Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Related tags

Deep Learninghydra
Overview

Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Paper

Overview

Hydra is a state-of-the-art fuzzing framework for file systems. It provides building blocks for file system fuzzing, including multi-dimensional input mutators, feedback engines, a libOS-based executor, and a bug reproducer with test case minimizer. Developers only need to focus on writing (or bringing in) a checker which defines the core logic for finding the types of bugs of their own interests. Along with the framework, this repository includes our in-house developed crash consistency checker (SymC3), with which 11 new crash consistency bugs were revealed from ext4, Btrfs, F2FS, and from two verified file systems: FSCQ and Yxv6.

Contents

  • General code base

    • src/combined: Hydra input mutator
    • src/lkl/tools/lkl/{FS}-combined-consistency: Hydra LibOS-based Executor (will be downloaded and compiled during setup)
  • Checkers

    • src/emulator: Hydra's in-house crash consistency checker, SymC3

Setup

1. All setup should be done under src

$ cd src

2. Install dependencies

./dep.sh

3. Compile for each file system

$ make build-btrfs-imgwrp
  • We can do the same for other file systems:
$ make build-ext4-imgwrp
$ make build-f2fs-imgwrp
$ make build-xfs-imgwrp
  • (Skip if you want to test the latest kernel) To reproduce bugs presented in the SOSP'19 paper, do the following to back-port LKL to kernel 4.16.
$ cd lkl (pwd: proj_root/src/lkl) # assuming that you are in the src directory
$ make mrproper
$ git pull
$ git checkout v4.16-backport
$ ./compile -t btrfs
$ cd .. (pwd: proj_root/src)

4. Set up environments

$ sudo ./prepare_fuzzing.sh
$ ./prepare_env.sh

5. Run fuzzing (single / multiple instance)

  • Single instance
$ ./run.py -t [fstype] -c [cpu_id] -l [tmpfs_id] -g [fuzz_group]

-t: choose from btrfs, f2fs, ext4, xfs
-c: cpu id to run this fuzzer instance
-l: tmpfs id to store logs (choose one from /tmp/mosbench/tmpfs-separate/)
-g: specify group id for parallel fuzzing, default: 0

e.g., ./run.py -t btrfs -c 4 -l 10 -g 1
Runs btrfs fuzzer, and pins the instance to Core #4.
Logs will be accumulated under /tmp/mosbench/tmpfs-separate/10/log/ .
  • You can also run multiple fuzzers in parallel by doing:
[Terminal 1] ./run.py -t btrfs -c 1 -l 10 -g 1
[Terminal 2] ./run.py -t btrfs -c 2 -l 10 -g 1
[Terminal 3] ./run.py -t btrfs -c 3 -l 10 -g 1
[Terminal 4] ./run.py -t btrfs -c 4 -l 10 -g 1
// all btrfs bug logs will be under /tmp/mosbench/tmpfs-separate/10/log/

[Terminal 5] ./run.py -t f2fs -c 5 -l 11 -g 2
[Terminal 6] ./run.py -t f2fs -c 6 -l 11 -g 2
[Terminal 7] ./run.py -t f2fs -c 7 -l 11 -g 2
[Terminal 8] ./run.py -t f2fs -c 8 -l 11 -g 2
// all f2fs bug logs will be under /tmp/mosbench/tmpfs-separate/11/log/

6. Important note

It is highly encouraged that you use separate input, output, log directories for each file system, unless you are running fuzzers in parallel. If you reuse the same directories from previous testings of other file systems, it won't work properly.

7. Experiments

Please refer to EXPERIMENTS.md for detailed experiment information.

