S&P 2024
Conference paper

SyzGen++: Dependency Inference for Augmenting Kernel Driver Fuzzing


In recent years, kernel fuzzing research has experienced a significant surge. Among various kernel fuzzers, Syzkaller stands out as the state-of-the-art tool, having identified over 5,000 bugs in the Linux kernel. Syzkaller’s success can be attributed to its utilization of manually-curated syscall specifications provided by kernel experts. However, this process is time-consuming and not scalable due to complex input structures and unknown dependencies among syscalls. Consequently, a substantial portion of the kernel codebase, specifically kernel drivers, lacks specifications, posing a significant security risk. In this paper, we introduce SyzGen++, an innovative approach for automatically inferring dependencies between syscalls and generating specifications without relying on existing test suites. Specifically, we define two fundamental building blocks of insertion and lookup operations and their pairing to accurately identify dependencies. We evaluated SyzGen++ against existing state-of-the-art techniques on both Linux and macOS drivers. Our results demonstrate that SyzGen++ uncovered 245 more dependencies. Furthermore, SyzGen++ outperforms DIFUZE, KSG, and SyzDescribe in terms of code coverage, achieving 71%, 67%, and 39% improvement on average, respectively. Notably, our evaluation discovered 10 previously unknown bugs in Linux Kernel 6.2 using specifications generated by SyzGen++, resulting in 6 CVEs, which demonstrates its effectiveness in identifying vulnerabilities.