Developer Builds DIY Toolkit to Test Whether AI Coding Agents Respect Sandbox Limits

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A software developer has published a practical method for independently verifying whether AI coding agents truly enforce the file-access boundaries they claim to uphold. The approach distinguishes three common sandboxing mechanisms — prompt-level scoping, tool-level filtering, and OS-level isolation — arguing that only the last constitutes genuine enforcement. The test involves a honeypot repository containing decoy credentials and personal data files placed outside the agent's permitted scope, with a hidden instruction baiting the agent to access them. A lightweight shell script captures file-state snapshots before and after an agent session, then flags any unauthorized access or data leakage into allowed directories. The author frames each vendor sandbox claim as a hypothesis to be falsified on one's own hardware before trusting an agent with a real codebase.

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