Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference

Related tags

Deep Learningfuzzer
Overview

Ankou

Ankou is a source-based grey-box fuzzer. It intends to use a more rich fitness function by going beyond simple branch coverage and considering the combination of branches during program execution. The details of the technique can be found in our paper "Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference", which is published in ICSE 2020.

Dependencies.

Go

Ankou is written solely in Go and thus requires its installation. Be sure to configure this GOPATH environment variable, for example to ~/go directory.

AFL

Ankou relies on AFL instrumentation: fuzzed targets needs to compiled using afl-gcc or afl-clang. To install AFL:

wget http://lcamtuf.coredump.cx/afl/releases/afl-latest.tgz
tar xf afl-latest.tgz
cd afl-2.52b
make
# The last command is optional, but you'll need to provide the absolute path to
# the compiler in the configure step below if you don't install AFL compiler.
sudo make install

GDB

For the triaging gdb is required, and ASLR needs to be deactivated:

sudo echo 0 | sudo tee /proc/sys/kernel/randomize_va_space

Note that when using docker containers, this needs to be run in the host.

Installation

Once Go and AFL are installed, you can get Ankou by:

go get github.com/SoftSec-KAIST/Ankou   # Clone Ankou and its dependencies
go build github.com/SoftSec-KAIST/Ankou # Compile Ankou
Note: If getting Ankou from another location, this needs to be done manually:
mkdir -p $GOPATH/src/github.com/SoftSec-KAIST
cd $GOPATH/src/github.com/SoftSec-KAIST
git clone REPO  # By default REPO is https://github.com/SoftSec-KAIST/Ankou
cd Ankou
go get .    # Get dependencies
go build .  # Compile

Usage

Now we are ready to fuzz. We first to compile any target we want with afl-gcc or afl-clang. Let's take the classical starting example for fuzzing, binutils:

wget https://mirror.ibcp.fr/pub/gnu/binutils/binutils-2.33.1.tar.xz
tar xf binutils-2.33.1.tar.xz
cd binutils-2.33.1
CC=afl-gcc CXX=afl-g++ ./configure --prefix=`pwd`/install
make -j
make install

Now we are ready to run Ankou:

cd install/bin
mkdir seeds; cp elfedit seeds/ # Put anything in the seeds folder.
go run github.com/SoftSec-KAIST/Ankou -app ./readelf -args "-a @@" -i seeds -o out
# Or use the binary we compiled above:
/path/to/Ankou -app ./readelf -args "-a @@" -i seeds -o out

Evaluation Reproduction

Once Ankou is installed, in order to reproduce the Ankou evaluation:

  1. Compile the 24 packages mentioned in the paper at the same version or commit using afl-gcc. All the packages' source can be found with the same version used in Ankou evaluation at https://github.com/SoftSec-KAIST/Ankou-Benchmark. Additionnally, this repository includes the seeds used to initialize the evalution fuzzing campaigns.
  2. Run the produced subjects with the commands found in benchmark/configuration.json. benchmark/rq1_rq3.json only contains the 24 subjets used for Research Question 1 and 3 of the paper.
  3. Analyze Ankou output directory for results. Crashes are listed in $OUTPUT_DIR/crashes-* and found seeds in $OUTPUT_DIR/seeds-*. Statistics of the fuzzing campaign can be found in the $OUTPUT_DIR/status* directory CSV files. The edge_n value of receiver.csv represents the branch coverage. And the execN column of seed_manager.csv represents the total number of test cases executed so far. Divide it by the time column to obtain the throughout.

There are too many programs in our benchmark, so we will use only one package in this example: cflow.

  1. Compilation.
git clone https://github.com/SoftSec-KAIST/Ankou-Benchmark
cd Ankou-Benchmark
tar xf seeds.tar.xz
cd sources
tar xf cflow-1.6.tar.xz
cd cflow-1.6
CC=afl-gcc CXX=afl-g++ ./configure --prefix=`pwd`/build
make -j
make install
cd ../../..
  1. Preparation of the fuzzing campaign.
mkdir fuzzrun
cp Ankou-Benchmark/sources/cflow-1.6/build/bin/cflow fuzzrun
cp -r Ankou-Benchmark/seeds/cflow fuzzrun/seeds
  1. Run the campaign. The above starts a 24 hours fuzzing campaign. The '-dur' option can be adjusted, or Ankou interrupted earlier. In this version of cflow, and initialized with these seeds, a crash should be found in less than an hour.
cd fuzzrun
go run github.com/SoftSec-KAIST/Ankou -app cflow -args "-o /dev/null @@" \
    -i seeds -threads 1 -o cflow_out -dur 24h
  1. Results analysis
cd cflow_out/status_*
# Print the final branch coverage:
python -c "print(open('receiver.csv').readlines()[-1].split(',')[0])"
# Print the overall throughput:
python -c "last = open('seed_manager.csv').readlines()[-1].split(','); print(float(last[5])/int(last[6]))"
# Print effectiveness of the dynamic PCA (see RQ2):
python -c "last = open('receiver.csv').readlines()[-1].split(','); print('{}%'.format(100-100*float(last[2])/float(last[1])))"

