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])">
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Dec 31, 2022
Tiny Object Detection in Aerial Images.

AI-TOD AI-TOD is a dataset for tiny object detection in aerial images. [Paper] [Dataset] Description AI-TOD comes with 700,621 object instances for ei

jwwangchn 116 Dec 30, 2022
Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"

BEGAN in Tensorflow Tensorflow implementation of BEGAN: Boundary Equilibrium Generative Adversarial Networks. Requirements Python 2.7 or 3.x Pillow tq

Taehoon Kim 922 Dec 21, 2022
Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays

Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays In this repo, you will find the instructions on how to requ

Intelligent Vision Research Lab 4 Jul 21, 2022
Delving into Localization Errors for Monocular 3D Object Detection, CVPR'2021

Delving into Localization Errors for Monocular 3D Detection By Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang. Intr

XINZHU.MA 124 Jan 04, 2023
[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

TransFuser This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our

695 Jan 05, 2023
Code for the paper "Multi-task problems are not multi-objective"

Multi-Task problems are not multi-objective This is the code for the paper "Multi-Task problems are not multi-objective" in which we show that the com

Michael Ruchte 5 Aug 19, 2022
Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Aviv Gabbay 41 Nov 29, 2022
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 09, 2022
Code for "Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency" paper

UNICORN 🦄 Webpage | Paper | BibTex PyTorch implementation of "Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency" pap

118 Jan 06, 2023
Turning SymPy expressions into PyTorch modules.

sympytorch A micro-library as a convenience for turning SymPy expressions into PyTorch Modules. All SymPy floats become trainable parameters. All SymP

Patrick Kidger 89 Dec 13, 2022
Implementation of the paper ''Implicit Feature Refinement for Instance Segmentation''.

Implicit Feature Refinement for Instance Segmentation This repository is an official implementation of the ACM Multimedia 2021 paper Implicit Feature

Lufan Ma 17 Dec 28, 2022
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

Hybrid solving process for combinatorial optimization problems Combinatorial optimization has found applications in numerous fields, from aerospace to

117 Dec 13, 2022
RNN Predict Street Commercial Vitality

RNN-for-Predicting-Street-Vitality Code and dataset for Predicting the Vitality of Stores along the Street based on Business Type Sequence via Recurre

Zidong LIU 1 Dec 15, 2021
PAIRED in PyTorch 🔥

PAIRED This codebase provides a PyTorch implementation of Protagonist Antagonist Induced Regret Environment Design (PAIRED), which was first introduce

UCL DARK Lab 46 Dec 12, 2022
Code for ICCV 2021 paper Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes using Scene Graphs

Graph-to-3D This is the official implementation of the paper Graph-to-3d: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs | arx

Helisa Dhamo 33 Jan 06, 2023
List of content farm sites like g.penzai.com.

内容农场网站清单 Google 中文搜索结果包含了相当一部分的内容农场式条目,比如「小 X 知识网」「小 X 百科网」。此种链接常会 302 重定向其主站,页面内容为自动生成,大量堆叠关键字,揉杂一些爬取到的内容,完全不具可读性和参考价值。 尤为过分的是,该类网站可能有成千上万个分身域名被 Goog

WDMPA 541 Jan 03, 2023
An open source object detection toolbox based on PyTorch

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

Bo Chen 24 Dec 28, 2022
A treasure chest for visual recognition powered by PaddlePaddle

简体中文 | English PaddleClas 简介 飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集,助力使用者训练出更好的视觉模型和应用落地。 近期更新 2021.11.1 发布PP-ShiTu技术报告,新增饮料识别demo 2021.10.23 发

4.6k Dec 31, 2022
A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Overview Code and supplemental materials for Karduni et al., 2020 IEEE Vis. "A Bayesian cognition approach for belief updating of correlation judgemen

Ryan Wesslen 1 Feb 08, 2022