Designing New Operating Primitives to Improve Fuzzing Performance

Fuzzing is a software testing technique that finds bugs by repeatedly injecting mutated inputs to a target program. Known to be a highly practical approach, fuzzing is gaining more popularity than ever before. Current research on fuzzing has focused on producing an input that is more likely to trigger a vulnerability. In this paper, we tackle another way to improve the performance of fuzzing, which is to shorten the execution time of each iteration. We observe that AFL, a state-of-the-art fuzzer, slows down by 24× because of file system contention and the scalability of fork() system call when it runs on 120 cores in parallel. Other fuzzers are expected to suffer from the same scalability bottlenecks in that they follow a similar design pattern. To improve the fuzzing performance, we design and implement three new operating primitives specialized for fuzzing that solve these performance bottlenecks and achieve scalable performance on multi-core machines. Our experiment shows that the proposed primitives speed up AFL and LibFuzzer by 6.1 to 28.9× and 1.1 to 735.7×, respectively, on the overall number of executions per second when targeting Google's fuzzer test suite with 120 cores. In addition, the primitives improve AFL's throughput up to 7.7× with 30 cores, which is a more common setting in data centers. Our fuzzer-agnostic primitives can be easily applied to any fuzzer with fundamental performance improvement and directly benefit large-scale fuzzing and cloud-based fuzzing services.