Differential fuzzing for the masses!

Related tags

Deep Learningnezha
Overview

NEZHA

NEZHA is an efficient and domain-independent differential fuzzer developed at Columbia University. NEZHA exploits the behavioral asymmetries between multiple test programs to focus on inputs that are more likely to trigger logic bugs.

What?

NEZHA features several runtime diversity-promoting metrics used to generate inputs for multi-app differential testing. These metrics are described in detail in the 2017 IEEE Symposium on Security and Privacy (Oakland) paper - NEZHA: Efficient Domain-Independent Differential Testing.

Getting Started

The current code is a WIP to port NEZHA to the latest libFuzzer and is non-tested. Users who wish to access the code used in the NEZHA paper and the respective examples should access v-0.1.

This repo follows the format of libFuzzer's fuzzer-test-suite. For a simple example on how to perform differential testing using the NEZHA port of libFuzzer see differential_fuzzing_tutorial.

Support

We welcome issues and pull requests with new fuzzing targets.

You might also like...
ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing
ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing ProFuzzBench is a benchmark for stateful fuzzing of network protocols. It includes a suite of

null 155 Jan 8, 2023
Emulation and Feedback Fuzzing of Firmware with Memory Sanitization
Emulation and Feedback Fuzzing of Firmware with Memory Sanitization

BaseSAFE This repository contains the BaseSAFE Rust APIs, introduced by "BaseSAFE: Baseband SAnitized Fuzzing through Emulation". The example/ directo

Security in Telecommunications 138 Dec 16, 2022
A fuzzing framework for SMT solvers
A fuzzing framework for SMT solvers

yinyang A fuzzing framework for SMT solvers. Given a set of seed SMT formulas, yinyang generates mutant formulas to stress-test SMT solvers. yinyang c

Project Yin-Yang for SMT Solver Testing 145 Jan 4, 2023
AntiFuzz: Impeding Fuzzing Audits of Binary Executables

AntiFuzz: Impeding Fuzzing Audits of Binary Executables Get the paper here: https://www.usenix.org/system/files/sec19-guler.pdf Usage: The python scri

Chair for Sys­tems Se­cu­ri­ty 88 Dec 21, 2022
Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

About Fuzzification Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-

gts3.org (SSLab@Gatech) 55 Oct 25, 2022
Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems Paper Finding Semantic Bugs in File Systems with an Extensible Fuzzin

gts3.org (SSLab@Gatech) 129 Dec 15, 2022
Fuzzing the Kernel Using Unicornafl and AFL++
Fuzzing the Kernel Using Unicornafl and AFL++

Unicorefuzz Fuzzing the Kernel using UnicornAFL and AFL++. For details, skim through the WOOT paper or watch this talk at CCCamp19. Is it any good? ye

Security in Telecommunications 283 Dec 26, 2022
Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS an

Chair for Sys­tems Se­cu­ri­ty 541 Nov 27, 2022
QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing Environment Tested on Ubuntu 14.04 64bit and 16.04 64bit Installation # disabl

gts3.org (SSLab@Gatech) 581 Dec 30, 2022
Comments
  • Building WolfSSl and mbedTLS

    Building WolfSSl and mbedTLS

    Hi,

    I would like to test out Nezha on the WolfSSL and mbedTLS libraries. Could you share out the below files, please? Thanks!

    build_wolfssl_lf.sh build_mbedtls_lf.sh

    opened by ghost 0
  • Unable to install LibFuzzer (for Nezha v0.1)

    Unable to install LibFuzzer (for Nezha v0.1)

    Hi,

    I cloned nezha-0.1 and run the ./utils/build_helpers/setup.sh but the setup was terminated when I received an error message "FAILED" during the Installation of LibFuzzer.

    I opened the README.txt in the directory /nezha-0.1/examples/src/libs/libFuzzer/ and it says "libFuzzer was moved to compiler-rt in https://reviews.llvm.org/D36908"

    Did you encounter the same issue? thanks!

    opened by ghost 0
  • Problem in Tutorial

    Problem in Tutorial

    When I try to follow the tutorial by running mkdir -p out && ./a.out -diff_mode=1 -artifact_prefix=out/ I get the following error:

    INFO: Seed: 3228985162
a.out: ./FuzzerTracePC.cpp:52: void fuzzer::TracePC::InitializeDiffCallbacks(fuzzer::ExternalFunctions *): Assertion `EF->__sanitizer_update_counter_bitset_and_clear_counters' failed.
Aborted
    opened by ppashakhanloo 2
  • Problems found in nezha v-0.1

    Problems found in nezha v-0.1

    1

    In the file "/examples/bugs/boringssl-f0451ca3/README.md", the 27th line says "cmd:./test_boringssl ..." and the 43rd line says "cmd:./test_libressl ...". The "./test_boringssl ..." and "./test_libressl ..." were run in the directory "sslcert" but the bash said "./test_boringssl: No such file or directory" and "./test_libressl: No such file or directory".
    Do the "./test_boringssl" and "./test_libressl"point to "./test_boringssl.pem.dbg" or "./test_boringssl.der.dbg" or "./test_libressl.pem.dbg" or "./test_libressl.der.dbg" which are generated after executing "./make_all_tests.sh"? If not, how to generate them?

