Fuzzing tool (TFuzz): a fuzzing tool based on program transformation

Related tags

Deep LearningT-Fuzz
Overview

T-Fuzz

T-Fuzz consists of 2 components:

  • Fuzzing tool (TFuzz): a fuzzing tool based on program transformation
  • Crash Analyzer (CrashAnalyzer): a tool that verifies whether crashes found transformed programs are true bugs in the original program or not (coming soon).

OS support

The current version is tested only on Ubuntu-16.04, while trying to run the code, please use our tested OS.

Prerequisite

T-Fuzz system is built on several opensource tools.

Installing radare2

$ git clone https://github.com/radare/radare2.git
$ cd radare2
$ ./sys/install.sh

Installing python libraries

installing some dependent libraries

Note: to use apt-get build-dep, you need to uncomment the deb-src lines in your apt source file (/etc/apt/sources.list) and run apt-get update.

$ sudo apt-get install build-essential gcc-multilib libtool automake autoconf bison debootstrap debian-archive-keyring
$ sudo apt-get build-dep qemu-system
$ sudo apt-get install libacl1-dev

installing pip and setting up virtualenv & wrapper

$ sudo apt-get install python-pip python-virtualenv
$ pip install virtualenvwrapper

Add the following lines to your shell rc file (~/.bashrc or ~/.zshrc).

export WORKON_HOME=$HOME/.virtual_envs
source /usr/local/bin/virtualenvwrapper.sh

Creating a python virtual environment

$ mkvirtualenv tfuzz-env

Installing dependent libraries

This command will install all the dependent python libraries for you.

$ workon tfuzz-env
$ pip install -r req.txt

Fuzzing target programs with T-Fuzz

$ ./TFuzz  --program  
   
     --work_dir 
    
      --target_opts 
     

     
    
   

Where

  • : the path to the target program to fuzz
  • : the directory to save the results
  • : the options to pass to the target program, like AFL, use @@ as placeholder for files to mutate.

Examples

  1. Fuzzing base64 with T-Fuzz
$ ./TFuzz  --program  target_programs/base64  --work_dir workdir_base64 --target_opts "-d @@"
  1. Fuzzing uniq with T-Fuzz
$ ./TFuzz  --program  target_programs/uniq  --work_dir workdir_uniq --target_opts "@@"
  1. Fuzzing md5sum with T-Fuzz
$ ./TFuzz  --program  target_programs/md5sum  --work_dir workdir_md5sum --target_opts "-c @@"
  1. Fuzzing who with T-Fuzz
$ ./TFuzz  --program  target_programs/who  --work_dir workdir_who --target_opts "@@"

Using CrashAnalyzer to verify crashes

T-Fuzz CrashAnalyzer has been put in a docker image, however, it is still not working in all binaries we tested, we are still investigating it the cause.

Here is how:

Run the following command to run our docker image

$ [sudo] docker pull tfuzz/tfuzz-test
$ [sudo] docker run  --security-opt seccomp:unconfined -it tfuzz/tfuzz-test  /usr/bin/zsh 

In the container:

There are 3 directories:

  • release: contains code the built lava binaries
  • results: contains some results we found in lava-m dataset
  • radare2: it is a program used by T-Fuzz.

Currently, T-Fuzz may not work, because the tracer crashes accidentally. And the CrashAnalyzer can not work on all results. But some cases can be recovered.

For example:

To verify bugs in base64, first goto release and checkout ca_base64:

$ cd release
$ git checkout ca_base64

Then we use a transformed program to recover the crash in the original program:

  1. Choose a transformed program and run it on the input found by a fuzzer:
$ cd ~
$./results/ca_base64/554/base64_tfuzz_28/base64_tfuzz_28 -d ./results/ca_base64/554/crashing_inputs_from/results_saved_0_from 
[1]    131 segmentation fault (core dumped)  ./results/ca_base64/554/base64_tfuzz_28/base64_tfuzz_28 -d
  1. Recover an input from this transformed program and crashing input
). Re-hooking. WARNING | 2018-12-04 04:28:23,228 | angr.project | Address is already hooked, during hook(0x90dd000, ). Re-hooking. WARNING | 2018-12-04 04:28:23,229 | angr.simos.linux | Tracer has been heavily tested only for CGC. If you find it buggy for Linux binaries, we are sorry! Adding = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 == 47))> Adding = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 == 47))> Adding = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 == 47))> Adding = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 == 47))> results saved to /root/base64_result/recover_0 ">
$ ./release/CrashAnalyzer  --tprogram ./results/ca_base64/554/base64_tfuzz_28/base64_tfuzz_28 --target_opts "-d @@" --crash_input ./results/ca_base64/554/crashing_inputs_from/results_saved_0_from --result_dir base64_result --save_to recover
WARNING | 2018-12-04 04:28:22,350 | angr.analyses.disassembly_utils | Your verison of capstone does not support MIPS instruction groups.
Trying /root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from
WARNING | 2018-12-04 04:28:23,228 | angr.project | Address is already hooked, during hook(0x9021cd0, 
        
         ). Re-hooking.
WARNING | 2018-12-04 04:28:23,228 | angr.project | Address is already hooked, during hook(0x90dd000, 
         
          ). Re-hooking.
WARNING | 2018-12-04 04:28:23,229 | angr.simos.linux | Tracer has been heavily tested only for CGC. If you find it buggy for Linux binaries, we are sorry!
Adding 
          
           = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_0_0_8 == 47))>
Adding 
           
            = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_1_1_8 == 47))>
Adding 
            
             = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_2_2_8 == 47))> Adding 
             
              = 65) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 <= 90)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 >= 97) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 <= 122)), ((file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 >= 48) && (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 <= 57)), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 == 43), (file_/root/results/ca_base64/554/crashing_inputs_from/results_saved_0_from_9_3_3_8 == 47))> results saved to /root/base64_result/recover_0 
             
            
           
          
         
        

Then /root/base64_result/recover_0 is generated, we can use it to trigger a crash in the original program.

