MOPT: Optimized Mutation Scheduling for Fuzzers


Chenyang Lyu, Zhejiang University; Shouling Ji, Zhejiang University & Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies; Chao Zhang, BNRist & INSC, Tsinghua University; Yuwei Li, Zhejiang University; Wei-Han Lee, IBM Research; Yu Song, Zhejiang University; Raheem Beyah, Georgia Institute of Technology


Mutation-based fuzzing is one of the most popular vulnerability discovery solutions. Its performance of generating interesting test cases highly depends on the mutation scheduling strategies. However, existing fuzzers usually follow a specific distribution to select mutation operators, which is inefficient in finding vulnerabilities on general programs. Thus, in this paper, we present a novel mutation scheduling scheme MOPT, which enables mutation-based fuzzers to discover vulnerabilities more efficiently. MOPT utilizes a customized Particle Swarm Optimization (PSO) algorithm to find the optimal selection probability distribution of operators with respect to fuzzing effectiveness, and provides a pacemaker fuzzing mode to accelerate the convergence speed of PSO. We applied MOPT to the state-of-the-art fuzzers AFL, AFLFast and VUzzer, and implemented MOPT-AFL, -AFLFast and -VUzzer respectively, and then evaluated them on 13 real world open-source programs. The results showed that, MOPT-AFL could find 170% more security vulnerabilities and 350% more crashes than AFL. MOPT-AFLFast and MOPT-VUzzer also outperform their counterparts. Furthermore, the extensive evaluation also showed that MOPT provides a good rationality, compatibility and steadiness, while introducing negligible costs.

@inproceedings {236282,
title = {{MOPT}: Optimized Mutation Scheduling for Fuzzers},
booktitle = {28th {USENIX} Security Symposium ({USENIX} Security 19)},
year = {2019},
address = {Santa Clara, CA},
url = {},
publisher = {{USENIX} Association},