    2

    In the same file, the same line says "...18010_0_18010_..." and the 36th line says "openssl: 18010". Does the "18010" in the 36th line refer to the first "...18010_..." or the second "...0_18010..." in the 27th line?

    3

    In the same file, the 51st line says "libressl: 1 (ok)". Is the number "1" the return value of LibreSSL? If yes, why "18010_0_18010" instead of "18010_1_1801" in the 27th line?

    On the contrary, the 57th line of the file "examples/bugs/libressl-2.4.0/README.md" says "openssl: 1 (ok) and the 48th line ("1_libressl_9010_0689e3080ef6eedb9fee46e0bf9ed8fe__MIN") starts with "1".

    4

    In the 48th line of the file "examples/bugs/libressl-2.4.0/README.md", "1_libressl_9010_0689e3080ef6eedb9fee46e0bf9ed8fe__MIN" does not have the same format as in the 27th line of "/examples/bugs/boringssl-f0451ca3/README.md", i.e., "1_libressl_9010" vs "18010_1_1801".

    5

    (This problem has been deleted since it was solved.)

    6

    In the file "/examples/bugs/boringssl-f0451ca3/README.md", the "stdout" (from the 32nd line to the 35th line) is the output of "./test_openssl.der.dbg" instead of "./test_boringssl.der.dbg". The 36th line, i.e., "openssl: 18010" is not output by the "./test_boringssl.der.dbg". Similarly, the 51st line is not output by "./test_libressl.der.dbg".

    In the file "examples/bugs/libressl-2.4.0/README.md", the 57th line is not output by the "./test_openssl.der.dbg"; the 69th line is not output but the "[LSSL] [cert:0x62000000f080 sz:3494] ret=0 depth=2 err=13" is got; the 70th and 71st line are not output by "./test_openssl.der.dbg".

    Thanks a lot!

    opened by pyjavago 1
Releases(v0.1)
Owner
GitHub Repository
Implementation for NeurIPS 2021 Submission: SparseFed

READ THIS FIRST This repo is an anonymized version of an existing repository of GitHub, for the AIStats 2021 submission: SparseFed: Mitigating Model P

2 Jun 15, 2022
Code for MarioNette: Self-Supervised Sprite Learning, in NeurIPS 2021

MarioNette | Webpage | Paper | Video MarioNette: Self-Supervised Sprite Learning Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Ale

Dima Smirnov 28 Nov 18, 2022
Code of 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces

3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces Installation After cloning the repo open

37 Dec 03, 2022
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

CCNet: Criss-Cross Attention for Semantic Segmentation Paper Links: Our most recent TPAMI version with improvements and extensions (Earlier ICCV versi

Zilong Huang 1.3k Dec 27, 2022
Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

This repository contains code for the following two papers: VisualBERT: A Simple and Performant Baseline for Vision and Language (arxiv) with a short

Natural Language Processing @UCLA 463 Dec 09, 2022
PyTorch implementation of paper "StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement" (ICCV 2021 Oral)

StarEnhancer StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement (ICCV 2021 Oral) Abstract: Image enhancement is a subjective process w

IDKiro 133 Dec 28, 2022
Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation This repository contains the official PyTorch implementation of the following

Wonjong Jang 270 Dec 30, 2022
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

Deep Cognition and Language Research (DeCLaRe) Lab 398 Dec 30, 2022
Official Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.

An Empirical Investigation of 3D Anomaly Detection and Segmentation Project | Paper Official PyTorch Implementation for the "An Empirical Investigatio

Eliahu Horwitz 55 Dec 14, 2022
This is an official implementation for "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"

DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation This repo is the official implementation of "DeciWatch: A Simple Baseline for

117 Dec 24, 2022
A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]

A 2D Visual Localization Framework based on Essential Matrices This repository provides implementation of our paper accepted at ICRA: To Learn or Not

Qunjie Zhou 27 Nov 07, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

DroneCrowd Paper Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark. Introduction This paper proposes a space-time multi-scale atte

VisDrone 98 Nov 16, 2022
PyTorch Implementation of ECCV 2020 Spotlight TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images

TuiGAN-PyTorch Official PyTorch Implementation of "TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images" (ECCV 2020 Spotligh

181 Dec 09, 2022
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes

FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes This repository contains the source code accompanying the paper: FlexConv: C

Robert-Jan Bruintjes 96 Dec 12, 2022
CAST: Character labeling in Animation using Self-supervision by Tracking

CAST: Character labeling in Animation using Self-supervision by Tracking (Published as a conference paper at EuroGraphics 2022) Note: The CAST paper c

15 Nov 18, 2022
TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments for IV 2022.

TorchGRL TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffi

XXQQ 42 Dec 09, 2022
The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

Inferring Spatial Uncertainty in Object Detection A teaser version of the code for the paper Labels Are Not Perfect: Inferring Spatial Uncertainty in

ZINING WANG 21 Mar 03, 2022
PAWS 🐾 Predicting View-Assignments with Support Samples

This repo provides a PyTorch implementation of PAWS (predicting view assignments with support samples), as described in the paper Semi-Supervised Learning of Visual Features by Non-Parametrically Pre

Facebook Research 437 Dec 23, 2022
Fewshot-face-translation-GAN - Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.

Few-shot face translation A GAN based approach for one model to swap them all. The table below shows our priliminary face-swapping results requiring o

768 Dec 24, 2022