AntiFuzz: Impeding Fuzzing Audits of Binary Executables

Related tags

Deep Learningantifuzz
Overview

AntiFuzz: Impeding Fuzzing Audits of Binary Executables

Get the paper here: https://www.usenix.org/system/files/sec19-guler.pdf

Usage:

The python script antifuzz_generate.py generates a "antifuzz.h" file that you need to include in your C project (see chapter below). The script takes multiple arguments to define which features you want to activate.

To disable all features, supply:

  --disable-all

To break assumption (A), i.e. to break coverage-guided fuzzing, use:

  --enable-anti-coverage

You can specify how many random BBs and random constrain functions you want to have by supplying "--anti-coverage [num]" (default: 10000).

To break assumption (B), i.e. to prevent fuzzers from detecting crashes, use:

  --signal --crash-action exit

To break assumption (C), i.e. to decrease the performance of the application when being fuzzed, use:

  --enable-sleep --signal

Additionaly, you can supply "--sleep [ms]" to set the length of the sleep in milliseconds (default: 750). You can also replace the crash behavior by supplying "--crash-action timeout" to replace every crash with a timeout.

To break assumption (D), i.e. to boggle down symbolic execution engines, use:

  --hash-cmp --enable-encrypt-decrypt

To enable all features, use:

  --enable-anti-coverage --signal --crash-action exit --enable-sleep --signal --hash-cmp --enable-encrypt-decrypt

Demo

To test it out, we supplied a demo application called antifuzz_test.c that just checks for "crsh" with single byte comparisons, and crashes if that's the case. It configures itself to fit the generated antifuzz header file, i.e. when hash comparisons are demanded via antifuzz_generate.py, antifuzz_test will compare the hashes instead of the plain constants.

First, generate the antifuzz.h file:

python antifuzz_generate.py --enable-anti-coverage --signal --crash-action exit --enable-sleep --signal --hash-cmp --enable-encrypt-decrypt

Next, compile the demo application with afl-gcc after installing AFL 2.52b (note that this may take minutes (!) depending on the number of random BBs added):

afl-gcc antifuzz_test.c -o antifuzz_test 

Run it in AFL to test it out:

mkdir inp; echo 1234 > inp/a.txt; afl-fuzz -i inp/ -o /dev/shm/out -- ./antifuzz_test @@

If you enabled all options, AFL may take a long time to start because the application is slowed down (to break assumption (C))

Protecting Applications

To include it in your own C project, follow these instructions (depending on your use-case and application, you might want to skip some of them):

1.

Add

#include "antifuzz.h"

to the header.

2.

Jump to the line that opens the (main) input file, the one that an attacker might target as an attack vector, and call

antifuzz_init("file_name_here", FLAG_ALL); 

This initializes AntiFuzz, checks if overwriting signals is possible, checks if the application is ptrace'd, puts the input through encryption and decryption, jumps through random BBs, etc.

3.

Find all lines and blocks of code that deal with malformed input files or introduce those yourself. It's often the case that these lines already exist to print some kind of error or warning message (e.g. "this is not a valid ... file"). Add a call to

antifuzz_onerror()

everywhere you deem appropriate.

4.

Find comparisons to constants (e.g. magic bytes) that you think are important for this file format, and change the comparison to hash comparisons. Add your constant to antifuzz_constants.tpl.h like this:

char *antifuzzELF = "ELF";

Our generator script will automatically change these lines to their respective SHA512 hashes when generating the final header file, you do not have to do this manually. Now change the lines from (as an example):

if(strcmp(header, "ELF") == 0)

to

if(antifuzz_str_equal(header, antifuzzELF))

See antifuzz.tpl.h for more comparison functions.

5.

If you have more data that you want to protect from symbolic execution, use:

antifuzz_encrypt_decrypt_buf(char *ptr, size_t fileSize) 
Owner
Chair for Sys­tems Se­cu­ri­ty
Chair for Sys­tems Se­cu­ri­ty
Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework"

Privacy-Aware Inverse RL (PRIL) Analysis Framework Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based

1 Dec 06, 2021
TagLab: an image segmentation tool oriented to marine data analysis

TagLab: an image segmentation tool oriented to marine data analysis TagLab was created to support the activity of annotation and extraction of statist

Visual Computing Lab - ISTI - CNR 49 Dec 29, 2022
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

Vikash Sehwag 65 Dec 19, 2022
On-device wake word detection powered by deep learning.

Porcupine Made in Vancouver, Canada by Picovoice Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening

Picovoice 2.8k Dec 29, 2022
a dnn ai project to classify which food people are eating on audio recordings

Deep Learning - EAT Challenge About This project is part of an AI challenge of the DeepLearning course 2021 at the University of Augsburg. The objecti

Marco Tröster 1 Oct 24, 2021
Pixel Consensus Voting for Panoptic Segmentation (CVPR 2020)

Implementation for Pixel Consensus Voting (CVPR 2020). This codebase contains the essential ingredients of PCV, including various spatial discretizati

Haochen 23 Oct 25, 2022
Real-time multi-object tracker using YOLO v5 and deep sort

This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algor

Mike 3.6k Jan 05, 2023
From the basics to slightly more interesting applications of Tensorflow

TensorFlow Tutorials You can find python source code under the python directory, and associated notebooks under notebooks. Source code Description 1 b

Parag K Mital 5.6k Jan 09, 2023
Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

cosFormer Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention Update log 2022/2/28 Add core code License This

120 Dec 15, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
A Machine Teaching Framework for Scalable Recognition

MEMORABLE This repository contains the source code accompanying our ICCV 2021 paper. A Machine Teaching Framework for Scalable Recognition Pei Wang, N

2 Dec 08, 2021
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

RAVE: Realtime Audio Variational autoEncoder Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthes

ACIDS 587 Jan 01, 2023
Code accompanying the paper "Knowledge Base Completion Meets Transfer Learning"

Knowledge Base Completion Meets Transfer Learning This code accompanies the paper Knowledge Base Completion Meets Transfer Learning published at EMNLP

14 Nov 27, 2022
A collection of random and hastily hacked together scripts for investigating EU-DCC

A collection of random and hastily hacked together scripts for investigating EU-DCC

Ryan Barrett 8 Mar 01, 2022
[CVPR2022] Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos

Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos Created by Muheng Li, Lei Chen, Yueqi Duan, Zhilan Hu, Jianjiang Feng, Jie

58 Dec 23, 2022
Hydra Lightning Template for Structured Configs

Hydra Lightning Template for Structured Configs Template for creating projects with pytorch-lightning and hydra. How to use this template? Create your

Model-driven Machine Learning 4 Jul 19, 2022
Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".

AgentFormer This repo contains the official implementation of our paper: AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecast

Ye Yuan 161 Dec 23, 2022
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution This is the official implementation code of the paper "CondLaneNe

Alibaba Cloud 311 Dec 30, 2022
This is a Deep Leaning API for classifying emotions from human face and human audios.

Emotion AI This is a Deep Leaning API for classifying emotions from human face and human audios. Starting the server To start the server first you nee

crispengari 5 Oct 02, 2022
Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks

Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks This is the official code for DyReg model inroduced in Discovering Dyna

Bitdefender Machine Learning 11 Nov 08, 2022