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Graphuzz

Graphuzz has four components：

  • FUZZER: for FUZZING
  • MODEL: for MODEL training
  • PREPROCESS: for INSTRUMENTATION and seed ESTIMATION

FUZZING WORKFLOWS

BUILD PREPROCESS Make sure that clang (version 11) is installed.

cd PATH_TO_Graphuzz/PREPROCESS
make
cd PATH_TO_Graphuzz/FUZZER
make

Compile target, using the normal OSS-FUZZ target as the example.

export CFLAGS=""
export CXXFLAGS=""
export CC=PATH_TO_Graphuzz/PREPROCESS/afl-clang-fast
export CXX=PATH_TO_Graphuzz/PREPROCESS/afl-clang-fast++
export OUT=`pwd`/out
export LIB_FUZZING_ENGINE=PATH_TO_Graphuzz/FUZZER/libAFLDriver.a
export AFL_MAP_SIZE=65536
export FUZZER_LIB=PATH_TO_Graphuzz/FUZZER/libAFLDriver.a
export AFL_LLVM_INSTRUMENT=CLASSIC
export CDF_FILE_DIR=`pwd`/CDF
export SRC=`pwd`
mkdir CDF
mkdir out

Then, cd to the source code directory of the target and follow the build.sh from OSS-FUZZ. After compilation, we will get the graph information of the target in the directory set by CDF_FILE_PATH. We need to aggreagate the e-CFG of the target by

cd $CDF_FILE_PATH
python PATH_TO_Graphuzz/FUZZER/nn_server/exportTrainingSet.py $CDF_FILE_PATH PATH_TO_Graphuzz/FUZZER/nn_server/

Now we will get the CDFG.pkl in the directory PATH_TO_Graphuzz/FUZZER/nn_server/

Now we can start the fuzzing process. Start a terminal and run

cd PATH_TO_Graphuzz/FUZZER/nn_server && python nn_server.py

And, start a new terminal and run

#chmod +x $CurDir/nn_server.sh
#nohup $CurDir/nn_server.sh &
export AFL_NO_AFFINITY=1
export AFL_NO_UI=1
/PATH_TO_Graphuzz/FUZZER/afl-fuzz -i path_to_seeds -o path_to_output -K /tmp/nn_server.sock -- target

TRAINING SET GENERATION

  1. Prepare enough seeds (more than 2048 seeds)
  2. Run each seed for 30 minutes (as described in our paper), and record the initial bitmap and last bitmap. We provide a program to do this work (/PATH_TO_Graphuzz/PREPROCESS/afl-evaluator). Use this program like ./afl-evaluator -I ~/TEST/pcapplusplus/seeds/default/queue -O ~/TEST/pcapplusplus/output_30 -B ~/TEST/pcapplusplus/FuzzTarget.
  3. Generate TRAINING SET. For example, run python3 /PATH_TO_Graphuzz/PREPROCESS/Extractor/exportTrainingSet.py THE_DIR_OF_CDFG.PKL THE_DIR_OF_CDFG.PKL ~/TEST/pcapplusplus/output_30 ~/TRAININGSET/trainingset_pcapplusplus.
  4. Normalize the labels. python preprocess.py ~/TRAININGSET/trainingset_pcapplusplus ~/TRAININGSET/trainingset_pcapplusplus_norm
  5. Sampling multiple software generated training sets and composing a new hybrid training set. For example, for x in {A,B,C,D}; do ls ~/TRAININGSET/trainingset_${x}_norm | sort -R | head -30000 | xargs -I {} cp ~/TRAININGSET/trainingset_$x_norm/{} ~/trainingset_cross/ ; done