Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing

Jinghan Wang (University of California, Riverside), Chengyu Song (University of California, Riverside), Heng Yin (University of California, Riverside)

Coverage metrics play an essential role in greybox fuzzing. Recent work has shown that fine-grained coverage metrics could allow a fuzzer to detect bugs that cannot be covered by traditional edge coverage. However, fine-grained coverage metrics will also select more seeds, which cannot be efficiently scheduled by existing algorithms. This work addresses this problem by introducing a new concept of multi-level coverage metric and the corresponding reinforcement-learning-based hierarchical scheduler. Evaluation of our prototype on DARPA CGC showed that our approach outperforms AFL and AFLFast significantly: it can detect 20% more bugs, achieve higher coverage on 83 out of 180 challenges, and achieve the same coverage on 60 challenges. More importantly, it can detect the same number of bugs and achieve the same coverage faster. On FuzzBench, our approach achieves higher coverage than AFL++ (Qemu) on 10 out of 20 projects.