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arXiv · 2506.08399

SafeCoT: Improving VLM Safety with Minimal Reasoning

Jiachen Ma·Zhanhui Zhou·Chao Yang·Chaochao Lu
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Abstract

Ensuring safe and appropriate responses from vision-language models (VLMs) remains a critical challenge, particularly in high-risk or ambiguous scenarios. We introduce SafeCoT, a lightweight, interpretable framework that leverages rule-based chain-of-thought (CoT) supervision to improve refusal behavior in VLMs. Unlike prior methods that rely on large-scale safety annotations or complex modeling, SafeCoT uses minimal supervision to help models reason about safety risks and make context-aware refusals. Experiments across multiple benchmarks show that SafeCoT significantly reduces overrefusal and enhances generalization, even with limited training data. Our approach offers a scalable solution for aligning VLMs with safety-critical objectives.

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Jiachen Ma, Zhanhui Zhou, Chao Yang, Chaochao Lu. 2025-06-10. SafeCoT: Improving VLM Safety with Minimal Reasoning. https://arxiv.org/abs/2506.08399

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