首个端到端评估大模型推理安全的框架，揭示越大的模型越容易因过度助人而产生实际危害。
SafeRBench: Dissecting the Reasoning Safety of Large Language Models
- 通过分层风险探测和微思考分析，定位模型推理中安全失效的具体环节。
- 19个大模型测试发现，大模型在启用思维模式后反而更易生成可执行的有害方案。
- 适合关注AI安全、伦理对齐的研究者与开发者使用。
大型推理模型（LRMs）通过显式的思维链（CoT）推理显著提升了问题解决能力，但这一特性也带来了安全-有用性悖论：推理过程本身可能被滥用以正当化有害行为或隐藏恶意意图。现有基准大多仅检查最终输出，忽略了风险在内部推理过程中如何演变或‘漂移’。为此，我们提出SafeRBench，首个端到端评估LRM安全性的框架，覆盖从输入到推理轨迹再到最终答案的全过程。其核心包括：(i) 风险分层探测，利用不同风险等级压力测试安全边界；(ii) 微思考分析，一种新的轨迹切片方法，精准定位安全对齐失效点；(iii) 包含10项细粒度指标的综合评测体系，首次联合衡量模型的风险暴露（如风险等级、可执行性）与安全意识（如意图识别）。在19个LRM上的实验表明，启用思维模式虽提升中等规模模型的安全性，却因‘始终助人’倾向反而使大型模型产生更高可执行风险。
原文摘要 · Abstract (English)
Large Reasoning Models (LRMs) have significantly improved problem-solving through explicit Chain-of-Thought (CoT) reasoning. However, this capability creates a Safety-Helpfulness Paradox: the reasoning process itself can be misused to justify harmful actions or conceal malicious intent behind lengthy intermediate steps. Most existing benchmarks only check the final output, missing how risks evolve, or ``drift'', during the model's internal reasoning. To address this, we propose SafeRBench, the first framework to evaluate LRM safety end-to-end, from the initial input to the reasoning trace and final answer. Our approach introduces: (i) a Risk Stratification Probing that uses specific risk levels to stress-test safety boundaries beyond simple topics; (ii) Micro-Thought Analysis, a new chunking method that segments traces to pinpoint exactly where safety alignment breaks down; and (iii) a comprehensive suite of 10 fine-grained metrics that, for the first time, jointly measure a model's Risk Exposure (e.g., risk level, execution feasibility) and Safety Awareness (e.g., intent awareness). Experiments on 19 LRMs reveal that while enabling Thinking modes improves safety in mid-sized models, it paradoxically increases actionable risks in larger models due to a strong always-help tendency.
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