PerfFuzz: Automatically Generate Pathological Inputs for C/C++ programs

Related tags

Deep Learningperffuzz
Overview

PerfFuzz

Performance problems in software can arise unexpectedly when programs are provided with inputs that exhibit pathological behavior. But how can we find these inputs in the first place? PerfFuzz can generate such inputs automatically: given a program and at least one seed input, PerfFuzz automatically generates inputs that exercise pathological behavior across program locations, without any domain knowledge.

PerfFuzz uses multi-dimensional performance feedback and independently maximizes execution counts for all program locations. This enables PerfFuzz to find a variety of inputs that exercise distinct hot spots in a program.

Read the ISSTA paper for more details.

Built by Caroline Lemieux ([email protected]) and Rohan Padhye ([email protected]) on top of Michal Zalewski's ([email protected]) AFL.

Building PerfFuzz

To build on *nix machines, run

make

in the perffuzz directory. Since PerfFuzz is built on AFL, it will not build on Windows machines. You will also need to build PerfFuzz's instrumenting compiler, which can be done by running

cd llvm_mode
make
cd ..

in the perffuzz directory, after having built PerfFuzz.

  • Q: What version of clang should I use?

  • A: PerfFuzz was evaluated with clang-3.8.0 on Linux and works with verison 8 on Mac. To experiment with different clang/LLVM version, add the bin/ directory from the pre-build clang archives to the front of your PATH when compiling.

  • Q: I'm getting an error involving the -fno-rtti option.

  • A: If you're on Redhat Linux, this may be a gcc/clang compatibility issue. Apparently gcc-4.7 fixes the issue.

Test PerfFuzz on Insertion Sort

To check whether PerfFuzz is working correctly, try running it on the insertion sort benchmark provided. The following commands assume you are in the PerfFuzz directory.

Build

First, compile the benchmark:

./afl-clang-fast insertion-sort.c -o isort

Run PerfFuzz

Let's make some seeds for PerfFuzz to start with:

mkdir isort-seeds
head -c 64 /dev/zero > isort-seeds/zeroes

Now we can run PerfFuzz:

./afl-fuzz -p -i isort-seeds -o isort_perf_test/ -N 64 ./isort @@

You should see the number of total paths (this is a misnomer; it's just the number of saved inputs) increase consistently. You can also check to see if the saved inputs are heading towards a worst-case by running

for i in isort_perf_test/queue/id*; do ./isort $i | grep comps; done

(which, for each saved input, plots the number of comparisons insertion sort performed while sorting that input)

For comparison with the performance compared to regular afl, you can run: ./afl-fuzz -i isort-seeds -o isort_afl_test/ -N 64 ./isort @@ without the -p option, this should just run regular AFL. You should see total_paths quickly topping out around ~20 or so, and the number of cycles increase a lot. There will probably be much fewer comparisons performed for the saved inputs as well. The highest number of comparisons printed when you run:

for i in isort_afl_test/queue/id*; do ./isort $i | grep comps; done

should be smaller than what you saw for the inputs in isort_perf_test/queue.

Running PerfFuzz on a program of your choice

Compile your program with PerfFuzz

To compile your C/C++ program with perffuzz, replace CC (resp. CXX) with path/to/perffuzz/afl-clang-fast (resp. path/to/perffuzz/afl-clang-fast++) in your build process. See section (3) of README (not README.md) for more details, replacing references of path/to/afl/afl-gcc with path/to/perffuzz/afl-clang-fast.

  • Q: afl-clang-fast doesn't exist!
  • A: make sure you ran make in the llvm_mode directory (see "Building PerfFuzz")

Run PerfFuzz on your program.

In short, follow the instructions in README (regular AFL readme) section 6, but add the -p option to enable PerfFuzz, and the -N num option to restrict the size of produced inputs to a maximum file size of num. Make sure your initial seed inputs (in the input directory) are of smaller size than num bytes!

On many programs (including the benchmarks in the paper), the -d option (Fidgety mode) offers better performance.

Let PerfFuzz run for as long as you like: we ran for a few hours on larger benchmarks.

Interpret PerfFuzz results.

In the queue directory of the ouput directory, inputs postfixed with +max were saved because the maximized a performance key.

We provide some tools to help analyze the results. Notably, afl-showmax can print:

  1. The total path length (default)
  2. The maximum hotspot (-x option)
  3. The entire performance map in a key:value format (-a option)

To build afl-showmax, run

make afl-showmax

in the PerfFuzz directory.

You might also like...
This repository contains the code for the paper
This repository contains the code for the paper "Hierarchical Motion Understanding via Motion Programs"

Hierarchical Motion Understanding via Motion Programs (CVPR 2021) This repository contains the official implementation of: Hierarchical Motion Underst

TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

TensorFlowOnSpark TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from the T

A testcase generation tool for Persistent Memory Programs.

