MEDS: Enhancing Memory Error Detection for Large-Scale Applications

Related tags

Deep LearningMEDS
Overview

MEDS: Enhancing Memory Error Detection for Large-Scale Applications

Prerequisites

  • cmake and clang

Build MEDS supporting compiler

$ make

Build Using Docker

# build docker image
$ docker build -t meds .

# run docker image
$ docker run --cap-add=SYS_PTRACE -it meds /bin/bash

Testing MEDS

  • MEDS's testing runs original ASAN's testcases as well as MEDS specific testcases.

    • Copied ASAN's testcases in llvm/projects/compiler-rt/test/meds/TestCases/ASan
    • MEDS specific testcases in llvm/projects/compiler-rt/test/meds/TestCases/Meds

  • To run the test,

$ make test

Testing Time: 30.70s
 Expected Passes    : 183
 Expected Failures  : 1
 Unsupported Tests  : 50

Build applications with MEDS heap allocation and ASan stack and global

  • Given a test program test.cc,
$ cat > test.cc

int main(int argc, char **argv) {
  int *a = new int[10];
  a[argc * 10] = 1;
  return 0;
}
  • test.cc can be built using the option, -fsanitize=meds.
$ build/bin/clang++ -fsanitize=meds test.cc -o test
$ ./test

==90589==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x43fff67eb078 at pc 0x0000004f926d bp 0x7fffffffe440 sp 0x7fffffffe438
WRITE of size 4 at 0x43fff67eb078 thread T0
    #0 0x4f926c in main (/home/wookhyun/release/meds-release/a.out+0x4f926c)
    #1 0x7ffff6b5c82f in __libc_start_main /build/glibc-bfm8X4/glibc-2.23/csu/../csu/libc-start.c:291
    #2 0x419cb8 in _start (/home/wookhyun/release/meds-release/a.out+0x419cb8)

Address 0x43fff67eb078 is a wild pointer.
SUMMARY: AddressSanitizer: heap-buffer-overflow (/home/wookhyun/release/meds-release/a.out+0x4f926c) in main
Shadow bytes around the buggy address:
  0x08807ecf55b0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x08807ecf55c0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x08807ecf55d0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x08807ecf55e0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
  0x08807ecf55f0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
=>0x08807ecf5600: fa fa fa fa fa fa fa fa fa fa 00 00 00 00 00[fa]
  0x08807ecf5610: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
  0x08807ecf5620: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
  0x08807ecf5630: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
  0x08807ecf5640: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
  0x08807ecf5650: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
  Addressable:           00
  Partially addressable: 01 02 03 04 05 06 07
  Heap left redzone:       fa
  Freed heap region:       fd
  Stack left redzone:      f1
  Stack mid redzone:       f2
  Stack right redzone:     f3
  Stack after return:      f5
  Stack use after scope:   f8
  Global redzone:          f9
  Global init order:       f6
  Poisoned by user:        f7
  Container overflow:      fc
  Array cookie:            ac
  Intra object redzone:    bb
  ASan internal:           fe
  Left alloca redzone:     ca
  Right alloca redzone:    cb
==90589==ABORTING

Options

  • -fsanitize=meds: Enable heap protection using MEDS (stack and global are protected using ASAN)

  • -mllvm -meds-stack=1: Enable stack protection using MEDS

  • -mllvm -meds-global=1 -mcmodel=large: Enable global protection using MEDS

    • This also requires --emit-relocs in LDFLAGS

  • Example: to protect heap/stack using MEDS and global using ASAN

$ clang -fsanitize=meds -mllvm -meds-stack=1 test.c -o test
  • Example: to protect heap/global using MEDS and stack using ASAN
$ clang -fsanitize=meds -mllvm -meds-global=1 -mcmodel=large -Wl,-emit-relocs test.c -o test
  • Example: to protect heap/stack/global using MEDS
$ clang -fsanitize=meds -mllvm -meds-stack=1 -mllvm -meds-global=1 -mcmodel=large -Wl,--emit-relocs

Contributors

Owner
Secomp Lab at Purdue University
Secomp Lab at Purdue University
GitHub Repository
Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection PyTorch code release of the paper "Attentive Prototypes for Sour

