IJON is an annotation mechanism that analysts can use to guide fuzzers such as AFL.

Related tags

Deep Learningijon
Overview

IJON SPACE EXPLORER

loading-ag-167

IJON is an annotation mechanism that analysts can use to guide fuzzers such as AFL. Using only a small (usually one line) annotation, one can help the fuzzer solve previously unsolvable challenges. For example, with this extension, a fuzzer is able to play and solve games such as Super Mario Bros. or resolve more complex patterns such as hash map lookups.

More data and the results of the experiments can be found here:

Compile AFL+IJON

after compiling AFL as usually, run:

cd llvm_mode
LLVM_CONFIG=llvm-config-6.0 CC=clang-6.0 make

Annotations

When using afl-clang-fastwith Ijon, you can use the following annotations & helper functions in you program to guide AFL.

void ijon_xor_state(ijon_u32_t);
void ijon_push_state(ijon_u32_t);

void ijon_map_inc(ijon_u32_t);
void ijon_map_set(ijon_u32_t);

ijon_u32_t ijon_strdist(char* a,char* b);
ijon_u32_t ijon_memdist(char* a,char* b, ijon_size_t len);

void ijon_max(ijon_u32_t addr, ijon_u64_t val);

void ijon_min(ijon_u32_t addr, ijon_u64_t val);

ijon_u64_t ijon_simple_hash(ijon_u64_t val);
ijon_u32_t ijon_hashint(ijon_u32_t old, ijon_u32_t val);
ijon_u32_t ijon_hashstr(ijon_u32_t old, char* val);
ijon_u32_t ijon_hashmem(ijon_u32_t old, char* val, ijon_size_t len);

uint32_t ijon_hashstack(); //warning, can be flaky as stackunwinding is nontrivial

void ijon_enable_feedback();
void ijon_disable_feedback();

#define _IJON_CONCAT(x, y) x##y
#define _IJON_UNIQ_NAME() IJON_CONCAT(temp,__LINE__)
#define _IJON_ABS_DIST(x,y) ((x)<(y) ? (y)-(x) : (x)-(y))

#define IJON_BITS(x) ((x==0)?{0}:__builtin_clz(x))
#define IJON_INC(x) ijon_map_inc(ijon_hashstr(__LINE__,__FILE__)^(x))
#define IJON_SET(x) ijon_map_set(ijon_hashstr(__LINE__,__FILE__)^(x))

#define IJON_CTX(x) ({ uint32_t hash = hashstr(__LINE__,__FILE__); ijon_xor_state(hash); __typeof__(x) IJON_UNIQ_NAME() = (x); ijon_xor_state(hash); IJON_UNIQ_NAME(); })

#define IJON_MAX(x) ijon_max(ijon_hashstr(__LINE__,__FILE__),(x))
#define IJON_MIN(x) ijon_max(ijon_hashstr(__LINE__,__FILE__),0xffffffffffffffff-(x))
#define IJON_CMP(x,y) IJON_INC(__builtin_popcount((x)^(y)))
#define IJON_DIST(x,y) ijon_min(ijon_hashstr(__LINE__,__FILE__), _IJON_ABS_DIST(x,y))
#define IJON_STRDIST(x,y) IJON_SET(ijon_hashint(ijon_hashstack(), ijon_strdist(x,y)))

TIPS on using IJON

You typically want to run AFL with IJON extension in slave mode with multiple other fuzzer instances. If IJON solved the challenging structure, the other fuzzers will pick up the resulting inputs, while ignoring the intermediate queue entries that IJON produced.

If you make extensive use of the IJON_MIN or IJON_MAX primitives, you might want to disable normal instrumentation using AFL_INST_RATIO=1 make.

