MOpt-AFL is a AFL-based fuzzer that utilizes a customized Particle Swarm Optimization (PSO) algorithm to find the optimal selection probability distribution of operators with respect to fuzzing effectiveness. More details can be found in the technical report. The installation of MOpt-AFL is the same as AFL's.
Chenyang Lyu, Shouling Ji, Chao Zhang, Yuwei Li, Wei-Han Lee, Yu Song and Raheem Beyah, MOPT: Optimized Mutation Scheduling for Fuzzers, USENIX Security 2019.
We open source all the seed sets used in the paper "MOPT: Optimized Mutation Scheduling for Fuzzers".
The experiment results can be found in https://drive.google.com/drive/folders/184GOzkZGls1H2NuLuUfSp9gfqp1E2-lL?usp=sharing. We only open source the crash files since the space is limited.
MOpt_TechReport.pdf is the technical report of the paper "MOPT: Optimized Mutation Scheduling for Fuzzers", which contains more deatails.
Most important, you must add the parameter -L
(e.g., -L 0
) to launch the MOpt scheme.
-L
controls the time to move on to the pacemaker fuzzing mode.
-L t:
when MOpt-AFL finishes the mutation of one input, if it has not discovered any new unique crash or path for more than t min, MOpt-AFL will enter the pacemaker fuzzing mode.
Setting 0 will enter the pacemaker fuzzing mode at first, which is recommended in a short time-scale evaluation.
Other important parameters can be found in afl-fuzz.c, for instance,
swarm_num:
the number of the PSO swarms used in the fuzzing process.
period_pilot:
how many times MOpt-AFL will execute the target program in the pilot fuzzing module, then it will enter the core fuzzing module.
period_core:
how many times MOpt-AFL will execute the target program in the core fuzzing module, then it will enter the PSO updating module.
limit_time_bound:
control how many interesting test cases need to be found before MOpt-AFL quits the pacemaker fuzzing mode and reuses the deterministic stage.
0 < limit_time_bound
< 1, MOpt-AFL-tmp. limit_time_bound
>= 1, MOpt-AFL-ever.
Having fun with MOpt-AFL.
@inproceedings {236282,
author = {Chenyang Lyu and Shouling Ji and Chao Zhang and Yuwei Li and Wei-Han Lee and Yu Song and Raheem Beyah},
title = {{MOPT}: Optimized Mutation Scheduling for Fuzzers},
booktitle = {28th {USENIX} Security Symposium ({USENIX} Security 19)},
year = {2019},
isbn = {978-1-939133-06-9},
address = {Santa Clara, CA},
pages = {1949--1966},
url = {https://www.usenix.org/conference/usenixsecurity19/presentation/lyu},
publisher = {{USENIX} Association},
month = aug,
}