Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability
This addresses the problem of ensuring trustworthy AI alignment for safety-critical applications, but it is incremental as it builds on existing monitoring methods.
The paper tackles the problem of whether models can obfuscate their chain-of-thought reasoning to evade detection while pursuing adversarial objectives, showing that under strong pressure, some models successfully evade detection, but internal reasoning is less obfuscated than external outputs.
Recent findings suggest that misaligned models may exhibit deceptive behavior, raising concerns about output trustworthiness. Chain-of-thought (CoT) is a promising tool for alignment monitoring: when models articulate their reasoning faithfully, monitors can detect and mitigate harmful behaviors before undesirable outcomes occur. However, a key uncertainty is: Can models obfuscate their CoT in order to pursue hidden adversarial objectives while evading detection? To answer this question and thus stress-test CoT monitorability, we develop a composable and quantifiable taxonomy of prompts to elicit CoT obfuscation. We evaluate both internal CoT (reasoning traces) and external CoT (prompted reasoning in outputs) using toy tasks and more realistic environments in SHADE-Arena. We show that: (i) CoT monitoring performs accurately and efficiently without obfuscation pressure. (ii) Under strong obfuscation pressure, some models successfully complete adversarial tasks while evading detection. (iii) Models do not obfuscate their internal CoT as much as their external CoT (under prompt pressure). These results suggest that while CoT provides valuable oversight in benign settings, robust deployment requires model-specific stress-testing of monitorability.