PMFuzz PMFuzz is a testcase generation tool to generate high-value tests cases for PM testing tools (XFDetector, PMDebugger, PMTest and Pmemcheck) If

Composable transformations of Python+NumPy programsComposable transformations of Python+NumPy programs

Chex Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help: Instrument your code (e.g. assertions) Debug

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

In this project, two programs can help you take full agvantage of time on the model training with a remote server

In this project, two programs can help you take full agvantage of time on the model training with a remote server, which can push notification to your phone about the information during model training, like the model indices and unexpected interrupts. Then you can do something in time for your work.

An NVDA add-on to split screen reader and audio from other programs to different sound channels

An NVDA add-on to split screen reader and audio from other programs to different sound channels (add-on idea credit: Tony Malykh)

Some simple programs built in Python: webcam with cv2 that detects eyes and face, with grayscale filter
Some simple programs built in Python: webcam with cv2 that detects eyes and face, with grayscale filter

Programas en Python Algunos programas simples creados en Python: 📹 Webcam con c

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Auto-ViML Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal" (autovimal logo created by Sanket Ghanmare) N

Comments
  • test of llvm_mode fails

    test of llvm_mode fails

    Hi,

    On a recent Arch Linux, when building llvm_mode, I'm getting:

    [email protected]:llvm_mode$ make
    [*] Checking for working 'llvm-config'...
    [*] Checking for working 'clang'...
    [*] Checking for '../afl-showmap'...
    [+] All set and ready to build.
    clang -O3 -funroll-loops -Wall -D_FORTIFY_SOURCE=2 -g -Wno-pointer-sign -DAFL_PATH=\"/usr/local/lib/afl\" -DBIN_PATH=\"/usr/local/bin\" -DVERSION=\"2.52b\"  afl-clang-fast.c -o ../afl-clang-fast 
    ln -sf afl-clang-fast ../afl-clang-fast++
    clang++ `llvm-config --cxxflags` -fno-rtti -fpic -O3 -funroll-loops -Wall -D_FORTIFY_SOURCE=2 -g -Wno-pointer-sign -DVERSION=\"2.52b\" -Wno-variadic-macros -shared afl-llvm-pass.so.cc -o ../afl-llvm-pass.so `llvm-config --ldflags` 
    clang -O3 -funroll-loops -Wall -D_FORTIFY_SOURCE=2 -g -Wno-pointer-sign -DAFL_PATH=\"/usr/local/lib/afl\" -DBIN_PATH=\"/usr/local/bin\" -DVERSION=\"2.52b\"  -fPIC -shared afl-catch-dlclose.so.c -o ../afl-catch-dlclose.so
    clang -O3 -funroll-loops -Wall -D_FORTIFY_SOURCE=2 -g -Wno-pointer-sign -DAFL_PATH=\"/usr/local/lib/afl\" -DBIN_PATH=\"/usr/local/bin\" -DVERSION=\"2.52b\"  -fPIC -c afl-llvm-rt.o.c -o ../afl-llvm-rt.o
    afl-llvm-rt.o.c:99:20: warning: incompatible pointer types assigning to 'u32 *' (aka 'unsigned int *') from 'u8 *' (aka 'unsigned char *') [-Wincompatible-pointer-types]
        __afl_perf_ptr = &__afl_area_ptr[MAP_SIZE];
                       ^ ~~~~~~~~~~~~~~~~~~~~~~~~~
    1 warning generated.
    [*] Building 32-bit variant of the runtime (-m32)... success!
    [*] Building 64-bit variant of the runtime (-m64)... success!
    [*] Testing the CC wrapper and instrumentation output...
    unset AFL_USE_ASAN AFL_USE_MSAN AFL_INST_RATIO; AFL_QUIET=1 AFL_PATH=. AFL_CC=clang ../afl-clang-fast -O3 -funroll-loops -Wall -D_FORTIFY_SOURCE=2 -g -Wno-pointer-sign -DAFL_PATH=\"/usr/local/lib/afl\" -DBIN_PATH=\"/usr/local/bin\" -DVERSION=\"2.52b\"  ../test-instr.c -o test-instr 
    echo 0 | ../afl-showmap -m none -q -o .test-instr0 ./test-instr
    echo 1 | ../afl-showmap -m none -q -o .test-instr1 ./test-instr
    
    Oops, the instrumentation does not seem to be behaving correctly!
    
    Please ping <[email protected]> to troubleshoot the issue.
    
    make: *** [Makefile:105: test_build] Error 1**
    

    It was a full normal compile, so I'm a bit confused. Is the test incorrectly set up for perffuzz and hasn't been changed/fixed?

    opened by msoos 7
  • Prioritize maximizing values with more granularity

    Prioritize maximizing values with more granularity

    Some values in the key: value map may be more worth increasing than others (either more interesteing, or others may just not increase). Two ideas:

    1. Favour based on the key achieving maximum value (similar to afl-rb's minimizing branch hits)
    2. Favour based on whether value is actually increasing.
    opened by carolemieux 3
  • What is Perf_Mask in the instrumentation pass?

    What is Perf_Mask in the instrumentation pass?

