A New Trick Reveals AI Models’ Inner Thoughts
Researchers developed a technique to extract hidden reasoning traces from Claude, GPT, and Gemini. The findings suggest some Chinese AI models are trained on outputs from leading US systems.
- New technique extracts hidden Chain of Thought from Claude, GPT, and Gemini.
- Analysis indicates Chinese models like DeepSeek may be trained on US model outputs.
- The finding exposes vulnerabilities in the security of closed-source AI systems.
- It suggests widespread use of synthetic data from leading models in global AI development.
Researchers have discovered a method to extract internal reasoning traces, or Chain of Thought, from major closed-source models like Anthropic's Claude, OpenAI's GPT, and Google's Gemini. This technique bypasses the safety filters designed to keep these internal processes hidden from users.
Analysis of these extracted traces indicates that certain Chinese AI models, specifically DeepSeek, likely utilized outputs from US models during their own training. This suggests a form of knowledge distillation where leading Western models serve as teachers for foreign competitors.
The discovery highlights significant security vulnerabilities in commercial AI APIs and raises concerns about intellectual property protection. It demonstrates that despite closed-source protections, the internal logic and data of these models can be accessed and potentially replicated.
Reveals security flaws in API integrations and prompt handling.
Raises concerns about IP theft and competitors using proprietary model outputs.
Highlights data provenance risks and the difficulty of maintaining moats in AI.
Shows how AI models can be probed to reveal secrets they are meant to keep.
- Chain of Thought
- The step-by-step logical process an AI model uses to solve a problem or generate an answer.
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