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])">
Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction".

TGIN Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction". Files in the folder dataset/ electr

Alibaba 21 Dec 21, 2022
SemEval2022 Patronizing and Condescending Language (PCL) Detection

SemEval2022 Patronizing and Condescending Language (PCL) Detection This task is from SemEval 2022. What is Patronizing and Condescending Language (PCL

Daniel Saeedi 0 Aug 05, 2022
Code for "Steerable Pyramid Transform Enables Robust Left Ventricle Quantification"

Code for "Steerable Pyramid Transform Enables Robust Left Ventricle Quantification" This is an end-to-end framework for accurate and robust left ventr

2 Jul 09, 2022
codes for IKM (arXiv2021, Submitted to IEEE Trans)

Image-specific Convolutional Kernel Modulation for Single Image Super-resolution This repository is for IKM introduced in the following paper Yuanfei

Yuanfei Huang 9 Dec 29, 2022
Human Detection - Pedestrian Detection using OpenCV Python

Pedestrian Detection using OpenCV Python Follow us on Instagram for Machine Lear

Hrishikesh Dutta 1 Jan 23, 2022
“Data Augmentation for Cross-Domain Named Entity Recognition” (EMNLP 2021)

Data Augmentation for Cross-Domain Named Entity Recognition Authors: Shuguang Chen, Gustavo Aguilar, Leonardo Neves and Thamar Solorio This repository

<a href=[email protected]"> 18 Sep 10, 2022
x-transformers-paddle 2.x version

x-transformers-paddle x-transformers-paddle 2.x version paddle 2.x版本 https://github.com/lucidrains/x-transformers 。 requirements paddlepaddle-gpu==2.2

yujun 7 Dec 08, 2022
PyTorch implementation of probabilistic deep forecast applied to air quality.

Probabilistic Deep Forecast PyTorch implementation of a paper, titled: Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Abdulmajid Murad 13 Nov 16, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
Apollo optimizer in tensorflow

Apollo Optimizer in Tensorflow 2.x Notes: Warmup is important with Apollo optimizer, so be sure to pass in a learning rate schedule vs. a constant lea

Evan Walters 1 Nov 09, 2021
PyTorch Implementation of CycleGAN and SSGAN for Domain Transfer (Minimal)

MNIST-to-SVHN and SVHN-to-MNIST PyTorch Implementation of CycleGAN and Semi-Supervised GAN for Domain Transfer. Prerequites Python 3.5 PyTorch 0.1.12

Yunjey Choi 401 Dec 30, 2022
This repo contains the source code and a benchmark for predicting user's utilities with Machine Learning techniques for Computational Persuasion

Machine Learning for Argument-Based Computational Persuasion This repo contains the source code and a benchmark for predicting user's utilities with M

Ivan Donadello 4 Nov 07, 2022
PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

Learning to Reweight Examples for Robust Deep Learning Unofficial PyTorch implementation of Learning to Reweight Examples for Robust Deep Learning. Th

Daniel Stanley Tan 325 Dec 28, 2022
[ACM MM 2021] Joint Implicit Image Function for Guided Depth Super-Resolution

Joint Implicit Image Function for Guided Depth Super-Resolution This repository contains the code for: Joint Implicit Image Function for Guided Depth

hawkey 78 Dec 27, 2022
商品推荐系统

商品top50推荐系统 问题建模 本项目的数据集给出了15万左右的用户以及12万左右的商品, 以及对应的经过脱敏处理的用户特征和经过预处理的商品特征,旨在为用户推荐50个其可能购买的商品。 推荐系统架构方案 本项目采用传统的召回+排序的方案。

107 Dec 29, 2022
PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambiguation for Partial Label Learning

PiCO: Contrastive Label Disambiguation for Partial Label Learning This is a PyTorch implementation of ICLR 2022 Oral paper PiCO; also see our Project

王皓波 147 Jan 07, 2023
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

MIRA Lab 33 Dec 07, 2022
A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).

Torch-RGCN Torch-RGCN is a PyTorch implementation of the RGCN, originally proposed by Schlichtkrull et al. in Modeling Relational Data with Graph Conv

Thiviyan Singam 66 Nov 30, 2022
Tensorflow Implementation of ECCV'18 paper: Multimodal Human Motion Synthesis

MT-VAE for Multimodal Human Motion Synthesis This is the code for ECCV 2018 paper MT-VAE: Learning Motion Transformations to Generate Multimodal Human

Xinchen Yan 36 Oct 02, 2022
Official code for paper "Demystifying Local Vision Transformer: Sparse Connectivity, Weight Sharing, and Dynamic Weight"

Demysitifing Local Vision Transformer, arxiv This is the official PyTorch implementation of our paper. We simply replace local self attention by (dyna

138 Dec 28, 2022