    Hey, I am trying to do some thing new on PerfFuzz. But there is one thing in the code I am confused.

    What is the purpose of this Perf_Mask? https://github.com/carolemieux/perffuzz/blob/f937f370555d0c54f2109e3b1aa5763f8defe337/llvm_mode/afl-llvm-pass.so.cc#L129

    I don't think it is correct to add Perf_Mask to Edge_Id to create a GEP instruction in PerfBranchPtr https://github.com/carolemieux/perffuzz/blob/f937f370555d0c54f2109e3b1aa5763f8defe337/llvm_mode/afl-llvm-pass.so.cc#L176 https://github.com/carolemieux/perffuzz/blob/f937f370555d0c54f2109e3b1aa5763f8defe337/llvm_mode/afl-llvm-pass.so.cc#L177

    However, EdgeId % PERF_SIZE is acctually needed to index the perf map.

    Looking forward to your reply, thanks.

    opened by zhanggenex 1
  • Rename staleness

    Rename staleness

    Find a new name for staleness which is either (1) more intuitive or (2) involves the use of the word "gradient".

    Suggestions What we currently use as staleness is really the inverse of what all these things could be...

    • magnitude-agnostic gradient
    • increase gradient
    • binary gradient
    opened by carolemieux 0
Releases(1.0)
Owner
Caroline Lemieux
Caroline Lemieux
Self-Supervised depth kalilia

Self-Supervised depth kalilia

24 Oct 15, 2022
My freqtrade strategies

My freqtrade-strategies Hi there! This is repo for my freqtrade-strategies. My name is Ilya Zelenchuk, I'm a lecturer at the SPbU university (https://

171 Dec 05, 2022
Machine Learning toolbox for Humans

Reproducible Experiment Platform (REP) REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main

Yandex 662 Nov 20, 2022
PyTorch code for MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning PyTorch code for our ACL 2020 paper "MART: Memory-Augmented Recur

Jie Lei 雷杰 151 Jan 06, 2023
Official Code for VideoLT: Large-scale Long-tailed Video Recognition (ICCV 2021)

Pytorch Code for VideoLT [Website][Paper] Updates [10/29/2021] Features uploaded to Google Drive, for access please send us an e-mail: zhangxing18 at

Skye 26 Sep 18, 2022
Evolutionary Scale Modeling (esm): Pretrained language models for proteins

Evolutionary Scale Modeling This repository contains code and pre-trained weights for Transformer protein language models from Facebook AI Research, i

Meta Research 1.6k Jan 09, 2023
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (arXiv2021)

Polyp-PVT by Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, & Ling Shao. This repo is the official implementation of "Polyp-PVT: Polyp Se

Deng-Ping Fan 102 Jan 05, 2023
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness This repository contains the code used for the exper

H.R. Oosterhuis 28 Nov 29, 2022
UFT - Universal File Transfer With Python

UFT 2.0.0 UFT (Universal File Transfer) is a CLI tool , which can be used to upl

Merwin 1 Feb 18, 2022
Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

Enformer - Pytorch (wip) Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch. The original tensorflow

Phil Wang 235 Dec 27, 2022
Multi-Glimpse Network With Python

Multi-Glimpse Network Multi-Glimpse Network: A Robust and Efficient Classification Architecture based on Recurrent Downsampled Attention arXiv Require

9 May 10, 2022
Make your own game in a font!

Project structure. Included is a suite of tools to create font games. Tutorial: For a quick tutorial about how to make your own game go here For devel

Michael Mulet 125 Dec 04, 2022
Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality".

personalized-breath Repo for the ACMMM20 submission: "Personalized breath based biometric authentication with wearable multimodality". Guideline To ex

Manh-Ha Bui 2 Nov 15, 2021
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
PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Representation

How to Reproduce our Results This repository contains PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Represen

opcrisis 46 Dec 15, 2022
BboxToolkit is a tiny library of special bounding boxes.

BboxToolkit is a light codebase collecting some practical functions for the special-shape detection, such as oriented detection

jbwang1997 73 Jan 01, 2023
Original Implementation of Prompt Tuning from Lester, et al, 2021

Prompt Tuning This is the code to reproduce the experiments from the EMNLP 2021 paper "The Power of Scale for Parameter-Efficient Prompt Tuning" (Lest

Google Research 282 Dec 28, 2022
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN in PyTorch PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in READM

Taehoon Kim 1k Jan 04, 2023
Manifold-Mixup implementation for fastai V2

Manifold Mixup Unofficial implementation of ManifoldMixup (Proceedings of ICML 19) for fast.ai (V2) based on Shivam Saboo's pytorch implementation of

Nestor Demeure 16 Jul 25, 2022
PyTorch implementation of Munchausen Reinforcement Learning based on DQN and SAC. Handles discrete and continuous action spaces

Exploring Munchausen Reinforcement Learning This is the project repository of my team in the "Advanced Deep Learning for Robotics" course at TUM. Our

Mohamed Amine Ketata 10 Mar 10, 2022