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
Storage-optimizer - Identify potintial optimizations on the cloud storage accounts

Storage Optimizer Identify potintial optimizations on the cloud storage accounts

Zaher Mousa 1 Feb 13, 2022
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features

CleanRL (Clean Implementation of RL Algorithms) CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation

Costa Huang 1.8k Jan 01, 2023
Educational API for 3D Vision using pose to control carton.

Educational API for 3D Vision using pose to control carton.

41 Jul 10, 2022
moving object detection for satellite videos.

DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos Algorithm Introduction DSFNet: Dynamic and Static Fusion Net

xiaochao 39 Dec 16, 2022
Code base for NeurIPS 2021 publication titled Kernel Functional Optimisation (KFO)

KernelFunctionalOptimisation Code base for NeurIPS 2021 publication titled Kernel Functional Optimisation (KFO) We have conducted all our experiments

2 Jun 29, 2022
Pomodoro timer that acknowledges the inexorable, infinite passage of time

Pomodouroboros Most pomodoro trackers assume you're going to start them. But time and tide wait for no one - the great pomodoro of the cosmos is cold

Glyph 66 Dec 13, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

Context-Aware-Healthcare Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs Download

LuChang 9 Dec 26, 2022
A fast implementation of bss_eval metrics for blind source separation

fast_bss_eval Do you have a zillion BSS audio files to process and it is taking days ? Is your simulation never ending ? Fear no more! fast_bss_eval i

Robin Scheibler 99 Dec 13, 2022
Tech Resources for Academic Communities

Free tech resources for faculty, students, researchers, life-long learners, and academic community builders for use in tech based courses, workshops, and hackathons.

Microsoft 2.5k Jan 04, 2023
This repository is an implementation of paper : Improving the Training of Graph Neural Networks with Consistency Regularization

CRGNN Paper : Improving the Training of Graph Neural Networks with Consistency Regularization Environments Implementing environment: GeForce RTX™ 3090

THUDM 28 Dec 09, 2022
Akshat Surolia 2 May 11, 2022
Pipeline for employing a Lightweight deep learning models for LOW-power systems

PL-LOW A high-performance deep learning model lightweight pipeline that gradually lightens deep neural networks in order to utilize high-performance d

POSTECH Data Intelligence Lab 9 Aug 13, 2022
This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

Behavior-Sequence-Transformer-Pytorch This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf This model

Jaime Ferrando Huertas 83 Jan 05, 2023
Reimplementation of the paper "Attention, Learn to Solve Routing Problems!" in jax/flax.

JAX + Attention Learn To Solve Routing Problems Reinplementation of the paper Attention, Learn to Solve Routing Problems! using Jax and Flax. Fully su

Gabriela Surita 7 Dec 01, 2022
Official implementation of Rich Semantics Improve Few-Shot Learning (BMVC, 2021)

Rich Semantics Improve Few-Shot Learning Paper Link Abstract : Human learning benefits from multi-modal inputs that often appear as rich semantics (e.

Mohamed Afham 11 Jul 26, 2022
PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

Pseudo Numerical Methods for Diffusion Models on Manifolds (PNDM) This repo is the official PyTorch implementation for the paper Pseudo Numerical Meth

Luping Liu (刘路平) 196 Jan 05, 2023
Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

Active Learning for Deep Object Detection via Probabilistic Modeling This repository is the official PyTorch implementation of Active Learning for Dee

NVIDIA Research Projects 130 Jan 06, 2023
Implementation of "Deep Implicit Templates for 3D Shape Representation"

Deep Implicit Templates for 3D Shape Representation Zerong Zheng, Tao Yu, Qionghai Dai, Yebin Liu. arXiv 2020. This repository is an implementation fo

Zerong Zheng 144 Dec 07, 2022
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

Adelaide Intelligent Machines (AIM) Group 3k Jan 02, 2023