QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

Overview

QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

Environment

  • Tested on Ubuntu 14.04 64bit and 16.04 64bit

Installation

# disable ptrace_scope for PIN
$ echo 0|sudo tee /proc/sys/kernel/yama/ptrace_scope

# install z3 and system deps
$ ./setup.sh

# install using virtual env
$ virtualenv venv
$ source venv/bin/activate
$ pip install .

Installation using Docker

# disable ptrace_scope for PIN
$ echo 0|sudo tee /proc/sys/kernel/yama/ptrace_scope

# build docker image
$ docker build -t qsym ./

# run docker image
$ docker run --cap-add=SYS_PTRACE -it qsym /bin/bash

Installation using vagrant

Since QSYM is dependent on underlying kernel because of its old PIN, we decided to provide a convenient way to install QSYM with VM. Please take a look our vagrant directory.

Run hybrid fuzzing with AFL

# require to set the following environment variables
#   AFL_ROOT: afl directory (http://lcamtuf.coredump.cx/afl/)
#   INPUT: input seed files
#   OUTPUT: output directory
#   AFL_CMDLINE: command line for a testing program for AFL (ASAN + instrumented)
#   QSYM_CMDLINE: command line for a testing program for QSYM (Non-instrumented)

# run AFL master
$ $AFL_ROOT/afl-fuzz -M afl-master -i $INPUT -o $OUTPUT -- $AFL_CMDLINE
# run AFL slave
$ $AFL_ROOT/afl-fuzz -S afl-slave -i $INPUT -o $OUTPUT -- $AFL_CMDLINE
# run QSYM
$ bin/run_qsym_afl.py -a afl-slave -o $OUTPUT -n qsym -- $QSYM_CMDLINE

Run for testing

$ cd tests
$ python build.py
$ python -m pytest -n $(nproc)

Troubleshooting

If you find that you can't get QSYM to work and you get the undefined symbol: Z3_is_seq_sort error in pin.log file, please make sure that you compile and make the target when you're in the virtualenv (env) environment. When you're out of this environment and you compile the target, QSYM can't work with the target binary and issues the mentioned error in pin.log file. This will save your time a lot to compile and make the target from env and then run QSYM on the target, then QSYM will work like a charm!

Authors

Publications

QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

@inproceedings{yun:qsym,
  title        = {{QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing}},
  author       = {Insu Yun and Sangho Lee and Meng Xu and Yeongjin Jang and Taesoo Kim},
  booktitle    = {Proceedings of the 27th USENIX Security Symposium (Security)},
  month        = aug,
  year         = 2018,
  address      = {Baltimore, MD},
}
Owner
gts3.org ([email protected])
https://gts3.org
gts3.org (<a href=[email protected])">
Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)

RMA-Net This repo is the implementation of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021). Paper

Wanquan Feng 205 Nov 09, 2022
NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch

PyTorch implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping Paper: https://arxiv.org/abs/2102.06171.pdf Original code: htt

Vaibhav Balloli 320 Jan 02, 2023
Audio Source Separation is the process of separating a mixture into isolated sounds from individual sources

Audio Source Separation is the process of separating a mixture into isolated sounds from individual sources (e.g. just the lead vocals).

Victor Basu 14 Nov 07, 2022
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Super Resolution Examples We run this script under TensorFlow 2.0 and the TensorLayer2.0+. For TensorLayer 1.4 version, please check release. 🚀 🚀 🚀

TensorLayer Community 2.9k Jan 08, 2023
null

DeformingThings4D dataset Video | Paper DeformingThings4D is an synthetic dataset containing 1,972 animation sequences spanning 31 categories of human

208 Jan 03, 2023
Code from Daniel Lemire, A Better Alternative to Piecewise Linear Time Series Segmentation

PiecewiseLinearTimeSeriesApproximation code from Daniel Lemire, A Better Alternative to Piecewise Linear Time Series Segmentation, SIAM Data Mining 20

Daniel Lemire 21 Oct 27, 2022
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Learning Pixel-level Semantic Affinity with Image-level Supervision This code is deprecated. Please see https://github.com/jiwoon-ahn/irn instead. Int

Jiwoon Ahn 337 Dec 15, 2022
A Tensorfflow implementation of Attend, Infer, Repeat

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models This is an unofficial Tensorflow implementation of Attend, Infear, Repeat (AIR)

Adam Kosiorek 82 May 27, 2022
DRLib:A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos.

DRLib:A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos A concise deep reinforcement learning libr

329 Jan 03, 2023
Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

Detecting Twenty-thousand Classes using Image-level Supervision Detic: A Detector with image classes that can use image-level labels to easily train d

Meta Research 1.3k Jan 04, 2023
Learning to Map Large-scale Sparse Graphs on Memristive Crossbar

Release of AutoGMap:Learning to Map Large-scale Sparse Graphs on Memristive Crossbar For reproduction of our searched model, the Ubuntu OS is recommen

2 Aug 23, 2022
Autoregressive Models in PyTorch.

Autoregressive This repository contains all the necessary PyTorch code, tailored to my presentation, to train and generate data from WaveNet-like auto

Christoph Heindl 41 Oct 09, 2022
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer

Spotify 10.6k Jan 04, 2023
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

Rishabh Anand 11 Mar 14, 2022
DetCo: Unsupervised Contrastive Learning for Object Detection

DetCo: Unsupervised Contrastive Learning for Object Detection arxiv link News Sparse RCNN+DetCo improves from 45.0 AP to 46.5 AP(+1.5) with 3x+ms trai

Enze Xie 234 Dec 18, 2022
Expert Finding in Legal Community Question Answering

Expert Finding in Legal Community Question Answering Arian Askari, Suzan Verberne, and Gabriella Pasi. Expert Finding in Legal Community Question Answ

Arian Askari 3 Oct 31, 2022
Image based Human Fall Detection

Here I integrated the YOLOv5 object detection algorithm with my own created dataset which consists of human activity images to achieve low cost, high accuracy, and real-time computing requirements

UTTEJ KUMAR 12 Dec 11, 2022
This is an official implementation for "Video Swin Transformers".

Video Swin Transformer By Ze Liu*, Jia Ning*, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin and Han Hu. This repo is the official implementation of "V

Swin Transformer 981 Jan 03, 2023
The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

MangaLineExtraction_PyTorch The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines" Usage model_torch.py [sourc

Miaomiao Li 82 Jan 02, 2023