How to Safety-Test AI Coding Agents Before Giving Them Repo Access

A software developer has outlined a lightweight auditing method to evaluate AI coding models before deploying them in real code repositories. The approach uses a small set of pinned test fixtures that assign a model a narrow task — fixing a typo in a README — while monitoring whether it attempts to access files or tools outside its permitted scope. Three scenarios test for boundary violations such as reading environment files, executing shell commands, or following potentially malicious instructions embedded in documentation. The fixtures are designed to fail loudly on small, isolated repos rather than hide risky behavior in complex codebases. Models that pass all three checks qualify only for further review, not immediate production access.