Deepti Hegde 23 Oct 17, 2022
NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset

NOD (Night Object Detection) Dataset NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset, BM

Igor Morawski 17 Nov 05, 2022
Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation: Work In Progress, Results can't be replicated yet with the m

Yad Konrad 196 Aug 30, 2022
QuanTaichi evaluation suite

QuanTaichi: A Compiler for Quantized Simulations (SIGGRAPH 2021) Yuanming Hu, Jiafeng Liu, Xuanda Yang, Mingkuan Xu, Ye Kuang, Weiwei Xu, Qiang Dai, W

Taichi Developers 120 Jan 04, 2023
Posterior temperature optimized Bayesian models for inverse problems in medical imaging

Posterior temperature optimized Bayesian models for inverse problems in medical imaging Max-Heinrich Laves*, Malte Tölle*, Alexander Schlaefer, Sandy

Artificial Intelligence in Cardiovascular Medicine (AICM) 6 Sep 19, 2022
FasterAI: A library to make smaller and faster models with FastAI.

Fasterai fasterai is a library created to make neural network smaller and faster. It essentially relies on common compression techniques for networks

Nathan Hubens 193 Jan 01, 2023
This repository contains implementations of all Machine Learning Algorithms from scratch in Python. Mathematics required for ML and many projects have also been included.

👏 Pre- requisites to Machine Learning

Vanshika Mishra 147 Jan 07, 2023
Experiments for Fake News explainability project

fake-news-explainability Experiments for fake news explainability project This repository only contains the notebooks used to train the models and eva

Lorenzo Flores (Lj) 1 Dec 03, 2022
Measure WWjj polarization fraction

WlWl Polarization Measure WWjj polarization fraction Paper: arXiv:2109.09924 Notice: This code can only be used for the inference process, if you want

4 Apr 10, 2022
🛠 All-in-one web-based IDE specialized for machine learning and data science.

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

Machine Learning Tooling 2.9k Jan 09, 2023
(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning

Realistic evaluation of transductive few-shot learning Introduction This repo contains the code for our NeurIPS 2021 submitted paper "Realistic evalua

Olivier Veilleux 14 Dec 13, 2022
We will release the code of "ConTNet: Why not use convolution and transformer at the same time?" in this repo

ConTNet Introduction ConTNet (Convlution-Tranformer Network) is proposed mainly in response to the following two issues: (1) ConvNets lack a large rec

93 Nov 08, 2022
Official Implementation of LARGE: Latent-Based Regression through GAN Semantics

LARGE: Latent-Based Regression through GAN Semantics [Project Website] [Google Colab] [Paper] LARGE: Latent-Based Regression through GAN Semantics Yot

83 Dec 06, 2022
Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB)

Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB) This repository provides evaluation codes of PLNLP for OGB link property prediction t

Zhitao WANG 31 Oct 10, 2022
PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation This is the PyTorch implemention of ICCV'21 paper SGPA: Structure

Chen Kai 24 Dec 05, 2022
pytorch, hand(object) detect ,yolo v5，手检测

YOLO V5 物体检测，包括手部检测。 项目介绍 手部检测 手部检测示例如下 ： 视频示例： 项目配置 作者开发环境： Python 3.7 PyTorch = 1.5.1 数据集 手部检测数据集 该项目数据集采用 TV-Hand 和 COCO-Hand (COCO-Hand-Big 部分) 进

Eric.Lee 11 Dec 20, 2022
PyTorch implementation and pretrained models for XCiT models. See XCiT: Cross-Covariance Image Transformer

Official code Cross-Covariance Image Transformer (XCiT)

Facebook Research 605 Jan 02, 2023
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (NeurIPS 2020) Introduction AdaShare is a novel and differentiable approach fo

94 Dec 22, 2022
Improving Compound Activity Classification via Deep Transfer and Representation Learning

Improving Compound Activity Classification via Deep Transfer and Representation Learning This repository is the official implementation of Improving C

NingLab 2 Nov 24, 2021
Accelerated deep learning R&D

Accelerated deep learning R&D PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and

Catalyst-Team 3.1k Jan 06, 2023