHub issue in a public repository with hidden instructions. GitHub Agentic Workflows combine GitHub Actions with AI models (Claude, GitHub Copilot) and interpret such issues as instructions rather than untrusted content. If the agent has read access to private repositories in the same organisation, it retrieves the requested files and publishes them in the public issue thread.
The attack is called “GitLost” and requires neither stolen credentials nor malware or software vulnerabilities. In a demonstration, Noma researchers generated a seemingly harmless issue with a documentation request, whereupon the agent read a README file from a private repository and posted it publicly as a comment. Further tests showed that minimal reformulations bypassed GitHub’s prompt-based guardrails, even though the agent had previously rejected identical instructions.
Systemic cause: Cybersecurity researcher Vibhum Dubey puts the problem into fundamental perspective: the error does not lie primarily in prompt injection, but in GitHub’s permission model. AI agents operate on the basis of service account permissions, not user permissions, and have no execution contexts for trust boundaries. The agent does not “know” that a repository is private—it only sees “accessible”. Noma warns that this represents a generic architectural problem affecting all AI agents with access to both untrusted external content and sensitive internal resources.
Implications for CISOs: As AI agent deployment increases, such “invisible permission gaps” are multiplying. Dubey emphasises that organisations must fundamentally rethink their permission management for AI agents—not just optimise monitoring. Trust boundaries exist in GitHub’s data model, but not in the agent’s execution context. This requires new control structures before permissions are granted to prevent agents from becoming uncontrolled bridges between internal and external resources.
Source: www.csoonline.com · Published 8 July 2026
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