English(EN) SyzHarness: Patch-Based Kernel Bug Reproduction with LLM-Synthesized Fuzzing Harnesses

LLM驱动的SyzHarness框架改进Linux内核错误复现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了SyzHarness，一个旨在改进内核漏洞复现的新框架。该系统结合了大型语言模型（LLM）推理和覆盖率引导的模糊测试，以创建参数化模糊测试Harness。然后，这些Harness用于精炼错误关键输入参数，旨在克服现有定向模糊测试和仅LLM生成方法的局限性。 AI

影响增强了自动化的内核漏洞复现能力，可能加速操作系统补丁验证和回归测试。

排序理由该集群包含一篇学术论文，详细介绍了一个用于内核漏洞复现的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 19 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了一个用于内核漏洞复现的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; 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.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

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arXiv cs.AI TIER_1 English(EN) · Xingyu Li, Juefei Pu, Haonan Li, Arrdya Srivastav, Kareem Shehada, Srikanth V. Krishnamurthy, Zhiyun Qian · 2026-10-02 04:00

SyzHarness：基于补丁的内核 Bug 复现与 LLM 合成模糊测试 Harness

arXiv:2609.23889v3 Announce Type: replace-cross Abstract: Automated kernel vulnerability reproduction is essential for bug triage, patch validation, and regression testing, but still lacks an effective and efficient solution. The core challenge is twofold: a reproducer must first…