Contacts

Owner
gts3.org ([email protected])
https://gts3.org
gts3.org (<a href=[email protected])">
Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

Learning Canonical Representations for Scene Graph to Image Generation (ECCV 2020) Roei Herzig*, Amir Bar*, Huijuan Xu, Gal Chechik, Trevor Darrell, A

roei_herzig 24 Jul 07, 2022
Utility tools for the "Divide and Remaster" dataset, introduced as part of the Cocktail Fork problem paper

Divide and Remaster Utility Tools Utility tools for the "Divide and Remaster" dataset, introduced as part of the Cocktail Fork problem paper The DnR d

Darius Petermann 46 Dec 11, 2022
Fully Convolutional DenseNets for semantic segmentation.

Introduction This repo contains the code to train and evaluate FC-DenseNets as described in The One Hundred Layers Tiramisu: Fully Convolutional Dense

485 Nov 26, 2022
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition (NeurIPS 2019)

MLCR This is the source code for paper Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. Xuesong Niu, Hu Han, Shiguang

Edson-Niu 60 Nov 29, 2022
Simultaneous NMT/MMT framework in PyTorch

This repository includes the codes, the experiment configurations and the scripts to prepare/download data for the Simultaneous Machine Translation wi

<a href=[email protected]"> 37 Sep 29, 2022
Code for "CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds" @ICRA2021

CloudAAE This is an tensorflow implementation of "CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds" Files log:

Gee 35 Nov 14, 2022
Level Based Customer Segmentation

level_based_customer_segmentation Level Based Customer Segmentation Persona Veri Seti kullanılarak müşteri segmentasyonu yapılmıştır. KOLONLAR : PRICE

Buse Yıldırım 6 Dec 21, 2021
Progressive Coordinate Transforms for Monocular 3D Object Detection

Progressive Coordinate Transforms for Monocular 3D Object Detection This repository is the official implementation of PCT. Introduction In this paper,

58 Nov 06, 2022
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

DeepMLS: Deep Implicit Moving Least-Squares Functions for 3D Reconstruction This repository contains the implementation of the paper: Deep Implicit Mo

103 Dec 22, 2022
Official implementation of "A Unified Objective for Novel Class Discovery", ICCV2021 (Oral)

A Unified Objective for Novel Class Discovery This is the official repository for the paper: A Unified Objective for Novel Class Discovery Enrico Fini

Enrico Fini 118 Dec 26, 2022
Complete* list of autonomous driving related datasets

AD Datasets Complete* and curated list of autonomous driving related datasets Contributing Contributions are very welcome! To add or update a dataset:

Daniel Bogdoll 13 Dec 19, 2022
Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Chris Donahue 98 Dec 14, 2022
Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on [ Paper ] [ Project Page ] This repository contains the code fo

Andrew Jong 97 Dec 13, 2022
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

pytorch-fcn PyTorch implementation of Fully Convolutional Networks. Requirements pytorch = 0.2.0 torchvision = 0.1.8 fcn = 6.1.5 Pillow scipy tqdm

Kentaro Wada 1.6k Jan 07, 2023
A package related to building quasi-fibration symmetries

qf A package related to building quasi-fibration symmetries. If you'd like to learn more about how it works, see the brief explanation and References

Paolo Boldi 1 Dec 01, 2021
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

167 Jan 02, 2023
UMich 500-Level Mobile Robotics Course

MOBILE ROBOTICS: METHODS & ALGORITHMS - WINTER 2022 University of Michigan - NA 568/EECS 568/ROB 530 For slides, lecture notes, and example codes, see

393 Dec 29, 2022
Code for one-stage adaptive set-based HOI detector AS-Net.

AS-Net Code for one-stage adaptive set-based HOI detector AS-Net. Mingfei Chen*, Yue Liao*, Si Liu, Zhiyuan Chen, Fei Wang, Chen Qian. "Reformulating

Mingfei Chen 45 Dec 09, 2022
Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation of the NeurIPS 2021 paper Alias-Free Generative Adversarial Net

Diego Porres 185 Dec 24, 2022
Dist2Dec: A Simplicial Neural Network for Homology Localization

Dist2Dec: A Simplicial Neural Network for Homology Localization

Alexandros Keros 6 Jun 12, 2022