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])">
Analyzing basic network responses to novel classes

novelty-detection Analyzing how AlexNet responds to novel classes with varying degrees of similarity to pretrained classes from ImageNet. If you find

Noam Eshed 34 Oct 02, 2022
HyperCube: Implicit Field Representations of Voxelized 3D Models

HyperCube: Implicit Field Representations of Voxelized 3D Models Authors: Magdalena Proszewska, Marcin Mazur, Tomasz Trzcinski, Przemysław Spurek [Pap

Magdalena Proszewska 3 Mar 09, 2022
TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

TransZero++ This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted

Shiming Chen 6 Aug 16, 2022
Deep Semisupervised Multiview Learning With Increasing Views (IEEE TCYB 2021, PyTorch Code)

Deep Semisupervised Multiview Learning With Increasing Views (ISVN, IEEE TCYB) Peng Hu, Xi Peng, Hongyuan Zhu, Liangli Zhen, Jie Lin, Huaibai Yan, Dez

3 Nov 19, 2022
This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras)

Yogi-Optimizer_Keras This is an implementation of Googles Yogi-Optimizer in Keras (tf.keras) The NeurIPS-Paper can be found here: http://papers.nips.c

14 Sep 13, 2022
Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX. The repository combines a class agnostic object localizer to first detect the objects in the image

Ibai Gorordo 24 Nov 14, 2022
Automatic self-diagnosis program (python required)Automatic self-diagnosis program (python required)

auto-self-checker 자동으로 자가진단 해주는 프로그램(python 필요) 중요 이 프로그램이 실행될때에는 절대로 마우스포인터를 움직이거나 키보드를 건드리면 안된다(화면인식, 마우스포인터로 직접 클릭) 사용법 프로그램을 구동할 폴더 내의 cmd창에서 pip

1 Dec 30, 2021
SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP

scdlpicker SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP Objective This is a simple deep learning (DL) repicker module

Joachim Saul 6 May 13, 2022
Adaptive Graph Convolution for Point Cloud Analysis

Adaptive Graph Convolution for Point Cloud Analysis This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph

64 Dec 21, 2022
ML models and internal tensors 3D visualizer

The free Zetane Viewer is a tool to help understand and accelerate discovery in machine learning and artificial neural networks. It can be used to ope

Zetane Systems 787 Dec 30, 2022
Pytorch tutorials for Neural Style transfert

PyTorch Tutorials This tutorial is no longer maintained. Please use the official version: https://pytorch.org/tutorials/advanced/neural_style_tutorial

Alexis David Jacq 135 Jun 26, 2022
Omnidirectional camera calibration in python

Omnidirectional Camera Calibration Key features pure python initial solution based on A Toolbox for Easily Calibrating Omnidirectional Cameras (Davide

Thomas Pönitz 12 Nov 22, 2022
This repository contains the reference implementation for our proposed Convolutional CRFs.

ConvCRF This repository contains the reference implementation for our proposed Convolutional CRFs in PyTorch (Tensorflow planned). The two main entry-

Marvin Teichmann 553 Dec 07, 2022
A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.

Built with ❤️ by Sam Showalter Contents Overview Installation Dependencies Usage Scripts Standard Execution Environment Development Environment Benchm

SRI-AIC 1 Nov 18, 2021
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
Hypercomplex Neural Networks with PyTorch

HyperNets Hypercomplex Neural Networks with PyTorch: this repository would be a container for hypercomplex neural network modules to facilitate resear

Eleonora Grassucci 21 Dec 27, 2022
A Human-in-the-Loop workflow for creating HD images from text

A Human-in-the-Loop? workflow for creating HD images from text DALL·E Flow is an interactive workflow for generating high-definition images from text

Jina AI 2.5k Jan 02, 2023
Research code of ICCV 2021 paper "Mesh Graphormer"

MeshGraphormer ✨ ✨ This is our research code of Mesh Graphormer. Mesh Graphormer is a new transformer-based method for human pose and mesh reconsructi

Microsoft 251 Jan 08, 2023
Implementation of Bottleneck Transformer in Pytorch

Bottleneck Transformer - Pytorch Implementation of Bottleneck Transformer, SotA visual recognition model with convolution + attention that outperforms

Phil Wang 621 Jan 06, 2023
Apache Spark - A unified analytics engine for large-scale data processing

Apache Spark Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an op

The Apache Software Foundation 34.7k Jan 04, 2023