ORFuzz: 测试LLM安全的'另一面' -- 检测过度拒绝
ORFuzz: Fuzzing the "Other Side" of LLM Safety -- Testing Over-Refusal
AI总结：
ORFuzz通过进化测试框架系统检测LLM的过度拒绝现象，生成高覆盖率的测试用例并建立新基准，提升LLM安全性。
摘要：
大型语言模型（LLMs）越来越多地表现出过度拒绝的现象——由于过于保守的安全措施而错误地拒绝良性查询，这是一种关键的功能缺陷，削弱了其可靠性和可用性。当前测试这种行为的方法明显不足，受到基准错误和测试生成能力有限的限制，这在我们的实证用户研究中得到了强调。据我们所知，本文介绍了首个进化测试框架ORFuzz，用于系统地检测和分析LLM的过度拒绝。ORFuzz独特地集成了三个核心组件：（1）安全类别感知的种子选择以实现全面的测试覆盖，（2）使用推理LLM进行自适应变异器优化以生成有效的测试用例，（3）OR-Judge，一个与人类对齐的判断模型，已验证能够准确反映用户对毒性及拒绝的感知。我们的广泛评估表明，ORFuzz生成的过度拒绝实例的比率（平均6.98%）比领先基线高出一倍多，有效地揭示了漏洞。此外，ORFuzz的输出构成了ORFuzzSet，一个包含1,855个高度可转移的测试用例的新基准，在10种不同的LLM上实现了平均63.56%的过度拒绝率，显著优于现有数据集。ORFuzz和ORFuzzSet提供了一个稳健的自动化测试框架和有价值的社区资源，为开发更可靠和可信赖的LLM软件系统铺平了道路。
英文摘要：
Large Language Models (LLMs) increasingly exhibit over-refusal - erroneously rejecting benign queries due to overly conservative safety measures - a critical functional flaw that undermines their reliability and usability. Current methods for testing this behavior are demonstrably inadequate, suffering from flawed benchmarks and limited test generation capabilities, as highlighted by our empirical user study. To the best of our knowledge, this paper introduces the first evolutionary testing framework, ORFuzz, for the systematic detection and analysis of LLM over-refusals. ORFuzz uniquely integrates three core components: (1) safety category-aware seed selection for comprehensive test coverage, (2) adaptive mutator optimization using reasoning LLMs to generate effective test cases, and (3) OR-Judge, a human-aligned judge model validated to accurately reflect user perception of toxicity and refusal. Our extensive evaluations demonstrate that ORFuzz generates diverse, validated over-refusal instances at a rate (6.98% average) more than double that of leading baselines, effectively uncovering vulnerabilities. Furthermore, ORFuzz's outputs form the basis of ORFuzzSet, a new benchmark of 1,855 highly transferable test cases that achieves a superior 63.56% average over-refusal rate across 10 diverse LLMs, significantly outperforming existing datasets. ORFuzz and ORFuzzSet provide a robust automated testing framework and a valuable community resource, paving the way for developing more reliable and trustworthy LLM-based software systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