  1. verify the input by running the generated input on the original program
$ ./results/base64 -d base64_result/recover_0 
Successfully triggered bug 554, crashing now!
Successfully triggered bug 554, crashing now!
Successfully triggered bug 554, crashing now!
[1]    177 segmentation fault (core dumped)  ./results/base64 -d base64_result/recover_0
Owner
HexHive
Enforcing memory safety guarantees and type safety guarantees at the compiler and runtime level
HexHive
Demystifying How Self-Supervised Features Improve Training from Noisy Labels

Demystifying How Self-Supervised Features Improve Training from Noisy Labels This code is a PyTorch implementation of the paper "[Demystifying How Sel

<a href=[email protected]"> 4 Oct 14, 2022
The audio-video synchronization of MKV Container Format is exploited to achieve data hiding

The audio-video synchronization of MKV Container Format is exploited to achieve data hiding, where the hidden data can be utilized for various management purposes, including hyper-linking, annotation

Maxim Zaika 1 Nov 17, 2021
A TensorFlow implementation of Neural Program Synthesis from Diverse Demonstration Videos

ViZDoom http://vizdoom.cs.put.edu.pl ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is pri

Hyeonwoo Noh 1 Aug 19, 2020
RSC-Net: 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos

RSC-Net: 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos Implementation for "3D Human Pose, Shape and Texture from Low-Resoluti

XiangyuXu 42 Nov 10, 2022
Vikrant Deshpande 1 Nov 17, 2022
A FAIR dataset of TCV experimental results for validating edge/divertor turbulence models.

TCV-X21 validation for divertor turbulence simulations Quick links Intro Welcome to TCV-X21. We're glad you've found us! This repository is designed t

0 Dec 18, 2021
Dilated Convolution with Learnable Spacings PyTorch

Dilated-Convolution-with-Learnable-Spacings-PyTorch Ismail Khalfaoui Hassani Dilated Convolution with Learnable Spacings (abbreviated to DCLS) is a no

15 Dec 09, 2022
Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

HeyangXue1997 103 Dec 23, 2022
An automated facial recognition based attendance system (desktop application)

Facial_Recognition_based_Attendance_System An automated facial recognition based attendance system (desktop application) Made using Python, Tkinter an

1 Jun 21, 2022
WORD: Revisiting Organs Segmentation in the Whole Abdominal Region

WORD: Revisiting Organs Segmentation in the Whole Abdominal Region. This repository provides the codebase and dataset for our work WORD: Revisiting Or

Healthcare Intelligence Laboratory 71 Jan 07, 2023
Compare outputs between layers written in Tensorflow and layers written in Pytorch

Compare outputs of Wasserstein GANs between TensorFlow vs Pytorch This is our testing module for the implementation of improved WGAN in Pytorch Prereq

Hung Nguyen 72 Dec 20, 2022
Official implementation of "Robust channel-wise illumination estimation"

This repository provides the official implementation of "Robust channel-wise illumination estimation." accepted in BMVC (2021).

Firas Laakom 4 Nov 08, 2022
Repository for the paper "Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data"

Sensory Spaces of English Perceptual Verbs This repository contains the code and collocational data described in the paper "Exploring the Sensory Spac

David Peng 0 Sep 07, 2021
A Pytorch Implementation of ClariNet

ClariNet A Pytorch Implementation of ClariNet (Mel Spectrogram -- Waveform) Requirements PyTorch 0.4.1 & python 3.6 & Librosa Examples Step 1. Downlo

Sungwon Kim 286 Sep 15, 2022
TensorFlow, PyTorch and Numpy layers for generating Orthogonal Polynomials

OrthNet TensorFlow, PyTorch and Numpy layers for generating multi-dimensional Orthogonal Polynomials 1. Installation 2. Usage 3. Polynomials 4. Base C

Chuan 29 May 25, 2022
Array Camera Ptychography

Array Camera Ptychography This repository provides the code for the following papers: Schulz, Timothy J., David J. Brady, and Chengyu Wang. "Photon-li

Brady lab in Optical Sciences 1 Nov 15, 2021
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
Fuzzing JavaScript Engines with Aspect-preserving Mutation

DIE Repository for "Fuzzing JavaScript Engines with Aspect-preserving Mutation" (in S&P'20). You can check the paper for technical details. Environmen

gts3.org (<a href=[email protected])"> 190 Dec 11, 2022
SmallInitEmb - LayerNorm(SmallInit(Embedding)) in a Transformer to improve convergence

SmallInitEmb LayerNorm(SmallInit(Embedding)) in a Transformer I find that when t

PENG Bo 11 Dec 25, 2022
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs Abstract: Image-to-image translation has recently achieved re

yaxingwang 23 Apr 14, 2022