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English(EN) AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation

调查详述了 AI 乱象和漏洞评估中的幻觉问题

一篇新发表在 arXiv 上的调查论文探讨了 AI 驱动的漏洞评估中“AI 乱象”和幻觉的问题。该论文指出了一个核心问题:人类安全专家的演绎推理与当前大型语言模型的概率生成之间的差异。虽然像思维链提示和工具使用代理等技术可以减轻这些问题,但并不能完全解决它们。作者们提议转向主动神经符号验证,并引入了两个评估工具 CVE-Bench 和 Slop-Score,以衡量准确性而非仅仅是语言流畅性。 AI

影响 强调了在网络安全等关键领域对可验证 AI 系统的需求,推动采用神经符号方法而非纯粹的概率模型。

排序理由 该集群包含一篇详细介绍研究发现并提出新评估方法的调查论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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调查详述了 AI 乱象和漏洞评估中的幻觉问题

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该集群包含一篇详细介绍研究发现并提出新评估方法的调查论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
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Same-day
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Junchen Ding, Jialiang Dong, Yichen Zhu, Yi Liu, Gelei Deng, Willy Susilo, Siqi Ma, Yuekang Li ·

    人工智能在漏洞评估中的“胡说八道”与幻觉:关于推理失败和可信缓解措施的调查

    arXiv:2608.25667v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into cybersecurity has transformed vulnerability assessment, but it has also produced a trustworthiness crisis driven by the unchecked proliferation of "AI slop." These artif…