A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing
Sanan Hasanov, Stefan Nagy, Paul Gazzillo
摘要
The Linux kernel is actively-developed and widely-used. It supports billions of devices of all classes, from high-performance computing to the Internet-of-Things, in part because of its sophisticated configuration system, which automatically tailors the source code according to thousands of user-provided configuration options. Fuzzing has been highly successful at finding kernel bugs, being among the top bug reporters. Since the kernel receives 100s of patches per day, fuzzers run continuously, stopping regularly to rebuild the kernel with the latest changes before restarting fuzzing. But kernel fuzzers currently use predefined configuration settings that, as we show, exclude the majority of new patches from the kernel binary, nullifying the benefits of continuous fuzzing. Unfortunately, state-of-the-art configuration testing techniques are generally ill-suited to the needs of continuous fuzzing, excluding necessary options or requiring too many configuration files to be tractable. We distill down the needs of continuous testing into six properties with the most impact, systematically analyze the space of configuration selection strategies, and provide actionable recommendations. Through our analysis, we discover that continuous fuzzers can improve configuration variety without sacrificing performance. We empirically evaluate our discovery by modifying the configuration selection strategy for syzkaller, the most popular Linux kernel fuzzer, which subsequently found more than twice as many new bugs (35 vs. 13) than with the original configuration file and 12x more (24 vs. 2) when considering only unique bugs-with one security vulnerability being assigned a CVE.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- Inferring 1-Minimal Trigger Configurations for Assessing Linux Kernel CVE TriggerabilityTongjie Wei, Peng Zhang, Zhiwen Hu, Xupu Hu 等ISSTA 2026
- SyzParam: Incorporating Runtime Parameters into Kernel Driver FuzzingYue Sun, Yan Kang, Chenggang Wu, Kangjie Lu 等CCS 2025
- Towards Better Linux Kernel Fault Localization: Leveraging Contrastive Reasoning and Hierarchical Context AnalysisHaichi Wang, Ruiguo Yu, Yesong Pang, Yingquan Zhao 等ICSE 2026
它引用的顶会 Paper30
- Evaluating Fuzz TestingGeorge Klees, Andrew Ruef, Benji Cooper, Shiyi Wei 等CCS 2018 · 被引用 753 次
- kAFL: Hardware-Assisted Feedback Fuzzing for OS KernelsSergej Schumilo, Cornelius Aschermann, Robert Gawlik, Sebastian Schinzel 等USENIX Security 2017 · 被引用 324 次
- Razzer: Finding Kernel Race Bugs through FuzzingDae R. Jeong, Kyungtae Kim, Basavesh Shivakumar, Byoungyoung Lee 等S&P 2019 · 被引用 202 次
- DIFUZE: Interface Aware Fuzzing for Kernel DriversJake Corina, Aravind Machiry, Christopher Salls, Yan Shoshitaishvili 等CCS 2017 · 被引用 195 次
- Full-Speed Fuzzing: Reducing Fuzzing Overhead through Coverage-Guided TracingStefan Nagy, Matthew HicksS&P 2019 · 被引用 156 次
相关 Paper
- Thunderkaller: Profiling and Improving the Performance of SyzkallerYang Lan, Di Jin, Zhun Wang, Wende Tan 等ASE 2023 · 被引用 2 次
- SyzVegas: Beating Kernel Fuzzing Odds with Reinforcement LearningDaimeng Wang, Zheng Zhang, Hang Zhang, Zhiyun Qian 等USENIX Security 2021 · 被引用 75 次
- SYSYPHUZZ: the Pressure of More CoverageZezhong Ren, Han Zheng, Zhiyao Feng, Qinying Wang 等NDSS 2026 · 被引用 1 次
- Fuzzing File Systems via Two-Dimensional Input Space ExplorationWen Xu, Hyungon Moon, Sanidhya Kashyap, Po-Ning Tseng 等S&P 2019 · 被引用 117 次
- SyzGen++: Dependency Inference for Augmenting Kernel Driver FuzzingWeiteng Chen, Yu Hao, Zheng Zhang, Xiaochen Zou 等S&P 2024 · 被引用 12 次
