A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing

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.

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Publication Details

Published
2025-04-26
DOI
https://doi.org/10.1109/icse55347.2025.00042
Citations
5
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
6.07

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article

A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing

Paul Gazzillo, Stefan Nagy, Sanan Hasanov
5 citations
Fault Detection and Control Systems
6.07
article

A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing

Paul Gazzillo, Stefan Nagy, Sanan Hasanov
article en
5 citations

Abstract

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.

University of Central Florida (US), University of Utah (US)
National Science Foundation
Openalex Percentile: Top 4%
Fault Detection and Control Systems
6.07
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