agentic-actions-auditor

To audit GitHub Actions workflows for AI agent prompt injection risks, scan the workflow files to detect where attacker-controlled input reaches AI coding agents. This process identifies vulnerabilities like direct expression injection and dangerous sandbox configurations before unauthorized access occurs.

Security vulnerabilities affecting AI coding agents in CI/CD pipelines include env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. These attack vectors allow malicious input to manipulate AI agents running in GitHub Actions workflows.

You can check if your GitHub Actions workflow exposes AI agents to unauthorized data access by auditing the pipeline for dangerous sandbox configurations and wildcard user allowlists. Analyzing workflow files reveals whether attacker-controlled input can reach AI integrations like OpenAI Codex.

Yes, this CI/CD security audit detects vulnerabilities in OpenAI Codex integrations. It locates specific AI agent integrations within workflow files and analyzes attack vectors where attacker-controlled input reaches the AI agent, preventing potential prompt injection attacks.

You should run an agentic actions security audit on your repository when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations to prevent unauthorized data access.

An env var intermediary pattern in GitHub Actions AI workflows is an attack vector where environment variables pass attacker-controlled input to AI agents. Detecting this pattern during CI/CD pipeline security audits prevents prompt injection attacks targeting integrations like Claude Code Action.