Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

Overview

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS and Windows.

Published at USENIX Security 2017.

Currently missing:

  • full documentation
  • agents for macOS and Windows (except for our test driver)

BibTex:

@inproceedings{schumilo2017kafl,
    author = {Schumilo, Sergej and Aschermann, Cornelius and Gawlik, Robert and Schinzel, Sebastian and Holz, Thorsten},
    title = {{kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels}},
    year = {2017},
    booktitle = {USENIX Security Symposium} 
}

Trophies

Setup

This is a short introduction on how to setup kAFL to fuzz Linux kernel components.

Download kAFL and install necessary components

$ git clone https://github.com/RUB-SysSec/kAFL.git
$ cd kAFL
$ chmod u+x install.sh
$ sudo ./install.sh
$ sudo reboot

Setup VM

  • Create QEMU hard drive image:
$ qemu-img create -f qcow2 linux.qcow2 20G
  • Retrieve an ISO file of the desired OS and install it inside a VM (in this case Ubuntu 16.04 server):
$ wget -O /path/to/where/to/store/ubuntu.iso http://de.releases.ubuntu.com/16.04/ubuntu-16.04.3-server-amd64.iso
$ qemu-system-x86_64 -cpu host -enable-kvm -m 512 -hda linux.qcow2 -cdrom ubuntu.iso -usbdevice tablet
  • Download kAFL and compile the loader agent:
git clone https://github.com/RUB-SysSec/kAFL.git
cd path/to/kAFL/kAFL-Fuzzer/agents
chmod u+x compile.sh
./compile.sh
  • Shutdown the VM

Prepare VM for kAFL fuzzing

  • On the host: Create Overlay and Snapshot Files:
mkdir snapshot && cd snapshot
qemu-img create -b /absolute/path/to/hdd/linux.qcow2 -f qcow2 overlay_0.qcow2
qemu-img create -f qcow2 ram.qcow2 512
  • Start the VM using QEMU-PT:
cd /path/to/kAFL
./qemu-2.9.0/x86_64-softmmu/qemu-system-x86_64 -hdb /path/to/snapshot/ram.qcow2 -hda /path/to/snapshot/overlay_0.qcow2 -machine pc-i440fx-2.6 -serial mon:stdio -enable-kvm -k de -m 512
  • (Optional) Install and load the vulnerable Test Driver:
cd path/to/kAFl/kAFL-Fuzzer/vuln_drivers/simple/linux_x86-64/
chmod u+x load.sh
sudo ./load.sh
  • Execute loader binary which is in path/to/kAFL/kAFL-Fuzzer/agents/linux_x86_64/loader/ as root. VM should freeze. Switch to the QEMU management console and create a snapshot:
# press CTRL-a + c
savevm kafl
q 

Compile and configure kAFL components

  • Edit /path/to/kAFL/kAFL-Fuzzer/kafl.ini (qemu-kafl_location to point to path/to/kAFL/qemu-2.9.0/x86_64-softmmu/qemu-system-x86_64)

  • Compile agents:

cd <KERNEL_AFL_ROOT>/kAFL-Fuzzer/agents
chmod u+x compile.sh
./compile.sh
  • Retrieve address ranges of loaded drivers:
cd /path/to/kAFL/kAFL-Fuzzer
python kafl_info.py /path/to/snapshot/ram.qcow2 /path/to/snapshot/ agents/linux_x86_64/info/info 512 -v

Start Fuzzing!

python kafl_fuzz.py /path/to/snapshot/ram.qcow2 /path/to/snapshot agents/linux_x86_64/fuzzer/kafl_vuln_test 512 /path/to/input/directory /path/to/working/directory -ip0 0xffffffffc0287000-0xffffffffc028b000 -v --Purge

The value ip0 is the address range of the fuzzing target.

Owner
Chair for Sys­tems Se­cu­ri­ty
Chair for Sys­tems Se­cu­ri­ty
Simply enable or disable your Nvidia dGPU

EnvyControl (WIP) Simply enable or disable your Nvidia dGPU Usage First clone this repo and install envycontrol with sudo pip install . CLI Turn off y

Victor Bayas 292 Jan 03, 2023
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

LSTMs for Human Activity Recognition Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amon

Guillaume Chevalier 3.1k Dec 30, 2022
Code for project: "Learning to Minimize Remainder in Supervised Learning".