If, for some reason you want to use the version exactly from the paper (even though it contains known bugs), please use this commit

Owner
Chair for Sys­tems Se­cu­ri­ty
Chair for Sys­tems Se­cu­ri­ty
Efficient Speech Processing Tookit for Automatic Speaker Recognition

Sugar Efficient Speech Processing Tookit for Automatic Speaker Recognition | HuggingFace | What's New EfficientTDNN: Efficient Architecture Search for

WangRui 14 Sep 14, 2022
Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch]

Ensemble Learning Priors Driven Deep Unfolding for Scalable Snapshot Compressive Imaging [PyTorch] Abstract Snapshot compressive imaging (SCI) can rec

integirty 6 Nov 01, 2022
I-BERT: Integer-only BERT Quantization

I-BERT: Integer-only BERT Quantization HuggingFace Implementation I-BERT is also available in the master branch of HuggingFace! Visit the following li

Sehoon Kim 139 Dec 27, 2022
Official implementation of the NeurIPS'21 paper 'Conditional Generation Using Polynomial Expansions'.

Conditional Generation Using Polynomial Expansions Official implementation of the conditional image generation experiments as described on the NeurIPS

Grigoris 4 Aug 07, 2022
An end-to-end project on customer segmentation

End-to-end Customer Segmentation Project Note: This project is in progress. Tools Used in This Project Prefect: Orchestrate workflows hydra: Manage co

Ocelot Consulting 8 Oct 06, 2022
Securetar - A streaming wrapper around python tarfile and allow secure handling files and support encryption

Secure Tar Secure Tarfile library It's a streaming wrapper around python tarfile

Pascal Vizeli 2 Dec 09, 2022
A few stylization coreML models that I've trained with CreateML

CoreML-StyleTransfer A few stylization coreML models that I've trained with CreateML You can open and use the .mlmodel files in the "models" folder in

Doron Adler 8 Aug 18, 2022
Codes to calculate solar-sensor zenith and azimuth angles directly from hyperspectral images collected by UAV. Works only for UAVs that have high resolution GNSS/IMU unit.

UAV Solar-Sensor Angle Calculation Table of Contents About The Project Built With Getting Started Prerequisites Installation Datasets Contributing Lic

Sourav Bhadra 1 Jan 15, 2022
A supplementary code for Editable Neural Networks, an ICLR 2020 submission.

Editable neural networks A supplementary code for Editable Neural Networks, an ICLR 2020 submission by Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Py

Anton Sinitsin 32 Nov 29, 2022
potpourri3d - An invigorating blend of 3D geometry tools in Python.

A Python library of various algorithms and utilities for 3D triangle meshes and point clouds. Managed by Nicholas Sharp, with new tools added lazily as needed. Currently, mainly bindings to C++ tools

Nicholas Sharp 295 Jan 05, 2023
🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

Deep CORAL A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016' Deep CORAL can learn

Andy Hsu 200 Dec 25, 2022
Unofficial implementation of Google "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization" in PyTorch

CutPaste CutPaste: image from paper Unofficial implementation of Google's "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization"

Lilit Yolyan 59 Nov 27, 2022
A pytorch-version implementation codes of paper: "BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation"

BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation A pytorch-version implementation

11 Oct 08, 2022
Public Models considered for emotion estimation from EEG

Emotion-EEG Set of models for emotion estimation from EEG. Composed by the combination of two deep-learing models learning together (RNN and CNN) with

Victor Delvigne 21 Dec 23, 2022
Benchmark spaces - Benchmarks of how well different two dimensional spaces work for clustering algorithms

benchmark_spaces Benchmarks of how well different two dimensional spaces work fo

Bram Cohen 6 May 07, 2022
Annotate datasets with a semi-trained or fully trained YOLOv5 model

YOLOv5 Auto Annotator Annotate datasets with a semi-trained or fully trained YOLOv5 model Prerequisites Ubuntu =20.04 Python =3.7 System dependencie

Akash James 3 May 14, 2022
The missing CMake project initializer

cmake-init - The missing CMake project initializer Opinionated CMake project initializer to generate CMake projects that are FetchContent ready, separ

1k Jan 01, 2023
Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection

CP-Cluster Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection, Instance Segme

Yichun Shen 41 Dec 08, 2022
Library to enable Bayesian active learning in your research or labeling work.

Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components

ElementAI 687 Dec 25, 2022
Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

Streamlit Demo: The Udacity Self-driving Car Image Browser This project demonstrates the Udacity self-driving-car dataset and YOLO object detection in

Streamlit 992 Jan 04, 2023