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LLMs show asymmetric deference to static analysis evidence in vulnerability detection

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper investigates how large language models (LLMs) interact with static analysis tools for vulnerability detection. The study found that LLMs exhibit asymmetric deference, being significantly misled by fabricated assurances that a vulnerability exists, while being less affected by fabricated alarms that a vulnerability is present. This suggests that current pipelines combining LLMs with static analysis may be unreliable, as models do not sufficiently scrutinize the evidence provided by the tools. AI

IMPACT Highlights potential unreliability in LLM-assisted vulnerability detection, suggesting a need for improved evidence verification in AI pipelines.

RANK_REASON The cluster contains a research paper published on arXiv detailing findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show asymmetric deference to static analysis evidence in vulnerability detection

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The cluster contains a research paper published on arXiv detailing findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · ZhiHong Liu, Shuyao Gao ·

    Trusting the Tool: Asymmetric Deference of LLM Vulnerability Auditors to Static-Analysis Evidence

    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…

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