Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting

Overview

StochFuzz: A New Solution for Binary-only Fuzzing

test benchmark

loading-ag-167

StochFuzz is a (probabilistically) sound and cost-effective fuzzing technique for stripped binaries. It is facilitated by a novel incremental and stochastic rewriting technique that is particularly suitable for binary-only fuzzing. Any AFL-based fuzzer, which takes edge coverage (defined by AFL) as runtime feedback, can acquire benefits from StochFuzz to directly fuzz stripped binaries.

More data and the results of the experiments can be found here. Example cases of leveraging StochFuzz to improve advanced AFL-based fuzzers (AFL++ and Polyglot) can be found in system.md.

Clarifications

  • We adopt a new system design than the one from the paper. Details can be found at system.md.
  • In the paper, when we are talking about e9patch, we are actually talking about the binary-only fuzzing tool built upon e9patch, namely e9tool. Please refer to its website for more details.
  • StochFuzz provides sound rewriting for binaries without inlined data, and probabilistically sound rewriting for the rest.

Building StochFuzz

StochFuzz is built upon Keystone, Capstone, GLib, and libunwind.

These dependences can be built by build.sh. If you are trying to build StochFuzz in a clean container, make sure some standard tools like autoreconf and libtool are installed.

$ git clone https://github.com/ZhangZhuoSJTU/StochFuzz.git
$ cd StochFuzz
$ ./build.sh

StochFuzz itself can be built by GNU Make.

$ cd src
$ make release

We have tested StochFuzz on Ubuntu 18.04. If you have any issue when running StochFuzz on other systems, please kindly let us know.

How to Use

StochFuzz provides multiple rewriting options, which follows the AFL's style of passing arguments.

$ ./stoch-fuzz -h
stoch-fuzz 1.0.0 by <[email protected]>

./stoch-fuzz [ options ] -- target_binary [ ... ]

Mode settings:

  -S            - start a background daemon and wait for a fuzzer to attach (defualt mode)
  -R            - dry run target_binary with given arguments without an attached fuzzer
  -P            - patch target_binary without incremental rewriting
  -D            - probabilistic disassembly without rewriting
  -V            - show currently observed breakpoints

Rewriting settings:

  -g            - trace previous PC
  -c            - count the number of basic blocks with conflicting hash values
  -d            - disable instrumentation optimization
  -r            - assume the return addresses are only used by RET instructions
  -e            - install the fork server at the entrypoint instead of the main function
  -f            - forcedly assume there is data interleaving with code
  -i            - ignore the call-fallthrough edges to defense RET-misusing obfuscation

Other stuff:

  -h            - print this help
  -x execs      - set the number of executions after which a checking run will be triggered
                  set it as zero to disable checking runs (default: 200000)
  -t msec       - set the timeout for each daemon-triggering execution
                  set it as zero to ignore the timeout (default: 2000 ms)
  -l level      - set the log level, including INFO, WARN, ERROR, and FATAL (default: INFO)

Basic Usage

- It is worth first trying the advanced strategy (see below) because that is much more cost-effective.

To fuzz a stripped binary, namely example.out, we need to cd to the directory of the target binary. For example, if the full path of example.out is /root/example.out, we need to first cd /root/. Furthermore, it is dangerous to run two StochFuzz instances under the same directory. These restrictions are caused by some design faults and we will try to relax them in the future.

Assuming StochFuzz is located at /root/StochFuzz/src/stoch-fuzz, execute the following command to start rewriting the target binary.

$ cd /root/
$ /root/StochFuzz/src/stoch-fuzz -- example.out # do not use ./example.out here

After the initial rewriting, we will get a phantom file named example.out.phantom. This phantom file can be directly fuzzed by AFL or any AFL-based fuzzer. Note that the StochFuzz process would not stop during fuzzing, so please make sure the process is alive during fuzzing.

Here is a demo that shows how StochFuzz works.

asciicast

Advanced Usage

Compared with the compiler-based instrumentation (e.g., afl-clang-fast), StochFuzz has additional runtime overhead because it needs to emulate each CALL instruction to support stack unwinding.

Inspired by a recent work, we provide an advanced rewriting strategy where we do not emulate CALL instructions but wrap the _ULx86_64_step function from libunwind to support stack unwinding. This strategy works for most binaries but may fail in some cases like fuzzing statically linked binaries.

To enable such strategy, simply provide a -r option to StochFuzz.

$ cd /root/
$ /root/StochFuzz/src/stoch-fuzz -r -- example.out # do not use ./example.out here

Addtionally, before fuzzing, we need to prepare the AFL_PRELOAD environment variable for AFL.

$ export STOCHFUZZ_PRELOAD=$(/root/StochFuzz/scritps/stochfuzz_env.sh)
$ AFL_PRELOAD=$STOCHFUZZ_PRELOAD afl-fuzz -i seeds -o output -t 2000 -- example.out.phantom @@

Following demo shows how to apply this advanced strategy.

asciicast

Troubleshootings

Common issues can be referred to trouble.md. If it cannot help solve your problem, please kindly open a Github issue.

