DAFuzz: data-aware fuzzing of in-memory data stores
The process is similar to Superion (https://github.com/zhunki/Superion), but we changed to a newer version of ANTLR. After installation, you should get fuzzer binaries including afl-fuzz-redis, and afl-fuzz-memcached.
Command like:
USE_RAW_FORMAT=1 LD_PRELOAD=/<pathto>/desockmulti.so LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/<pathtothefuzzer>/tree_mutation/redis/:./ /<pathtothefuzzer>/afl-fuzz-redis -D -F redis_command.json -G redis_command_related.json -d -m 1G -x redis_zeng2.dict -i testcase_dir -o findings_dir_me_d -- ./redis-server --dbfilename zeng2.rdb --bind 127.0.0.1
Please cite our paper if you use it in research.
Zeng Y, Zhu F, Zhang S, Yang Y, Yi S, Pan Y, Xie G, Wu T. 2023. DAFuzz: data-aware fuzzing of in-memory data stores. PeerJ Computer Science 9:e1592 https://doi.org/10.7717/peerj-cs.1592
bibtex
@article{Zeng:DAFuzz:2023,
title={DAFuzz: data-aware fuzzing of in-memory data stores},
author={Zeng, Yingpei and Zhu, Fengming and Zhang, Siyi and Yang, Yu and Yi, Siyu and Pan, Yufan and Xie, Guojie and Wu, Ting},
journal={PeerJ Computer Science},
volume={9},
pages={e1592},
year={2023},
publisher={PeerJ Inc.}
}