An AFL implementation with UnTracer (our coverage-guided tracer)

Overview

UnTracer-AFL

This repository contains an implementation of our prototype coverage-guided tracing framework UnTracer in the popular coverage-guided fuzzer AFL. Coverage-guided tracing employs two versions of the target binary: (1) a forkserver-only oracle binary modified with basic block-level software interrupts on unseen basic blocks for quickly identifying coverage-increasing testcases and (2) a fully-instrumented tracer binary for tracing the coverage of all coverage-increasing testcases.

In UnTracer, both the oracle and tracer binaries use the AFL-inspired forkserver execution model. For oracle instrumentation we require all target binaries be compiled with untracer-cc -- our "forkserver-only" modification of AFL's assembly-time instrumenter afl-cc. For tracer binary instrumentation we utilize Dyninst with much of our code based-off AFL-Dyninst. We plan to incorporate a purely binary-only ("black-box") instrumentation approach in the near future. Our current implementation of UnTracer supports basic block coverage.

Presented in our paper Full-speed Fuzzing: Reducing Fuzzing Overhead through Coverage-guided Tracing
(2019 IEEE Symposium on Security and Privacy).
Citing this repository: @inproceedings{nagy:fullspeedfuzzing,
title = {Full-speed Fuzzing: Reducing Fuzzing Overhead through Coverage-guided Tracing},
author = {Stefan Nagy and Matthew Hicks},
booktitle = {{IEEE} Symposium on Security and Privacy (Oakland)},
year = {2019},}
Developers: Stefan Nagy ([email protected]) and Matthew Hicks ([email protected])
License: MIT License
Disclaimer: This software is strictly a research prototype.

INSTALLATION

1. Download and build Dyninst (we used v9.3.2)

sudo apt-get install cmake m4 zlib1g-dev libboost-all-dev libiberty-dev
wget https://github.com/dyninst/dyninst/archive/v9.3.2.tar.gz
tar -xf v9.3.2.tar.gz dyninst-9.3.2/
mkdir dynBuildDir
cd dynBuildDir
cmake ../dyninst-9.3.2/ -DCMAKE_INSTALL_PREFIX=`pwd`
make
make install

2. Download UnTracer-AFL (this repo)

git clone https://github.com/FoRTE-Research/UnTracer-AFL

3. Configure environment variables

export DYNINST_INSTALL=/path/to/dynBuildDir
export UNTRACER_AFL_PATH=/path/to/Untracer-AFL

export DYNINSTAPI_RT_LIB=$DYNINST_INSTALL/lib/libdyninstAPI_RT.so
export LD_LIBRARY_PATH=$DYNINST_INSTALL/lib:$UNTRACER_AFL_PATH
export PATH=$PATH:$UNTRACER_AFL_PATH

4. Build UnTracer-AFL

Update DYN_ROOT in UnTracer-AFL/Makefile to your Dyninst install directory. Then, run the following commands:

make clean && make all

USAGE

First, compile all target binaries using "forkserver-only" instrumentation. As with AFL, you will need to manually set the C compiler (untracer-clang or untracer-gcc) and/or C++ compiler (untracer-clang++ or untracer-g++). Note that only non-position-independent target binaries are supported, so compile all target binaries with CFLAG -no-pie (unnecessary for Clang). For example:

NOTE: We provide a set of fuzzing-ready benchmarks available here: https://github.com/FoRTE-Research/FoRTE-FuzzBench.

$ CC=/path/to/afl/untracer-clang ./configure --disable-shared
$ CXX=/path/to/afl/untracer-clang++.
$ make clean all
Instrumenting in forkserver-only mode...

Then, run untracer-afl as follows:

untracer-afl -i [/path/to/seed/dir] -o [/path/to/out/dir] [optional_args] -- [/path/to/target] [target_args]

Status Screen

  • calib execs and trim execs - Number of testcase calibration and trimming executions, respectively. Tracing is done for both.
  • block coverage - Percentage of total blocks found (left) and the number of total blocks (right).
  • traced / queued - Ratio of traced versus queued testcases. This ratio should (ideally) be 1:1 but will increase as trace timeouts occur.
  • trace tmouts (discarded) - Number of testcases which timed out during tracing. Like AFL, we do not queue these.
  • no new bits (discarded) - Number of testcases which were marked coverage-increasing by the oracle but did not actually increase coverage. This should (ideally) be 0.

