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  • Rank 242,095 (Top 5 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created over 1 year ago
  • Updated 3 months ago

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Repository Details

PASTIS: Collaborative Fuzzing Framework

The PASTIS project is a fuzzing framework aiming at combining various software testing techniques within the same workflow to perform collaborative fuzzing also called ensemble fuzzing. At the moment it supports the following fuzzing engines:

  • Honggfuzz (greybox fuzzer)
  • AFL++ (greybox fuzzer)
  • TritonDSE (whitebox fuzzer)

[Documentation]


Overview

Note
The video highlight the use-case driven by SAST alerts. However, the main use-case the standard fuzzing for coverage or bug research.


Quick start

Installation

The PASTIS framework can be installed with:

pip install pastis-framework

The pip package will install all dependencies and the tritondse engine.

AFL++

To install AFL++ please refer to the official documentation.

Honggfuzz

Honggfuzz requires a specific build with a patch applied it can be compiled by applying the following steps:

sudo apt-get install binutils-dev libunwind-dev libblocksruntime-dev clang
git clone https://github.com/quarkslab/pastis.git
cd pastis/engines/pastis-honggfuzz/patches
./make_hf.sh
echo "export HFUZZ_PATH=$PWD/honggfuzz-5a504b49" >> ~/.profile

Usage

The main component is the broker that will serve the appropriate configurations to fuzzing engines and that will aggregate results. An example is the following:

tar xvf doc/figs/fsm-demo.tar.gz && cd fsm-demo
make
pastis-broker -b bin -s initial -w output

It will run the broker using binaries in the bin directory. Initial corpus is initial and the whole output workspace will be save in output. By default it will listen on the local interface on port 5555.

Then fuzzing engines can be launched to start testing the software.

pastis-aflpp online

Or:

pastis-triton online

Full documentation is available: here

Adding a Fuzzer

Integrating a fuzzer requires writing a Python driver using the libpasts library installed by the package. It requires implementing some callbacks to receive the initial configuration and also to receive inputs from the broker. Conversely the API enables sending newly generated inputs to the broker.

The process is further detailed in the documentation.

Note
We warmly welcome any Pull Request to add the support for a new fuzzing engine.


Docker

You can also run PASTIS using Docker:

docker pull ubuntu:22.04
docker build -t pastis-docker .
docker run -v <HOST-WORKSPACE>:/workspace --cap-add=SYS_PTRACE --user $(id -u $USER):$(id -g $USER) -it pastis-docker

To open another terminal to an already running container:

docker exec -it $(docker ps | grep 'pastis-docker' | awk '{print $1}') /bin/bash

The PASTIS Docker image has already installed all the needed dependencies such as AFL++ and Honggfuzz.


Papers and conference

  • Symbolic Execution the Swiss-Knife of the Reverse Engineer Toolbox
    Venue: KLEE Workshop, 2022 [๐Ÿ“š] [๐ŸŽฅ]
    Authors: Robin David, Richard Abou Chaaya, Christian Heitman

  • From source code to crash test-case through software testing automation
    Venue: European Cyber Week, C&ESAR Workshop, 2021 paper slides
    Authors: Robin David, Jonathan Salwan, Justin Bourroux

Cite Pastis

soon

Contributors

Pastis is powered by Quarkslab and initially financed by DGA-MI.

All contributions

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