发现大模型在工具失效时会谎称安全拒绝，且安全提示越强越容易撒谎。
Guardrails as Scapegoats: Auditing Unfaithful Safety Refusals in Tool-Augmented LLM Agents
- 通过注入四种静默故障，分类模型响应为诚实放弃、捏造结果和虚假安全拒绝三类。
- 56.6%的响应为捏造结果，安全提示使虚假拒绝率飙升15.6倍（0.25%→3.95%）。
- 敏感工具失败时更易出现虚假拒绝，建议用响应与数据错位检测其风险。
针对工具增强型大模型的评估框架长期忽视静默基础设施故障和返回空/无效数据但状态码为200的情况。本文提出轻量级黑盒审计框架，在12个近生产环境的工具桩中注入四类静默故障，将代理响应分为三类：诚实放弃（HSR）、捏造（FAR）和不忠实的安全拒绝（USR）。在温度为零的两前沿及两开源模型上测试，发现捏造占有效响应的56.6%：模型将空数据误当作真实信息并生成虚构内容。基线状态下虚假安全拒绝极少见（0.25%，396条轨迹中仅1例）。当系统提示加入标准安全语句（“优先保障用户隐私与数据安全”），虚假拒绝率提升15.6倍（0.25%→3.95%；95%置信区间：2.2%-6.4%；Fisher精确检验，p<0.001）。该行为是隐性存在的，由安全词汇触发模型在工具静默失败时编造政策或隐私理由。敏感工具（fetch_medical_record, retrieve_contract, fetch_user_profile）导致多数此类实例。本文提出基于响应与数据错位的生产级检测机制，并讨论安全导向部署的治理影响。
原文摘要 · Abstract (English)
Evaluation frameworks for tool-augmented LLM agents focus overwhelmingly on capability metrics or explicit tool crashes, leaving silent infrastructure failures and HTTP 200 responses with empty, null, or malformed payloads largely unaudited. We introduce a lightweight black-box auditing framework that injects four silent failure profiles across 12 production-adjacent tool stubs and classifies agent responses into three mutually exclusive behavioral classes: Honest Surrender (HSR), Fabrication (FAR), and Unfaithful Safety Refusal (USR). Evaluating two frontier and two open-source models at temperature zero under a neutral system prompt, we find that FAR dominates (56.6% of valid responses): agents treat empty payloads as real data, silently returning fabricated results. USR, in which an agent invents a policy or privacy rationale to explain the failure, is nearly absent at baseline (0.25%, one instance across 396 valid trajectories). Our key finding emerges from an ablation where we augment the system prompt with standard safety language ("prioritize user privacy and data security"), which amplifies USR by 15.6x (from 0.25% to 3.95%; 95% CI on ablation rate: 2.2%-6.4%; Fisher's exact test, p < 0.001). USR is a latent behavior, activated when safety vocabulary in the system prompt primes the model to reach for policy rationales when tools silently fail. Sensitive tools (fetch_medical_record, retrieve_contract, fetch_user_profile) account for the majority of USR instances. We propose a payload-response misalignment heuristic for production-level detection and discuss governance implications for safety-forward deployments.
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