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
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework This repository contains a framework for Recommender Systems (RecSys), a

RecSys Lab 8 Jul 03, 2022
Implementation of SSMF: Shifting Seasonal Matrix Factorization

SSMF Implementation of SSMF: Shifting Seasonal Matrix Factorization, Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. NeurIPS, 2021

Koki Kawabata 9 Jun 10, 2022
Image-generation-baseline - MUGE Text To Image Generation Baseline

MUGE Text To Image Generation Baseline Requirements and Installation More detail

23 Oct 17, 2022
Source code of our work: "Benchmarking Deep Models for Salient Object Detection"

SALOD Source code of our work: "Benchmarking Deep Models for Salient Object Detection". In this works, we propose a new benchmark for SALient Object D

22 Dec 30, 2022
FFCV: Fast Forward Computer Vision (and other ML workloads!)

Fast Forward Computer Vision: train models at a fraction of the cost with accele

FFCV 2.3k Jan 03, 2023
Key information extraction from invoice document with Graph Convolution Network

Key Information Extraction from Scanned Invoices Key information extraction from invoice document with Graph Convolution Network Related blog post fro

Phan Hoang 39 Dec 16, 2022
Code release for SLIP Self-supervision meets Language-Image Pre-training

SLIP: Self-supervision meets Language-Image Pre-training What you can find in this repo: Pre-trained models (with ViT-Small, Base, Large) and code to

Meta Research 621 Dec 31, 2022
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

ClusterGCN ⠀⠀ A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019). A

Benedek Rozemberczki 697 Dec 27, 2022
Simple tutorials on Pytorch DDP training

pytorch-distributed-training Distribute Dataparallel (DDP) Training on Pytorch Features Easy to study DDP training You can directly copy this code for

Ren Tianhe 188 Jan 06, 2023
Earthquake detection via fiber optic cables using deep learning

Earthquake detection via fiber optic cables using deep learning Author: Fantine Huot Getting started Update the submodules After cloning the repositor

Fantine 4 Nov 30, 2022
A demonstration of using a live Tensorflow session to create an interactive face-GAN explorer.

Streamlit Demo: The Controllable GAN Face Generator This project highlights Streamlit's new hash_func feature with an app that calls on TensorFlow to

Streamlit 257 Dec 31, 2022
Omnidirectional camera calibration in python

Omnidirectional Camera Calibration Key features pure python initial solution based on A Toolbox for Easily Calibrating Omnidirectional Cameras (Davide

Thomas Pönitz 12 Nov 22, 2022
PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

76 Jan 03, 2023
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images (ISBI 2021, MELBA 2021)

MultiMix This repository contains the implementation of MultiMix. Our publications for this project are listed below: "MultiMix: Sparingly Supervised,

Ayaan Haque 27 Dec 22, 2022
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh

Akshita Gupta 127 Dec 27, 2022
MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

Octave Convolution MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution Imag

Meta Research 549 Dec 28, 2022
Adaptive Attention Span for Reinforcement Learning

Adaptive Transformers in RL Official implementation of Adaptive Transformers in RL In this work we replicate several results from Stabilizing Transfor

100 Nov 15, 2022
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems

The SLIDE package contains the source code for reproducing the main experiments in this paper. Dataset The Datasets can be downloaded in Amazon-

Intel Labs 72 Dec 16, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
OpenGAN: Open-Set Recognition via Open Data Generation

OpenGAN: Open-Set Recognition via Open Data Generation ICCV 2021 (oral) Real-world machine learning systems need to analyze novel testing data that di

Shu Kong 90 Jan 06, 2023