Recovering Encrypted LLM Reasoning Traces

Researchers discovered a method to recover hidden reasoning traces from AI models by replaying encrypted data blobs (encrypted reasoning, where an AI's internal thought process is encoded and hidden) from one model to a less capable model that can be manipulated into revealing the original content. The attack works because providers likely use shared encryption keys across users and models, meaning encrypted reasoning traces that leak into public repositories can potentially be decoded and expose sensitive information like passwords and API keys.