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新基准测试AI代理定位软件漏洞的能力

研究人员推出了Vulnerability Localization Benchmark (VLoc Bench),这是一个新的数据集,旨在评估AI代理在软件仓库中识别与安全漏洞相关的特定代码位置的能力。该基准包含290个仓库和147个通用弱点枚举 (CWE) 类别的500个真实世界漏洞,测试代理精确定位受影响文件的能力。对27个语言模型和4个静态分析工具的初步评估显示,仓库规模的漏洞定位仍然是一个重大挑战,表现最好的系统仅达到0.229的文件F1分数。 AI

影响 为AI安全代理建立了新的评估标准,突出了代码级漏洞识别方面的现有局限性。

排序理由 该集群描述了一个用于评估AI代理在特定任务上的新学术基准,发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新基准测试AI代理定位软件漏洞的能力

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该集群描述了一个用于评估AI代理在特定任务上的新学术基准,发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Aman Priyanshu, Supriti Vijay, Kimia Majd, Xuhong He, Fraser Burch, Takahiro Matsumoto, Jianliang He, Baturay Saglam, Arthur Goldblatt, Zhuoran Yang, Amin Karbasi ·

    漏洞定位基准:衡量代码库规模下的Agentic安全分析能力

    arXiv:2609.15939v1 Announce Type: cross Abstract: Language-model agents increasingly operate over complete software repositories, yet cybersecurity evaluations primarily measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the …