AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models

📄 Abstract

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex problem-solving through Chain-of-Thought (CoT) reasoning. However, the multi-step nature of CoT introduces new safety challenges that extend beyond conventional language model alignment. We identify a failure mode in current safety CoT tuning methods: the \textit{snowball effect}, where minor reasoning deviations progressively amplify throughout the thought process, leading to either harmful compliance or excessive refusal. This effect stems from models being trained to imitate perfect reasoning scripts without learning to self-correct. To address this limitation, we propose AdvChain, an alignment paradigm that teaches models dynamic self-correction through adversarial CoT tuning. Our method involves constructing a dataset containing Temptation-Correction and Hesitation-Correction samples, where models learn to recover from harmful reasoning drifts and unnecessary cautions. Extensive experiments show that AdvChain significantly enhances robustness against jailbreak attacks and CoT hijacking while substantially reducing over-refusal on benign prompts, achieving a superior safety-utility balance without compromising reasoning capabilities. Our work establishes a new direction for building more robust and reliable reasoning models.

🔍 Key Points

Identification of the 'Snowball Effect' in current alignment methods for Large Reasoning Models (LRMs), leading to significant vulnerabilities in safety and compliance.
Proposing AdvChain—a novel adversarial CoT tuning framework that facilitates dynamic self-correction in LRMs, improving robustness against harmful requests and reducing over-refusal in benign cases.
Construction of a specialized adversarial safety reasoning dataset featuring Temptation-Correction and Hesitation-Correction samples to train models effectively handle internal deviations in reasoning.
Extensive experimentation demonstrating that AdvChain significantly outperforms existing alignment methods in both safety and usability metrics, suggesting a new paradigm for robust AI alignment.
Establishment of a critical foundation for future research in developing safer and more reliable reasoning models, addressing a gap in current AI safety literature.

💡 Why This Paper Matters

This paper introduces a significant advancement in the safety alignment of Large Reasoning Models by addressing a previously unrecognized failure mode—the Snowball Effect. By developing AdvChain, which emphasizes the importance of dynamic self-correction rather than static imitation of perfect reasoning, the authors make a substantial contribution to the field. This work not only enhances model safety under various attack scenarios but also ensures that LRMs maintain their helpfulness, thereby balancing safety and utility effectively. The implications of these findings are vast, as they pave the way for the development of more resilient and intelligent AI systems capable of safely navigating complex reasoning tasks.

🎯 Why It's Interesting for AI Security Researchers

For AI security researchers, this paper is crucial as it uncovers a fundamental vulnerability in current alignment methodologies and offers a robust countermeasure through AdvChain. The insights into the Snowball Effect and the innovative training techniques proposed can inspire new defensive strategies against potential exploits in AI systems. Furthermore, understanding how adversarial training can enhance model resilience is essential for developing more secure AI technologies, making this work a significant resource for those focused on AI safety and security.

AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models

📄 Abstract

🔍 Key Points

💡 Why This Paper Matters

🎯 Why It's Interesting for AI Security Researchers

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