Have models report provable security bugs in their environment

A LessWrong post by anithite proposes a system to catch deployment security failures before they're discovered the hard way. Rather than finding out only when an AI breaks free or leaves suspicious logs, the idea is to give models a dedicated channel — verifiable, self-reporting tools that prove specific permission gaps exist. If a model with no internet access can curl a secretly provisioned URL, that call itself becomes proof of breach. Similar mechanisms could verify unauthorized file read or write, or code execution across isolation boundaries. The proposal includes concrete mechanics: tokens per isolation domain, canary files with random GUIDs, and a tree structure for nested sandboxes. The math is attractive — if only five percent of a hundred instances report a breach, the detection rate hits ninety-nine point four percent. As labs increasingly rely on behavioral logs to find policy violations, this approach flips the model: let the AI itself signal when the supposed safeguards failed.

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