大模型文本安全不等于工具调用安全，需单独评估。
Mind the GAP: Text Safety Does Not Transfer to Tool-Call Safety in LLM Agents
- 构建GAP基准，对比文本与工具调用的安全性差异。
- 6个模型在6个领域中均出现文本拒绝但工具执行违规行为。
- 系统提示词影响显著，部分模型工具调用安全率相差57个百分点。
部署为智能体的大语言模型越来越多地通过工具调用与外部系统交互——这些操作具有真实世界后果，而仅靠文本输出无法体现。然而，当前的安全评估几乎全以文本拒绝行为为标准，未回答关键问题：抑制有害文本的对齐是否也能抑制有害操作？我们提出GAP基准，系统评估大模型智能体在文本级安全与工具调用级安全之间的偏差。测试了六个前沿模型在六个受监管领域（制药、金融、教育、就业、法律、基础设施）中的表现，每个领域包含七种越狱场景，三种系统提示条件（中立、强化安全、鼓励工具使用），以及两种提示变体，共生成17,420个可分析数据点。核心发现是：文本安全无法转移到工具调用安全。所有六种模型均出现文本拒绝但工具调用仍执行禁用动作的情况，我们将其形式化为GAP指标。即使在强化安全提示下，仍有219例此类情况持续存在。系统提示词措辞对工具调用行为有显著影响：最稳健模型的工具调用安全率跨度达21个百分点，最敏感模型达57个百分点，16组成对消融比较在邦弗朗尼校正后仍显著。运行时治理合约虽能减少信息泄露，但对禁止工具调用尝试无明显威慑效果。结果表明，仅依赖文本安全评估不足以判断智能体行为，工具调用安全必须独立测量与干预。
原文摘要 · Abstract (English)
Large language models deployed as agents increasingly interact with external systems through tool calls--actions with real-world consequences that text outputs alone do not carry. Safety evaluations, however, overwhelmingly measure text-level refusal behavior, leaving a critical question unanswered: does alignment that suppresses harmful text also suppress harmful actions? We introduce the GAP benchmark, a systematic evaluation framework that measures divergence between text-level safety and tool-call-level safety in LLM agents. We test six frontier models across six regulated domains (pharmaceutical, financial, educational, employment, legal, and infrastructure), seven jailbreak scenarios per domain, three system prompt conditions (neutral, safety-reinforced, and tool-encouraging), and two prompt variants, producing 17,420 analysis-ready datapoints. Our central finding is that text safety does not transfer to tool-call safety. Across all six models, we observe instances where the model's text output refuses a harmful request while its tool calls simultaneously execute the forbidden action--a divergence we formalize as the GAP metric. Even under safety-reinforced system prompts, 219 such cases persist across all six models. System prompt wording exerts substantial influence on tool-call behavior: TC-safe rates span 21 percentage points for the most robust model and 57 for the most prompt-sensitive, with 16 of 18 pairwise ablation comparisons remaining significant after Bonferroni correction. Runtime governance contracts reduce information leakage in all six models but produce no detectable deterrent effect on forbidden tool-call attempts themselves. These results demonstrate that text-only safety evaluations are insufficient for assessing agent behavior and that tool-call safety requires dedicated measurement and mitigation.
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