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

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

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

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

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-

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

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

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

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

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)
This repository includes code of my study about Asynchronous in Frequency domain of GAN images.

Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images Binh M. Le & Simon S. Woo, "Exploring the Asynchronous of the Frequ

4 Aug 06, 2022
Mining-the-Social-Web-3rd-Edition - The official online compendium for Mining the Social Web, 3rd Edition (O'Reilly, 2018)

Mining the Social Web, 3rd Edition The official code repository for Mining the Social Web, 3rd Edition (O'Reilly, 2019). The book is available from Am

Mikhail Klassen 838 Jan 01, 2023
Instance-Dependent Partial Label Learning

Instance-Dependent Partial Label Learning Installation pip install -r requirements.txt Run the Demo benchmark-random mnist python -u main.py --gpu 0 -

17 Dec 29, 2022
Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Recursive-NeRF: An Efficient and Dynamically Growing NeRF This is a Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

33 Nov 30, 2022
Dirty Pixels: Towards End-to-End Image Processing and Perception

Dirty Pixels: Towards End-to-End Image Processing and Perception This repository contains the code for the paper Dirty Pixels: Towards End-to-End Imag

50 Nov 18, 2022
JugLab 33 Dec 30, 2022
This repository provides code for "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness".

On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness This repository provides the code for the paper On Interaction B

Meta Research 33 Dec 08, 2022
ICNet for Real-Time Semantic Segmentation on High-Resolution Images, ECCV2018

ICNet for Real-Time Semantic Segmentation on High-Resolution Images by Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, Jiaya Jia, details a

Hengshuang Zhao 594 Dec 31, 2022
Keras Image Embeddings using Contrastive Loss

Keras-Image-Embeddings-using-Contrastive-Loss Image to Embedding projection in vector space. Implementation in keras and tensorflow for custom data. B

Shravan Anand K 5 Mar 21, 2022
Synthesizing and manipulating 2048x1024 images with conditional GANs

pix2pixHD Project | Youtube | Paper Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translatio

NVIDIA Corporation 6k Dec 27, 2022
Easy to use Python camera interface for NVIDIA Jetson

JetCam JetCam is an easy to use Python camera interface for NVIDIA Jetson. Works with various USB and CSI cameras using Jetson's Accelerated GStreamer

NVIDIA AI IOT 358 Jan 02, 2023
A semismooth Newton method for elliptic PDE-constrained optimization

sNewton4PDEOpt The Python module implements a semismooth Newton method for solving finite-element discretizations of the strongly convex, linear ellip

2 Dec 08, 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
A more easy-to-use implementation of KPConv

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 35 Dec 14, 2022
Code for the paper "How Attentive are Graph Attention Networks?"

How Attentive are Graph Attention Networks? This repository is the official implementation of How Attentive are Graph Attention Networks?. The PyTorch

175 Dec 29, 2022
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

CMUA-Watermark The official code for CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022) arxiv. It is bas

50 Nov 26, 2022
Towards Implicit Text-Guided 3D Shape Generation (CVPR2022)

Towards Implicit Text-Guided 3D Shape Generation Towards Implicit Text-Guided 3D Shape Generation (CVPR2022) Code for the paper [Towards Implicit Text

55 Dec 16, 2022
《Deep Single Portrait Image Relighting》(ICCV 2019)

Ratio Image Based Rendering for Deep Single-Image Portrait Relighting [Project Page] This is part of the Deep Portrait Relighting project. If you find

62 Dec 21, 2022
Neural Point-Based Graphics

Neural Point-Based Graphics Project   Video   Paper Neural Point-Based Graphics Kara-Ali Aliev1 Artem Sevastopolsky1,2 Maria Kolos1,2 Dmitry Ulyanov3

Ali Aliev 252 Dec 13, 2022
A GUI for Face Recognition, based upon Docker, Tkinter, GPU and a camera device.

Face Recognition GUI This repository is a GUI version of Face Recognition by Adam Geitgey, where e.g. Docker and Tkinter are utilized. All the materia

Kasper Henriksen 6 Dec 05, 2022