Safe Stack Hash Triaging

Once the environment is setup, the scripts works in two steps:

  1. Run the binary on the crashing input to produce a core file. Using ulimit -c unlimited ensures the core to be dumped.
  2. Use the scripts in the triage folder of this repository:
cd $GOPATH/src/github.com/SoftSec-KAIST/Ankou/triage
gdb -x triage.py -x triage.gdb -batch -c /path/to/core /path/to/binary
cat hash.txt # The stack hashes are found in this text file.
Owner
SoftSec Lab
SoftSec Lab @ KAIST
SoftSec Lab
Multi-Objective Reinforced Active Learning

Multi-Objective Reinforced Active Learning Dependencies wandb tqdm pytorch = 1.7.0 numpy = 1.20.0 scipy = 1.1.0 pycolab == 1.2 Weights and Biases O

Markus Peschl 6 Nov 19, 2022
Detectron2 is FAIR's next-generation platform for object detection and segmentation.

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up r

Facebook Research 23.3k Jan 08, 2023
A best practice for tensorflow project template architecture.

A best practice for tensorflow project template architecture.

Mahmoud Gamal Salem 3.6k Dec 22, 2022
User-friendly bulk RNAseq deconvolution using simulated annealing

Welcome to cellanneal - The user-friendly application for deconvolving omics data sets. cellanneal is an application for deconvolving biological mixtu

11 Dec 16, 2022
A Comparative Review of Recent Kinect-Based Action Recognition Algorithms (TIP2020, Matlab codes)

A Comparative Review of Recent Kinect-Based Action Recognition Algorithms This repo contains: the HDG implementation (Matlab codes) for 'Analysis and

Lei Wang 5 Oct 22, 2022
Python codes for Lite Audio-Visual Speech Enhancement.

Lite Audio-Visual Speech Enhancement (Interspeech 2020) Introduction This is the PyTorch implementation of Lite Audio-Visual Speech Enhancement (LAVSE

Shang-Yi Chuang 85 Dec 01, 2022
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
Recurrent Conditional Query Learning

Recurrent Conditional Query Learning (RCQL) This repository contains the Pytorch implementation of One Model Packs Thousands of Items with Recurrent C

Dongda 4 Nov 28, 2022
Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric

PyEMD: Fast EMD for Python PyEMD is a Python wrapper for Ofir Pele and Michael Werman's implementation of the Earth Mover's Distance that allows it to

William Mayner 433 Dec 31, 2022
[NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning

SoCo [NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning By Fangyun Wei*, Yue Gao*, Zhirong Wu, Han Hu,

Yue Gao 139 Dec 14, 2022
Node for thenewboston digital currency network.

Project setup For project setup see INSTALL.rst Community Join the community to stay updated on the most recent developments, project roadmaps, and ra

thenewboston 27 Jul 08, 2022
SeMask: Semantically Masked Transformers for Semantic Segmentation.

SeMask: Semantically Masked Transformers Jitesh Jain, Anukriti Singh, Nikita Orlov, Zilong Huang, Jiachen Li, Steven Walton, Humphrey Shi This repo co

Picsart AI Research (PAIR) 186 Dec 30, 2022
StyleMapGAN - Official PyTorch Implementation

StyleMapGAN - Official PyTorch Implementation StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing Hyunsu Kim, Yunj

NAVER AI 425 Dec 23, 2022
GraphGT: Machine Learning Datasets for Graph Generation and Transformation

GraphGT: Machine Learning Datasets for Graph Generation and Transformation Dataset Website | Paper Installation Using pip To install the core environm

y6q9 50 Aug 18, 2022
Code for paper Novel View Synthesis via Depth-guided Skip Connections

Novel View Synthesis via Depth-guided Skip Connections Code for paper Novel View Synthesis via Depth-guided Skip Connections @InProceedings{Hou_2021_W

8 Mar 14, 2022
(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters

NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min

Active Vision Laboratory 411 Dec 26, 2022
Companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsura et al.

META-RS This is the companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsu

Bosch Research 7 Dec 09, 2022
Python implementation of "Single Image Haze Removal Using Dark Channel Prior"

##Dependencies pillow(~2.6.0) Numpy(~1.9.0) If the scripts throw AttributeError: __float__, make sure your pillow has jpeg support e.g. try: $ sudo ap

Joyee Cheung 73 Dec 20, 2022
Advanced Signal Processing Notebooks and Tutorials

Advanced Digital Signal Processing Notebooks and Tutorials Prof. Dr. -Ing. Gerald Schuller Jupyter Notebooks and Videos: Renato Profeta Applied Media

Guitars.AI 115 Dec 13, 2022
Shuffle Attention for MobileNetV3

SA-MobileNetV3 Shuffle Attention for MobileNetV3 Train Run the following command for train model on your own dataset: python train.py --dataset mnist

Sajjad Aemmi 36 Dec 28, 2022