English(EN) Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

新的RL框架VulAgentRL检测过程间软件漏洞

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-31 04:00

研究人员开发了VulAgentRL，一个新颖的智能体强化学习框架，旨在检测跨越多个函数的软件漏洞。与现有孤立分析函数的方法不同，VulAgentRL利用代码属性图（CPG）使模型能够跨函数调用、数据流和其他过程间关系收集和验证证据。这种方法确保模型的判断有引用的证据支持，与最先进的基线相比，即使在分布外数据集上，也能提高准确性并减少不必要的工具调用。 AI

影响增强了AI检测复杂软件漏洞的能力，可能提高代码安全性。

排序理由详细介绍一种新漏洞检测方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yikun Li, Ting Zhang, Jiakun Liu, Jinfeng Jiang, Yuheng Yieh, Yixin Yang, Wen Bin Leow, Yide Yin, Yintong Huo, Eng Lieh Ouh, Lwin Khin Shar, David Lo · 2026-07-31 04:00

图是验证器：用于过程间漏洞检测的智能体强化学习

arXiv:2607.26656v1 Announce Type: cross Abstract: Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vulnerable functions require evidence from outside th…

本文如何被排名

报道来源 [1]

图是验证器：用于过程间漏洞检测的智能体强化学习

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