Encrypted LLM reasoning can be cracked, exposing IP and private data
A new paper shows that encrypted chain‑of‑thought blocks returned by major LLM APIs can be swapped into weaker models, forcing them to reveal the plaintext reasoning. The attack leaks proprietary model logic, personal data, credentials, and enables hidden prompt injections, prompting urgent mitigation proposals.