New Technique Reveals LLM Reasoning Traces, Data Leak Risk

TL;DR. Researchers developed a method to extract hidden reasoning steps from LLMs like Claude, GPT, and Gemini, revealing potential data leakage and model distillation concerns. - The technique allows observation of an AI model's internal 'thinking' process when solving complex problems. - This method exposed a vulnerability that could leak personal information like passwords and API keys from model reasoning. - Findings suggest some Chinese models may use distillation from leading US models, though this is not conclusive proof.

• Computer scientists found a way to extract hidden reasoning traces from frontier AI models.
• This method identified a vulnerability allowing personal information leakage, since patched.
• The research provides evidence of potential reasoning distillation from US models by Chinese AI.

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