Researchers Find Encrypted LLM Reasoning Tokens Vulnerable to Cross-Model Extraction

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A new research paper reveals that major AI companies' method of hiding model reasoning traces through encryption is fundamentally flawed. Providers like OpenAI, Anthropic, and Google encrypt their models' internal reasoning steps before sending them to users, intending to keep the thought processes proprietary. However, researchers found that these encrypted tokens are designed to be universally compatible across all models and sessions within a provider's ecosystem, creating an exploitable architectural weakness. Because a token generated by one model can be read by another within the same family, attackers can potentially extract proprietary reasoning traces by routing tokens across compatible models. The vulnerability does not stem from broken encryption itself, but from the deliberate design choice to prioritize cross-model convenience over strict user-level isolation.

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