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English(EN) APTInvestBench: Evaluating Autonomous APT Investigation under Varying Telemetry

新的基准测试评估LLM代理在不同遥测设置下的APT调查能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了APTInvestBench，这是一个新的基准测试，旨在评估大型语言模型（LLM）代理在不同遥测设置下调查高级持续性威胁（APT）的鲁棒性。该基准测试包含370个案例，源自56次攻击重建，总计超过1600万条日志记录。对十一个LLM进行的初步测试表明，代理能够为平均44.3%的攻击行为获取足够证据，但正式引用仅支持25.0%，这凸显了证据可靠性和跨遥测稳定性方面存在的显著差距。 AI

影响 该基准测试旨在提高用于网络安全威胁调查的AI代理的可靠性和鲁棒性。

排序理由 该集群描述了一个用于评估AI代理的新基准测试，该基准测试发表在一篇学术论文中。

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新的基准测试评估LLM代理在不同遥测设置下的APT调查能力

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yu Wang, Shuhao Li, Tao Yin, Ziyang Li, Xueying Zhao, Peishuai Sun, Jiang Xie ·

    APTInvestBench：在不同遥测数据下评估自主APT调查

    arXiv:2609.38954v1 Announce Type: cross Abstract: Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads into evidence for intrusion scoping and response. Yet success under one telemetry…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    APTInvestBench：在不同遥测数据下评估自主APT调查

    Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads into evidence for intrusion scoping and response. Yet success under one telemetry setting does not establish robustness to changes …

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