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])">
Matthew Colbrook 1 Apr 08, 2022
Official code repository for ICCV 2021 paper: Gravity-Aware Monocular 3D Human Object Reconstruction

GraviCap Official code repository for ICCV 2021 paper: Gravity-Aware Monocular 3D Human Object Reconstruction. Gravity-Aware Monocular 3D Human-Object

Rishabh Dabral 15 Dec 09, 2022
Vision-Language Transformer and Query Generation for Referring Segmentation (ICCV 2021)

Vision-Language Transformer and Query Generation for Referring Segmentation Please consider citing our paper in your publications if the project helps

Henghui Ding 143 Dec 23, 2022
Code for the paper "Balancing Training for Multilingual Neural Machine Translation, ACL 2020"

Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat

Xinyi Wang 21 May 18, 2022
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at .

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

Aayush Bansal 196 Aug 10, 2022
Notes taking website build with Docker + Django + React.

Notes website. Try it in browser! / But how to run? Description. This is monorepository with notes website. Website provides web interface for creatin

Kirill Zhosul 2 Jul 27, 2022
🗣️ Microsoft Edge TTS for Home Assistant, no need for app_key

Microsoft Edge TTS for Home Assistant This component is based on the TTS service of Microsoft Edge browser, no need to apply for app_key. Install Down

152 Dec 31, 2022
Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms

Open-L2O This repository establishes the first comprehensive benchmark efforts of existing learning to optimize (L2O) approaches on a number of proble

VITA 161 Jan 02, 2023
Cosine Annealing With Warmup

CosineAnnealingWithWarmup Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an

zhuyun 4 Apr 18, 2022
Implementation of Squeezenet in pytorch, pretrained models on Cifar 10 data to come

Pytorch Squeeznet Pytorch implementation of Squeezenet model as described in https://arxiv.org/abs/1602.07360 on cifar-10 Data. The definition of Sque

gaurav pathak 86 Oct 28, 2022
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis [Paper] [Online Demo] The following results are obtained by our SCUNet with purely syn

Kai Zhang 312 Jan 07, 2023
Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution

Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution Abstract Within the Latin (and ancient Greek) production, it is well

4 Dec 03, 2022
U-Net for GBM

My Final Year Project(FYP) In National University of Singapore(NUS) You need Pytorch(stable 1.9.1) Both cuda version and cpu version are OK File Str

PinkR1ver 1 Oct 27, 2021
Matlab Python Heuristic Battery Opt - SMOP conversion and manual conversion

SMOP is Small Matlab and Octave to Python compiler. SMOP translates matlab to py

Tom Xu 1 Jan 12, 2022
Code release for "COTR: Correspondence Transformer for Matching Across Images"

COTR: Correspondence Transformer for Matching Across Images This repository contains the inference code for COTR. We plan to release the training code

UBC Computer Vision Group 360 Jan 06, 2023
Semi-Supervised Learning with Ladder Networks in Keras. Get 98% test accuracy on MNIST with just 100 labeled examples !

Semi-Supervised Learning with Ladder Networks in Keras This is an implementation of Ladder Network in Keras. Ladder network is a model for semi-superv

Divam Gupta 101 Sep 07, 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
Analysing poker data from home games with friends

Poker Game Analysis Analysing poker data from home games with friends. Not a lot of data is collected, so this project is primarily focussed on descri

Stavros Karmaniolos 1 Oct 15, 2022
Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition

Integrated Semantic and Phonetic Post-correction for Chinese Speech Recognition | paper | dataset | pretrained detection model | Authors: Yi-Chang Che

Yi-Chang Chen 1 Aug 23, 2022
Google Brain - Ventilator Pressure Prediction

Google Brain - Ventilator Pressure Prediction https://www.kaggle.com/c/ventilator-pressure-prediction The ventilator data used in this competition was

Samuele Cucchi 1 Feb 11, 2022