Stealing Reasoning Traces from Proprietary LLM APIs

Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different ses

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ethics.ai (10 August 2026), “Stealing Reasoning Traces from Proprietary LLM APIs,” evidence record 18250, https://ethics.ai/record/18250 (originally published by arXiv cs.AI).

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