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
Server files for UltimateLabeling

UltimateLabeling server files Server files for UltimateLabeling. git clone https://github.com/alexandre01/UltimateLabeling_server.git cd UltimateLabel

Alexandre Carlier 4 Oct 10, 2022
Attention Probe: Vision Transformer Distillation in the Wild

Attention Probe: Vision Transformer Distillation in the Wild Jiahao Wang, Mingdeng Cao, Shuwei Shi, Baoyuan Wu, Yujiu Yang In ICASSP 2022 This code is

Wang jiahao 3 Oct 31, 2022
The project covers common metrics for super-resolution performance evaluation.

Super-Resolution Performance Evaluation Code The project covers common metrics for super-resolution performance evaluation. Metrics support The script

xmy 10 Aug 03, 2022
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting This is the origin Pytorch implementation of Informer in the followin

Haoyi 3.1k Dec 29, 2022
[CVPR2021 Oral] FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation.

FFB6D This is the official source code for the CVPR2021 Oral work, FFB6D: A Full Flow Biderectional Fusion Network for 6D Pose Estimation. (Arxiv) Tab

Yisheng (Ethan) He 201 Dec 28, 2022
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai

Coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks an

Aman Chadha 1.7k Jan 08, 2023
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

BasicVSR BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond Ported from https://github.com/xinntao/BasicSR Dependencie

Holy Wu 8 Jun 07, 2022
TensorFlow implementation of the paper "Hierarchical Attention Networks for Document Classification"

Hierarchical Attention Networks for Document Classification This is an implementation of the paper Hierarchical Attention Networks for Document Classi

Quoc-Tuan Truong 83 Dec 05, 2022
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation Prerequisites This repo is built upon a local copy of transfo

Jixuan Wang 10 Sep 28, 2022
VGG16 model-based classification project about brain tumor detection.

Brain-Tumor-Classification-with-MRI VGG16 model-based classification project about brain tumor detection. First, you can check what people are doing o

Atakan Erdoğan 2 Mar 21, 2022
Parameter-ensemble-differential-evolution - Shows how to do parameter ensembling using differential evolution.

Ensembling parameters with differential evolution This repository shows how to ensemble parameters of two trained neural networks using differential e

Sayak Paul 9 May 04, 2022
RealFormer-Pytorch Implementation of RealFormer using pytorch

RealFormer-Pytorch Implementation of RealFormer using pytorch. Includes comparison with classical Transformer on image classification task (ViT) wrt C

Simo Ryu 90 Dec 08, 2022
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

61 Jan 07, 2023
ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

AliceMind AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab This repository provides pre-trained encode

Alibaba 1.4k Jan 01, 2023
Bolt Online Learning Toolbox

Bolt Online Learning Toolbox Bolt features discriminative learning of linear predictors (e.g. SVM or Logistic Regression) using fast online learning a

Peter Prettenhofer 87 Dec 12, 2022
Continual Learning of Electronic Health Records (EHR).

Continual Learning of Longitudinal Health Records Repo for reproducing the experiments in Continual Learning of Longitudinal Health Records (2021). Re

Jacob 7 Oct 21, 2022
DIRL: Domain-Invariant Representation Learning

DIRL: Domain-Invariant Representation Learning Domain-Invariant Representation Learning (DIRL) is a novel algorithm that semantically aligns both the

Ajay Tanwani 30 Nov 07, 2022
This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust.

Demo BERT ONNX pipeline written in rust This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust. R

Xavier Tao 14 Dec 17, 2022
Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation

NorCal Normalization Calibration (NorCal) for Long-Tailed Object Detection and Instance Segmentation On Model Calibration for Long-Tailed Object Detec

Tai-Yu (Daniel) Pan 24 Dec 25, 2022
Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks

OnsagerNet Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks This is the original pyTorch implemenati

Haijun.Yu 3 Aug 24, 2022