Learning to Minimize Remainder in Supervised Learning Code for project: "Learning to Minimize Remainder in Supervised Learning". Requirements and Envi

Yan Luo 0 Jul 18, 2021
Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Contra-OOD Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers. Requirements PyTorch Transformers datasets

Wenxuan Zhou 27 Oct 28, 2022
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition - NeurIPS2021

Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition Project Page | Video | Paper Implementation for Neural-PIL. A novel method wh

Computergraphics (University of Tübingen) 64 Dec 29, 2022
Arxiv harvester - Poor man's simple harvester for arXiv resources

Poor man's simple harvester for arXiv resources This modest Python script takes

Patrice Lopez 5 Oct 18, 2022
Method for facial emotion recognition compitition of Xunfei and Datawhale .

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档 队名:W03KFgNOc 排名:3 正确率: 0.75564 队员:yyMoming,xkwang,RichardoMu。 比赛链接:人脸情绪识别挑战赛 文章地址:link emotion 该项目分别训练八个模型并生成csv文

6 Oct 17, 2022
Just-Now - This Is Just Now Login Friendlist Cloner Tools

JUST NOW LOGIN FRIENDLIST CLONER TOOLS Install $ apt update $ apt upgrade $ apt

MAHADI HASAN AFRIDI 21 Mar 09, 2022
An LSTM based GAN for Human motion synthesis

GAN-motion-Prediction An LSTM based GAN for motion synthesis has a few issues reading H3.6M data from A.Jain et al , will fix soon. Prediction of the

Amogh Adishesha 9 Jun 17, 2022
Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

Discretization Robust Correspondence Benchmark One challenge of machine learning on 3D surfaces is that there are many different representations/sampl

Nicholas Sharp 10 Sep 30, 2022
CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation

CSKG: The CommonSense Knowledge Graph CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation: AT

USC ISI I2 85 Dec 12, 2022
Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

CoMatch: Semi-supervised Learning with Contrastive Graph Regularization (Salesforce Research) This is a PyTorch implementation of the CoMatch paper [B

Salesforce 107 Dec 14, 2022
Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN).

arXiv, porject page, paper Blind Image Decomposition (BID) Blind Image Decomposition is a novel task. The task requires separating a superimposed imag

64 Dec 20, 2022
To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beginners, intermediates as well as experts

JaxTon 💯 JAX exercises Mission 🚀 To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beg

Rohan Rao 512 Jan 01, 2023
Nerf pl - NeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning

nerf_pl Update: an improved NSFF implementation to handle dynamic scene is open! Update: NeRF-W (NeRF in the Wild) implementation is added to nerfw br

AI葵 1.8k Dec 30, 2022
PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"

DRNet for Video Indvidual Counting (CVPR 2022) Introduction This is the official PyTorch implementation of paper: DR.VIC: Decomposition and Reasoning

tao han 35 Nov 22, 2022
BOVText: A Large-Scale, Multidimensional Multilingual Dataset for Video Text Spotting

BOVText: A Large-Scale, Bilingual Open World Dataset for Video Text Spotting Updated on December 10, 2021 (Release all dataset(2021 videos)) Updated o

weijiawu 47 Dec 26, 2022
Code for our paper "MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction" published at ICCV 2021.

MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction This repository contains the code for the p

Sven 30 Jan 05, 2023
A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

DeepKE is a knowledge extraction toolkit supporting low-resource and document-level scenarios for entity, relation and attribute extraction. We provide comprehensive documents, Google Colab tutorials

ZJUNLP 1.6k Jan 05, 2023
Arquitetura e Desenho de Software.

S203 Este é um repositório dedicado às aulas de Arquitetura e Desenho de Software, cuja sigla é "S203". E agora, José? Como não tenho muito a falar aq

Fabio 7 Oct 23, 2021