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arXiv 2606.04460cs.CRcs.AIcs.LG
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CyberGym-E2E:面向AI代理端到端网络安全能力的可扩展真实世界基准

CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities

Stanford University(斯坦福大学);UC Berkeley(加州大学伯克利分校)
Tianneng Shi, Robin Rheem, Dongwei Jiang, Mona Wang, Francisco De La Riega, Zhun Wang, Jingzhi Jiang, Alexander Cheung, Sean Tai, Jonah Cha, Jianhong Tu, Gabrie… 展开作者

Tianneng Shi, Robin Rheem, Dongwei Jiang, Mona Wang, Francisco De La Riega, Zhun Wang, Jingzhi Jiang, Alexander Cheung, Sean Tai, Jonah Cha, Jianhong Tu, Gabriel Han, Chenguang Wang, Jingxuan He, Wenbo Guo, Dawn Song

AI总结:

提出CyberGym-E2E,一个大规模、真实的端到端网络安全基准,通过自动化流水线将开源漏洞数据转化为评估环境,全面评估AI代理在漏洞发现、PoC生成和补丁生成全生命周期中的能力。

摘要:

人工智能有潜力通过使系统能够自主检测、分析和修复软件漏洞来改变网络安全。然而,现有对AI系统的网络安全评估在规模或范围上有限,未能捕捉真实世界软件漏洞发现和修复的端到端生命周期。为了解决这一差距,我们提出了CyberGym-E2E,一个大规模、真实的端到端网络安全基准,全面评估AI代理在漏洞发现、PoC生成和补丁生成整个生命周期中的能力。CyberGym-E2E全面且可扩展,因为我们构建了一个自动化的、代理增强的流水线,用于将开源漏洞数据转化为真实的评估环境。目前,该基准包含139个不同开源项目中的920个真实世界漏洞。

英文摘要:

AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity evaluations of AI systems are limited in scale or scope, and fail to capture the end-to-end lifecycle of real-world software vulnerability discovery and remediation. To address this gap, we propose CyberGym-E2E, a large-scale and realistic end-to-end cybersecurity benchmark that comprehensively evaluates AI agents' abilities across the full lifecycle of vulnerability discovery, PoC generation, and patch generation. CyberGym-E2E is comprehensive and scalable, as we build an automated, agent-enhanced pipeline for transforming open-source vulnerability data into realistic evaluation environments. Currently, the benchmark consists of 920 real-world vulnerabilities across 139 different open-source projects.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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