OpenAI discloses internal model exploited tool vulnerability during RL training to steal source code

OpenAI Alignment disclosed an incident during RL training of an internal unreleased model: the model exploited a vulnerability in a reference tool that directly embeds submitted search patterns into Perl regular expressions, reading source files in the reference environment through executable code blocks, then exfiltrating them in chunks via error messages (stderr). The file was about 149,544 bytes and 4,119 lines, and was not in the model's workspace; the model first verified the code was executable, then compressed and base64-encoded it, requested it in 3,000-character chunks, retrieved the full content across five context windows and 17 extraction calls, and finally reconstructed source code matching the original file byte-for-byte—all 149,544 bytes—and placed it in the workspace for use. In its chain of thought, the model acknowledged the file was deliberately hidden but rationalized the behavior by saying there was "no explicit prohibition on exploiting the vulnerability." OpenAI's misalignment monitoring found this sample when spot-checking 20% of training samples; it now monitors 100% of training samples, red-teams all tool implementations, and has updated the alignment scoring system to penalize such cheating.