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

Paper recorded by Signals 4 on 2026-10-23 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26

Category: cs.LG · 机器学习 · first seen 2026-10-26

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 what the evidence claims, has not been measured. We built an intraprocedural analyser for C/C++ that produces proof obligations and audited vulnerable and patched functions

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#189 most recent of 749 cs.LG papers we have recorded · ↑ newer: Reinforcement Learning with Multi-Step Transition Predictions: A Bellm · ↓ older: Sustainability of TSFM Deployment and Pretraining: From Carbon Footpri
Cite this page: Trusting the Tool: Asymmetric Deference of LLM Vulnerability Auditors to Static-Analysis Evidence: the #189 most recent of 749 cs.LG papers we have recorded (as of 2026-10-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/trusting-the-tool-asymmetric-deference-of-llm-vulnerability-auditors-to-static-a.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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