Stealing Reasoning Traces from Proprietary LLM APIs
Researchers figured out how to extract proprietary reasoning traces from closed LLM APIs—without access to model weights.
Why it matters
A new attack surface on frontier models: adversaries can now reverse-engineer reasoning patterns and internal logic from API outputs, potentially exposing model architecture and training data. This reshapes the security model for reasoning-native APIs and forces labs to reconsider what traces they expose.
The key facts
5 to knowExtraction of reasoning traces from proprietary LLM APIs possible without model weights
Implies exposure of internal reasoning patterns and model logic
Attack vector targets closed-API business models (OpenAI o1, Claude Thinking, Gemini Deep Research)
Research published Aug 11, 2026 on Simon Willison's blog (trusted AI research aggregator)
Affects competitive moat of frontier labs relying on opaque reasoning as a differentiator
Go to the source
Simon Willisonsimonwillison.net