LLM Reasoning Blocks Security
I’ve never been happy with the idea of locking up model reasoning in encrypted blocks. If we’re going to treat an AI’s internal thoughts like state secrets, we’d better be damn sure they stay secret. The latest research suggests we might not be. Researchers showed how to smuggle sensitive data straight out of proprietary LLM APIs by hiding it in the encrypted reasoning blocks that models return. It’s not some elaborate side-channel attack—just a clever abuse of how providers handle their own encryption. They demonstrated this across OpenAI, Anthropic, and Google’s latest models, which should make anyone running a commercial API pause. If you’re trusting these blocks to keep secrets while they shuttle between user and server, you’re trusting the wrong thing. The kicker? This isn’t just theory. They tested it with a sanitized GitHub repo search—no API keys, but plenty of tokens that don’t belong in public code. The results were messy, but the method worked. And if it works once,...