Daily Arxiv

This page organizes papers related to artificial intelligence published around the world.
This page is summarized using Google Gemini and is operated on a non-profit basis.
The copyright of the paper belongs to the author and the relevant institution. When sharing, simply cite the source.

Thought Purity: A Defense Framework For Chain-of-Thought Attack

Created by
  • Haebom

Author

Zihao Xue, Zhen Bi, Long Ma, Zhenlin Hu, Yan Wang, Zhenfang Liu, Qing Sheng, Jie Xiao, Jungang Lou

Outline

Large-scale inference models (LRMs) trained using reinforcement learning demonstrate advanced inference capabilities, but are vulnerable to security threats. In particular, they are vulnerable to adversarial attacks, such as backdoor prompt attacks, during the Chain-of-Thought (CoT) generation process. CoT attacks (CoTA) exploit prompt controllability to degrade CoT security and operational performance. This paper proposes Thought Purity (TP), a defense framework for CoTA vulnerabilities. TP strengthens resistance to malicious content and maintains operational efficiency through three components: a safety-optimized data processing pipeline, reinforcement learning-based rule constraints, and adaptive monitoring metrics.

Takeaways, Limitations

Takeaways:
Presenting the first comprehensive defense mechanism against CoTA vulnerabilities in reinforcement learning-based inference systems.
Significantly improving the security-functionality balance of next-generation AI architectures.
The Thought Purity (TP) framework demonstrates the potential for enhanced security without compromising safety or performance.
Limitations:
It is difficult to grasp the specific technical Limitations from only a summary of the paper's contents.
The actual implementation of the TP framework and the verification results for various attack scenarios need to be confirmed through the paper.
The attack and defense methodologies covered in this study may be limited to certain types of models and attacks, and their generalization limitations require further research.
👍