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
GitHub Repository
This is a vision-based 3d model manipulation and control UI

Manipulation of 3D Models Using Hand Gesture This program allows user to manipulation 3D models (.obj format) with their hands. The project support bo

Cortic Technology Corp. 43 Oct 23, 2022
A library for graph deep learning research

Documentation | Paper [JMLR] | Tutorials | Benchmarks | Examples DIG: Dive into Graphs is a turnkey library for graph deep learning research. Why DIG?

DIVE Lab, Texas A&M University 1.3k Jan 01, 2023
​TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

TextWorld A text-based game generator and extensible sandbox learning environment for training and testing reinforcement learning (RL) agents. Also ch

Microsoft 983 Dec 23, 2022
Code for the paper "Offline Reinforcement Learning as One Big Sequence Modeling Problem"

Trajectory Transformer Code release for Offline Reinforcement Learning as One Big Sequence Modeling Problem. Installation All python dependencies are

Michael Janner 266 Dec 27, 2022
Code for paper Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Decoupled Spatial-Temporal Graph Neural Networks Code for our paper: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.

S22 43 Jan 04, 2023
Lipstick ain't enough: Beyond Color-Matching for In-the-Wild Makeup Transfer (CVPR 2021)

Table of Content Introduction Datasets Getting Started Requirements Usage Example Training & Evaluation CPM: Color-Pattern Makeup Transfer CPM is a ho

VinAI Research 248 Dec 13, 2022
Implicit Graph Neural Networks

Implicit Graph Neural Networks This repository is the official PyTorch implementation of "Implicit Graph Neural Networks". Fangda Gu*, Heng Chang*, We

Heng Chang 48 Nov 29, 2022
FID calculation with proper image resizing and quantization steps

clean-fid: Fixing Inconsistencies in FID Project | Paper The FID calculation involves many steps that can produce inconsistencies in the final metric.

Gaurav Parmar 606 Jan 06, 2023
[v1 (ISBI'21) + v2] MedMNIST: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification

MedMNIST Project (Website) | Dataset (Zenodo) | Paper (arXiv) | MedMNIST v1 (ISBI'21) Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bili

683 Dec 28, 2022
[CVPR 2021 Oral] Variational Relational Point Completion Network

VRCNet: Variational Relational Point Completion Network This repository contains the PyTorch implementation of the paper: Variational Relational Point

PL 121 Dec 12, 2022
Registration Loss Learning for Deep Probabilistic Point Set Registration

RLLReg This repository contains a Pytorch implementation of the point set registration method RLLReg. Details about the method can be found in the 3DV

Felix Järemo Lawin 35 Nov 02, 2022
Text Extraction Formulation + Feedback Loop for state-of-the-art WSD (EMNLP 2021)

ConSeC is a novel approach to Word Sense Disambiguation (WSD), accepted at EMNLP 2021. It frames WSD as a text extraction task and features a feedback loop strategy that allows the disambiguation of

Sapienza NLP group 36 Dec 13, 2022
Memory efficient transducer loss computation

Introduction This project implements the optimization techniques proposed in Improving RNN Transducer Modeling for End-to-End Speech Recognition to re

Fangjun Kuang 51 Nov 25, 2022
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields Paper | Supplementary | Video | Poster If you find our code or paper useful, please

26 Nov 29, 2022
Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch]

Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch] Abstract Snapshot compressive imaging (SCI) can rec

integirty 6 Nov 01, 2022
This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

README This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et a

Raghav 42 Dec 15, 2022
Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Welcome to AirSim AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open

Microsoft 13.8k Jan 05, 2023
Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

Blacksmith Rowhammer Fuzzer This repository provides the code accompanying the paper Blacksmith: Scalable Rowhammering in the Frequency Domain that is

Computer Security Group @ ETH Zurich 173 Nov 16, 2022
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods

ADGC: Awesome Deep Graph Clustering ADGC is a collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes and datasets).

yueliu1999 297 Dec 27, 2022
This was initially the repo for the project of [email protected] of Asaf Mazar, Millad Kassaie and Georgios Chochlakis named "Powered by the Will? Exploring Lay Theories of Behavior Change through Social Media"

Subreddit Analysis This repo includes tools for Subreddit analysis, originally developed for our class project of PSYC 626 in USC, titled "Powered by

Georgios Chochlakis 1 Dec 17, 2021