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GitLost: We Tricked GitHub's AI Agent into Leaking Private Repos

GitLost: How We Tricked GitHub’s AI Agent into Leaking Private Repos - Noma Security

noma.security

July 8, 2026

5 min read

🔥🔥🔥🔥🔥

68/100

Summary

Noma Labs identified a prompt injection vulnerability in GitHub's Agentic Workflows, allowing unauthenticated attackers to access private repository data by posting a crafted GitHub Issue in a public repository within the same organization. This vulnerability has been named GitLost.

Key Takeaways

  • Noma Labs identified a prompt injection vulnerability in GitHub’s Agentic Workflows, named GitLost, allowing unauthorized access to private repositories through crafted GitHub Issues in public repositories.
  • The vulnerability enables attackers to execute commands by submitting benign-looking issues, which the GitHub agent processes without verifying the trustworthiness of the content.
  • Exploiting the GitLost vulnerability requires no coding skills or credentials; an attacker only needs to create an issue in a public repository associated with the target organization.
  • The GitHub agent, when triggered by specific workflow actions, can inadvertently expose private repository data by posting it publicly as a comment.
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Community Sentiment

Negative

Positives

  • Some commenters see prompt injection as a critical issue that demands robust defenses, paralleling SQL injection vulnerabilities — a call to arms for better AI safety.
  • There's a recognition that organizations need to properly scope LLM access to prevent leaks, hinting at a path forward for responsible AI deployment.

Concerns

  • Many users are frustrated with large corporations like Microsoft pushing half-baked AI solutions, fearing a breaking point for consumer trust.
  • There's skepticism about GitHub's handling of AI features, with users questioning the responsibility and security of private repositories in the face of these vulnerabilities.
  • The consensus is that misconfiguration and poor scoping of agent permissions led to this vulnerability, highlighting a systemic failure in AI design and governance.

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