Besides, we provide some tips on using StochFuzz, which can be found at tips.md

Development

Currently, we have many todo items. We present them in todo.md.

We also present many pending decisions which we are hesitating to take, in todo.md. If you have any thought/suggestion, do not hesitate to let us know. It would be very appreciated if you can help us improve StochFuzz.

StochFuzz should be considered an alpha-quality software and it is likely to contain bugs.

I will try my best to maintain StochFuzz timely, but sometimes it may take me more time to respond. Thanks for your understanding in advance.

Cite

Zhang, Zhuo, et al. "STOCHFUZZ: Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting." 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 2021.

References

  • Duck, Gregory J., Xiang Gao, and Abhik Roychoudhury. "Binary rewriting without control flow recovery." Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation. 2020.
  • Meng, Xiaozhu, and Weijie Liu. "Incremental CFG patching for binary rewriting." Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 2021.
  • Aschermann, Cornelius, et al. "Ijon: Exploring deep state spaces via fuzzing." 2020 IEEE Symposium on Security and Privacy (SP). IEEE, 2020.
  • Google. “Google/AFL.” GitHub, github.com/google/AFL.
Owner
Zhuo Zhang
Zhuo Zhang
Sudoku solver - A sudoku solver with python

sudoku_solver A sudoku solver What is Sudoku? Sudoku (Japanese: 数独, romanized: s

Sikai Lu 0 May 22, 2022
A simplistic and efficient pure-python neural network library from Phys Whiz with CPU and GPU support.

A simplistic and efficient pure-python neural network library from Phys Whiz with CPU and GPU support.

Manas Sharma 19 Feb 28, 2022
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without

sianchen 22 May 28, 2022
Deep functional residue identification

DeepFRI Deep functional residue identification Citing @article {Gligorijevic2019, author = {Gligorijevic, Vladimir and Renfrew, P. Douglas and Koscio

Flatiron Institute 156 Dec 25, 2022
Pytorch implementation for ACMMM2021 paper "I2V-GAN: Unpaired Infrared-to-Visible Video Translation".

I2V-GAN This repository is the official Pytorch implementation for ACMMM2021 paper "I2V-GAN: Unpaired Infrared-to-Visible Video Translation". Traffic

69 Dec 31, 2022
PyTorch implementations of the NeRF model described in "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"

PyTorch NeRF and pixelNeRF NeRF: Tiny NeRF: pixelNeRF: This repository contains minimal PyTorch implementations of the NeRF model described in "NeRF:

Michael A. Alcorn 178 Dec 20, 2022
Code for the Higgs Boson Machine Learning Challenge organised by CERN & EPFL

A method to solve the Higgs boson challenge using Least Squares - Novae This project is the Project 1 of EPFL CS-433 Machine Learning. The project is

Giacomo Orsi 1 Nov 09, 2021
This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis "

kwd-extraction-study This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer

ping 543f 1 Dec 05, 2022
The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection .

GCoNet The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection . Trained model Download final_gconet.pth

Qi Fan 46 Nov 17, 2022
PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

Pseudo Numerical Methods for Diffusion Models on Manifolds (PNDM) This repo is the official PyTorch implementation for the paper Pseudo Numerical Meth

Luping Liu (刘路平) 196 Jan 05, 2023
A Tensorflow based library for Time Series Modelling with Gaussian Processes

Markovflow Documentation | Tutorials | API reference | Slack What does Markovflow do? Markovflow is a Python library for time-series analysis via prob

Secondmind Labs 24 Dec 12, 2022
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 917 Jan 03, 2023
PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners

Masked Autoencoders: A PyTorch Implementation This is a PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners: @

Meta Research 4.8k Jan 04, 2023
Implement A3C for Mujoco gym envs

pytorch-a3c-mujoco Disclaimer: my implementation right now is unstable (you ca refer to the learning curve below), I'm not sure if it's my problems. A

Andrew 70 Dec 12, 2022
CMT: Convolutional Neural Networks Meet Vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers [arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no refer

FlyEgle 83 Dec 30, 2022
We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

Auto-exposure fusion for single-image shadow removal We propose a new method for effective shadow removal by regarding it as an exposure fusion proble

Qing Guo 146 Dec 31, 2022
🗺 General purpose U-Network implemented in Keras for image segmentation

TF-Unet General purpose U-Network implemented in Keras for image segmentation Getting started • Training • Evaluation Getting started Looking for Jupy

Or Fleisher 2 Aug 31, 2022
PyTorch Implementation for Fracture Detection in Wrist Bone X-ray Images

wrist-d PyTorch Implementation for Fracture Detection in Wrist Bone X-ray Images note: Paper: Under Review at MPDI Diagnostics Submission Date: Novemb

Fatih UYSAL 5 Oct 12, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022
Combinatorial model of ligand-receptor binding

Combinatorial model of ligand-receptor binding The binding of ligands to receptors is the starting point for many import signal pathways within a cell

Mobolaji Williams 0 Jan 09, 2022