English(EN) Trusting the Tool: Asymmetric Deference of LLM Vulnerability Auditors to Static-Analysis Evidence

LLM在漏洞检测中对静态分析证据表现出不对称信赖

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新的arXiv论文研究了大型语言模型（LLM）在漏洞检测中与静态分析工具的交互方式。研究发现，LLM表现出不对称信赖，它们会被捏造的漏洞存在保证误导，但不太会受到捏造的漏洞存在警报的影响。这表明当前将LLM与静态分析相结合的流程可能并不可靠，因为模型没有充分审查工具提供的证据。 AI

影响强调了LLM辅助漏洞检测中潜在的不可靠性，表明需要改进AI流程中的证据验证。

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了LLM行为的发现。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · ZhiHong Liu, Shuyao Gao · 2026-10-27 04:00

信任工具：LLM漏洞审计员对静态分析证据的不对称让步

arXiv:2610.32876v1 Announce Type: cross Abstract: Pipelines that combine large language models (LLMs) with static analysis let the model consume the analyser's output as evidence, but whether the model distinguishes true from false evidence, and whether its scrutiny depends on wh…