directed-neuzz

主要的工作是基于aflgo的框架做的改进。 可按照aflgo的流程 1）获取cg、cfg图 2）将距离计算脚本替换为pre-dominate脚本，用于获取dominate节点 3）aflgo/llvm_mode下主要对afl-llvm-pass.so.cc 增加了获取dominate节点的插桩随机值的代码 4）nn1中添加了基于随机值计算出的一些边。

NEUZZ: a neural-network-assisted fuzzer (S&P'19)

See IEEE S&P(Oakland)'19 slides and paper NEUZZ: Efficient Fuzzing with Neural Program Smoothing for details.

Prerequisite

Tested on a machine with Nvidia 1080Ti, Ubuntu 16.04/18.04, Tensorflow 1.8.0 and Keras 2.2.3.
We recommend running NEUZZ on a machine with a Nvidia 1080Ti or higher for efficient NN training.

• Python 2.7
• Tensorflow
• Keras

Build

    gcc -O3 -funroll-loops ./neuzz.c -o neuzz

Usage

We use a sample program readelf as an example.
Open a terminal, start nn module

    #python nn.py [program [arguments]]
    python nn.py ./readelf -a

open another terminal, start neuzz module.

    #./neuzz -i in_dir -o out_dir -l mutation_len [program path [arguments]] @@
    ./neuzz -i neuzz_in -o seeds -l 7506 ./readelf -a @@  

If you want to try NEUZZ on a new program,

1. Compile the new program from source code using afl-gcc.
2. Collect the training data by running AFL on the binary for a while(about an hour), then copy the queue folder to neuzz_in.
3. Follow the above two steps to start NN module and NEUZZ module.

Sample programs

Try 10 real-world programs on NEUZZ. Check setup details at programs/[program names]/README.

Contact

Feel free to send me email about Neuzz. dongdong at cs.columbia.edu