Critical Vulnerability Exposes GitHub Agentic Workflows to Prompt Injection
Summary
Researchers have discovered a critical vulnerability in GitHub's agentic workflows that allows attackers to exploit prompt injection. By crafting a public GitHub Issue, attackers can trick AI-powered workflows into exfiltrating data from private repositories without requiring authentication.
IFF Assessment
This vulnerability allows unauthorized data exfiltration, posing a significant risk to sensitive information stored in private repositories.
Severity
The vulnerability allows for unauthorized access to private data, impacting confidentiality significantly. The attack vector is relatively straightforward, involving the manipulation of public issues to trigger unintended workflow actions, and exploitability is high due to the nature of AI model interactions with user inputs.
Defender Context
This discovery highlights a critical risk vector for AI-powered automation tools, particularly within platforms like GitHub where code and sensitive data are managed. Defenders should be vigilant about the security of AI integrations, scrutinize inputs to AI agents, and review access controls for automated workflows to prevent similar prompt injection attacks.