Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models
Large language models increasingly rely on explicit chain-of-thought reasoning to solve complex tasks, yet the safety of the reasoning process itself remains largely unaddressed. Existing work focuses predominantly on content safety (i.e., detecting harmful, biased, or factually incorrect outputs), while treating the underlying reasoning chain as an opaque intermediate artifact. We argue that reasoning safety constitutes a fundamental security dimension orthogonal to content safety: the requirement that a model's reasoning trajectory be logically consistent, computationally efficient, and resistant to adversarial manipulation. In this paper, we formalize reasoning safety and introduce a s...
| Authors | Xunguang Wang, Yuguang Zhou, Qingyue Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 March 2026 |
| Last updated at source | 6 May 2026 |
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