Run containerized, rootless applications with podman

Why? restrict scope of file system access run any application without root privileges creates usable "Desktop applications" to integrate into your nor

119 Dec 27, 2022
Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in Pytorch

Retrieval-Augmented Denoising Diffusion Probabilistic Models (wip) Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in P

Phil Wang 55 Jan 01, 2023
Transformer in Vision

Transformer-in-Vision Recent Transformer-based CV and related works. Welcome to comment/contribute! Keep updated. Resource SCENIC: A JAX Library for C

Yong-Lu Li 1.1k Dec 30, 2022
Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".

Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time

David Álvarez de la Torre 0 Feb 09, 2022
Vehicle speed detection with python

Vehicle-speed-detection In the project simulate the tracker.py first then simulate the SpeedDetector.py. Finally, a new window pops up and the output

3 Dec 15, 2022
Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery"

SegSwap Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery" [PDF] [Project page] If our project

xshen 41 Dec 10, 2022
Repository sharing code and the model for the paper "Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes"

Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes Setup virtualenv -p python3 venv source venv/bin/activate pip instal

Planet AI GmbH 9 May 20, 2022
Code repository for the paper "Tracking People with 3D Representations"

Tracking People with 3D Representations Code repository for the paper "Tracking People with 3D Representations" (paper link) (project site). Jathushan

Jathushan Rajasegaran 77 Dec 03, 2022
TensorFlow implementation of ENet

TensorFlow-ENet TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. This model was tested on th

Kwotsin 255 Oct 17, 2022
Official code release for: EditGAN: High-Precision Semantic Image Editing

Official code release for: EditGAN: High-Precision Semantic Image Editing

565 Jan 05, 2023
Embeddinghub is a database built for machine learning embeddings.

Embeddinghub is a database built for machine learning embeddings.

Featureform 1.2k Jan 01, 2023
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
Pytorch implementation of the paper: "A Unified Framework for Separating Superimposed Images", in CVPR 2020.

Deep Adversarial Decomposition PDF | Supp | 1min-DemoVideo Pytorch implementation of the paper: "Deep Adversarial Decomposition: A Unified Framework f

Zhengxia Zou 72 Dec 18, 2022
Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM) Introduction The average lifetime of the $D^{0}$ me

Son Gyo Jung 1 Dec 17, 2021
Object DGCNN and DETR3D, Our implementations are built on top of MMdetection3D.

Object DGCNN & DETR3D This repo contains the implementations of Object DGCNN (https://arxiv.org/abs/2110.06923) and DETR3D (https://arxiv.org/abs/2110

Wang, Yue 539 Jan 07, 2023
Set of models for classifcation of 3D volumes

Classification models 3D Zoo - Keras and TF.Keras This repository contains 3D variants of popular CNN models for classification like ResNets, DenseNet

69 Dec 28, 2022
Benchmark spaces - Benchmarks of how well different two dimensional spaces work for clustering algorithms

benchmark_spaces Benchmarks of how well different two dimensional spaces work fo

Bram Cohen 6 May 07, 2022
Simple Dynamic Batching Inference

Simple Dynamic Batching Inference 解决了什么问题? 众所周知,Batch对于GPU上深度学习模型的运行效率影响很大。。。 是在Inference时。搜索、推荐等场景自带比较大的batch,问题不大。但更多场景面临的往往是稀碎的请求(比如图片服务里一次一张图)。 如果

116 Jan 01, 2023
Meta-meta-learning with evolution and plasticity

Evolve plastic networks to be able to automatically acquire novel cognitive (meta-learning) tasks

5 Jun 28, 2022
Bayesian dessert for Lasagne

Gelato Bayesian dessert for Lasagne Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the be

Maxim Kochurov 84 May 11, 2020