Precise Detection of Kernel Data Races with Probabilistic Lockset Analysis

Finding data races is critical for ensuring security in modern kernel development. However, finding data races in the kernel is challenging because it requires jointly searching over possible combinations of system calls and concurrent execution schedules. Kernel race testing systems typically perform this search by executing groups of fuzzer seeds from a corpus and applying a combination of schedule fuzzing and dynamic race prediction on traces. However, predicting which combinations of seeds can expose races in the kernel is difficult as fuzzer seeds will usually follow different execution paths when executed concurrently due to inter-thread communications and synchronization.To address this challenge, we introduce a new analysis for kernel race prediction, Probabilistic Lockset Analysis (PLA) that addresses the challenges posed by race prediction for the kernel. PLA leverages the observation that system calls almost always perform certain memory accesses to shared memory to perform their function. PLA uses randomized concurrent trace sampling to identify memory accesses that are performed consistently and estimates the probability of races between them subject to kernel lock synchronization. By prioritizing high probability races, PLA is able to make accurate predictions.We evaluate PLA against comparable kernel race testing methods and show it finds races at a 3× higher rate over 24 hours. We use PLA to find 183 races in linux kernel v5.18-rc5, including 102 harmful races. PLA is able to find races that have severe security impact in heavily tested core kernel modules, including use-after-free in memory management, OOB write in network cryptography, and leaking kernel heap memory information. Some of these vulnerabilities have been overlooking by existing systems for years: one of the races found by PLA involving an OOB write has been present in the kernel since 2013 (version v3.14-rc1) and has been designated a high severity CVE.