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
End-To-End Crowdsourcing

End-To-End Crowdsourcing Comparison of traditional crowdsourcing approaches to a state-of-the-art end-to-end crowdsourcing approach LTNet on sentiment

Andreas Koch 1 Mar 06, 2022
Code for Fold2Seq paper from ICML 2021

[ICML2021] Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design Environment file: environment.yml Data and Feat

International Business Machines 43 Dec 04, 2022
This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022
Code repo for "FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation" (ICCV 2021)

FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation (ICCV 2021) This repository contains the implementation of th

Yuhang Zang 21 Dec 17, 2022
Modular Gaussian Processes

Modular Gaussian Processes for Transfer Learning 🧩 Introduction This repository contains the implementation of our paper Modular Gaussian Processes f

Pablo Moreno-Muñoz 10 Mar 15, 2022
Music source separation is a task to separate audio recordings into individual sources

Music Source Separation Music source separation is a task to separate audio recordings into individual sources. This repository is an PyTorch implmeme

Bytedance Inc. 958 Jan 03, 2023
Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

This is the codebase for the paper: Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Directory Structur

Peter Hase 19 Aug 21, 2022
The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

SD-AANet The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv] Overview confi

cv516Buaa 9 Nov 07, 2022
Code for "Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance" at NeurIPS 2021

Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance Justin Lim, Christina X Ji, Michael Oberst, Saul Blecker, Leor

Sontag Lab 3 Feb 03, 2022
Code for the CVPR 2021 paper: Understanding Failures of Deep Networks via Robust Feature Extraction

Welcome to Barlow Barlow is a tool for identifying the failure modes for a given neural network. To achieve this, Barlow first creates a group of imag

Sahil Singla 33 Dec 05, 2022
A simple but complete full-attention transformer with a set of promising experimental features from various papers

x-transformers A concise but fully-featured transformer, complete with a set of promising experimental features from various papers. Install $ pip ins

Phil Wang 2.3k Jan 03, 2023
This repository is the official implementation of Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (NeurIPS21).

Core-tuning This repository is the official implementation of ``Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regular

vanint 18 Dec 17, 2022
Some pre-commit hooks for OpenMMLab projects

pre-commit-hooks Some pre-commit hooks for OpenMMLab projects. Using pre-commit-hooks with pre-commit Add this to your .pre-commit-config.yaml - rep

OpenMMLab 16 Nov 29, 2022
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

vid2vid Project | YouTube(short) | YouTube(full) | arXiv | Paper(full) Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic vid

NVIDIA Corporation 8.1k Jan 01, 2023
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images (ISBI 2021, MELBA 2021)

MultiMix This repository contains the implementation of MultiMix. Our publications for this project are listed below: "MultiMix: Sparingly Supervised,

Ayaan Haque 27 Dec 22, 2022
SW components and demos for visual kinship recognition. An emphasis is put on the FIW dataset-- data loaders, benchmarks, results in summary.

FIW Data Development Kit Table of Contents Introduction Families In the Wild Database Publications Organization To Do License Getting Involved Introdu

Joseph P. Robinson 12 Jun 04, 2022
DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021]

DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021] Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng

Automation and Intelligence for Civil Engineering (AI4CE) Lab @ NYU 98 Dec 21, 2022
Code for Learning Manifold Patch-Based Representations of Man-Made Shapes, in ICLR 2021.

LearningPatches | Webpage | Paper | Video Learning Manifold Patch-Based Representations of Man-Made Shapes Dmitriy Smirnov, Mikhail Bessmeltsev, Justi

Dima Smirnov 22 Nov 14, 2022
code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation (CVPR 2021) Introduction PBR is a conceptually simple yet effective

H.Chen 143 Jan 05, 2023
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the

SNU ADSL 0 Feb 07, 2022