Inferred Model-based Fuzzer

Overview

IMF: Inferred Model-based Fuzzer

IMF is a kernel API fuzzer that leverages an automated API model inferrence techinque proposed in our paper at CCS. IMF currently only supports macOS. To see how to configure and run IMF, see the followings.

Setup

Requirements

  • python2.7
  • pypy
  • clang

How to run

  1. Generate hooking library for APIs
$ ./gen-hook [output(hooking code) path]
$ clang  -Wall -dynamiclib -framework IOKit -framework CoreFoundation -arch i386\
         -arch x86_64 hook.c -o hook
  1. Collect logs
$ DYLD_INSERT_LIBRARIES=[hooking library path] [program path] [program args]
  1. Filter logs
$ ./filter-log [log dir] [output dir] [# of output(filtered log)] [# of core]
  1. Infer a model and generate a fuzzer.
$ ./gen-fuzz [filtered logs path] [output(fuzzer code) path] [# of core]
  1. Compile the fuzzer
$ clang -framework IOKit -framework CoreFoundation -arch i386 fuzz.c -o fuzz
  1. Run the fuzzer
$ ./fuzz -f [log path] -s [seed] -b [bitlen] -r [rate] -l [# of max loops]
  1. You may want to run the generated fuzzer within a while loop.

CVEs

  • CVE-2017-7159

Authors

This research project has been conducted by SoftSec Lab at KAIST.

Citing IMF

To cite our paper (pdf):

@INPROCEEDINGS{han:ccs2017,
    author = {HyungSeok Han and Sang Kil Cha},
    title = {Inferred Model-based Fuzzing},
    booktitle = {Proceedings of the ACM Conference on Computer and Communications Security},
    year = {2017},
    pages = {2345--2358}
}

Acknowledgement

The work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT).

Owner
SoftSec Lab
SoftSec Lab @ KAIST
SoftSec Lab
[ICCV 2021] Group-aware Contrastive Regression for Action Quality Assessment

CoRe Created by Xumin Yu*, Yongming Rao*, Wenliang Zhao, Jiwen Lu, Jie Zhou This is the PyTorch implementation for ICCV paper Group-aware Contrastive

Xumin Yu 31 Dec 24, 2022
Fast and customizable reconnaissance workflow tool based on simple YAML based DSL.

Fast and customizable reconnaissance workflow tool based on simple YAML based DSL, with support of notifications and distributed workload of that work

Américo Júnior 3 Mar 11, 2022
Official Implementation of "Designing an Encoder for StyleGAN Image Manipulation"

Designing an Encoder for StyleGAN Image Manipulation (SIGGRAPH 2021) Recently, there has been a surge of diverse methods for performing image editing

749 Jan 09, 2023
Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

PortraitNet Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device". @ CAD&Graphics 2019 Introduction We propose a

265 Dec 01, 2022
null

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

208 Jan 03, 2023
Gesture-Volume-Control - This Python program can adjust the system's volume by using hand gestures

Gesture-Volume-Control This Python program can adjust the system's volume by usi

VatsalAryanBhatanagar 1 Dec 30, 2021
EMNLP'2021: SimCSE: Simple Contrastive Learning of Sentence Embeddings

SimCSE: Simple Contrastive Learning of Sentence Embeddings This repository contains the code and pre-trained models for our paper SimCSE: Simple Contr

Princeton Natural Language Processing 2.5k Dec 29, 2022
Predicting future trajectories of people in cameras of novel scenarios and views.

Pedestrian Trajectory Prediction Predicting future trajectories of pedestrians in cameras of novel scenarios and views. This repository contains the c

8 Sep 03, 2022
Customer Segmentation using RFM

Customer-Segmentation-using-RFM İş Problemi Bir e-ticaret şirketi müşterilerini segmentlere ayırıp bu segmentlere göre pazarlama stratejileri belirlem

Nazli Sener 7 Dec 26, 2021
Optimizing synthesizer parameters using gradient approximation

Optimizing synthesizer parameters using gradient approximation NASH 2021 Hackathon! These are some experiments I conducted during NASH 2021, the Neura

Jordie Shier 10 Feb 10, 2022
Code for the paper 'A High Performance CRF Model for Clothes Parsing'.

Clothes Parsing Overview This code provides an implementation of the research paper: A High Performance CRF Model for Clothes Parsing Edgar Simo-S

Edgar Simo-Serra 119 Nov 21, 2022
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction This is the implementation of DeepSTD in

5 Sep 26, 2022
Official implementation of VQ-Diffusion

Official implementation of VQ-Diffusion: Vector Quantized Diffusion Model for Text-to-Image Synthesis

Microsoft 592 Jan 03, 2023
Training DiffWave using variational method from Variational Diffusion Models.

Variational DiffWave Training DiffWave using variational method from Variational Diffusion Models. Quick Start python train_distributed.py discrete_10

Chin-Yun Yu 26 Dec 13, 2022
A lossless neural compression framework built on top of JAX.

Kompressor Branch CI Coverage main (active) main development A neural compression framework built on top of JAX. Install setup.py assumes a compatible

Rosalind Franklin Institute 2 Mar 14, 2022
[ICLR2021oral] Rethinking Architecture Selection in Differentiable NAS

DARTS-PT Code accompanying the paper ICLR'2021: Rethinking Architecture Selection in Differentiable NAS Ruochen Wang, Minhao Cheng, Xiangning Chen, Xi

Ruochen Wang 86 Dec 27, 2022
Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/

Peng Qiao 1 Dec 14, 2021
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation (CVPR2019) This is a pytorch implementatio

Yawei Luo 280 Jan 01, 2023
LightningFSL: Pytorch-Lightning implementations of Few-Shot Learning models.

LightningFSL: Few-Shot Learning with Pytorch-Lightning In this repo, a number of pytorch-lightning implementations of FSL algorithms are provided, inc

Xu Luo 76 Dec 11, 2022
A powerful framework for decentralized federated learning with user-defined communication topology

Scatterbrained Decentralized Federated Learning Scatterbrained makes it easy to build federated learning systems. In addition to traditional federated

Johns Hopkins Applied Physics Laboratory 7 Sep 26, 2022