English(EN) CAROL: Context-Aware Online Learning for Fuzzer Scheduling

CAROL调度器利用在线学习发现更多软件漏洞

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了CAROL，一种旨在优化集成模糊测试的新型在线学习调度器。与依赖固定规则和过往性能摘要的现有调度器不同，CAROL利用模糊器的当前上下文，包括奖励趋势、等待时间和代码覆盖率。这种上下文感知的方法使CAROL能够根据模糊器是正在改进还是停滞不前来调整其学习规则，从而发现更多独特的漏洞。在对九个Magma目标和五个C++程序的测试中，CAROL的表现显著优于基线调度器，发现了先前未知的崩溃缺陷。 AI

影响这种上下文感知的学习方法可以提高自动化软件测试和漏洞发现的效率。

排序理由该集群描述了一篇详细介绍模糊器调度新算法的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 6 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇详细介绍模糊器调度新算法的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zirui Liu, Mengfan Xu, Juan Zhai, Shenglong Yao, Shiqing Ma · 2026-09-10 04:00

CAROL：用于模糊测试调度的上下文感知在线学习

arXiv:2609.06772v1 Announce Type: cross Abstract: Ensemble fuzzing runs multiple fuzzers on a target while a scheduler allocates CPU time among them. Existing schedulers base these decisions on compact summaries of past performance and rules fixed before a campaign. Our measureme…

本文如何被排名

报道来源 [1]

CAROL：用于模糊测试调度的上下文感知在线学习

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