Stealing Reasoning Traces from Proprietary LLM APIs
An academic paper by Panfilov et al. that systematically investigates attacks in which reasoning trace data is extracted from closed-source large model APIs and used to train proprietary models. The paper examines the technical mechanisms, scope of impact, and potential defense measures against such attacks.
Timeline (last 90 days)
The research in this paper involves large-scale extraction and distillation of reasoning chains from closed-source large model APIs to provide high-quality data for reinforcement learning training of self-developed models.
Researchers Ilya and Sasha published a paper titled "Stealing Reasoning Traces from Proprietary LLM APIs," studying the theft of reasoning traces from proprietary large model APIs.
The paper received approximately 3 million views within 40 hours of its release.
一篇名为《Stealing Reasoning Traces from Proprietary LLM APIs》的安全论文于2026年8月11日发布，作者包括Alexander Panfilov、David Schmutz、Ilya Shmelov等人，来自MATS项目
All Facts (3)
The research in this paper involves large-scale extraction and distillation of reasoning chains from closed-source large model APIs to provide high-quality data for reinforcement learning training of self-developed models.
50%UnverifiedResearchers Ilya and Sasha published a paper titled "Stealing Reasoning Traces from Proprietary LLM APIs," studying the theft of reasoning traces from proprietary large model APIs.
60%Unverified一篇名为《Stealing Reasoning Traces from Proprietary LLM APIs》的安全论文于2026年8月11日发布，作者包括Alexander Panfilov、David Schmutz、Ilya Shmelov等人，来自MATS项目
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