ReasoningShield: Content Safety Detection over Reasoning Traces of Large Reasoning Models

• URL: http://arxiv.org/abs/2505.17244v1
• Date: Thu, 22 May 2025 19:44:41 GMT
• Title: ReasoningShield: Content Safety Detection over Reasoning Traces of Large Reasoning Models
• Authors: Changyi Li, Jiayi Wang, Xudong Pan, Geng Hong, Min Yang,
• Abstract summary: Reasoning Models (LRMs) are transforming the AI landscape with advanced reasoning capabilities.<n>While the generated reasoning traces enhance model transparency, they can still contain unsafe content, even when the final answer appears safe.<n>Existing moderation tools, primarily designed for question-answer (QA) pairs, are empirically ineffective at detecting hidden risks embedded in reasoning traces.<n>We propose ReasoningShield, the first safety detection model tailored to identify potential risks in the reasoning trace before reaching the final answer.
• Score: 19.963759799471568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Reasoning Models (LRMs) are transforming the AI landscape with advanced reasoning capabilities. While the generated reasoning traces enhance model transparency, they can still contain unsafe content, even when the final answer appears safe. Existing moderation tools, primarily designed for question-answer (QA) pairs, are empirically ineffective at detecting hidden risks embedded in reasoning traces. After identifying the key challenges, we formally define the question-thought (QT) moderation task and propose ReasoningShield, the first safety detection model tailored to identify potential risks in the reasoning trace before reaching the final answer. To construct the model, we synthesize a high-quality reasoning safety detection dataset comprising over 8,000 question-thought pairs spanning ten risk categories and three safety levels. Our dataset construction process incorporates a comprehensive human-AI collaborative annotation pipeline, which achieves over 93% annotation accuracy while significantly reducing human costs. On a diverse set of in-distribution and out-of-distribution benchmarks, ReasoningShield outperforms mainstream content safety moderation models in identifying risks within reasoning traces, with an average F1 score exceeding 0.92. Notably, despite being trained on our QT dataset only, ReasoningShield also demonstrates competitive performance in detecting unsafe question-answer pairs on traditional benchmarks, rivaling baselines trained on 10 times larger datasets and base models, which strongly validates the quality of our dataset. Furthermore, ReasoningShield is built upon compact 1B/3B base models to facilitate lightweight deployment and provides human-friendly risk analysis by default. To foster future research, we publicly release all the resources.

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