A tool to predict vulnerability discovery of binary only programs
Python

README.md

VDiscover

VDiscover is a tool designed to train a vulnerability detection predictor. Given a vulnerability discovery procedure and a large enough number of training testcases, it extracts lightweight features to predict which testcases are potentially vulnerable. This repository contains an improved version of a proof-of-concept used to show experimental results in our technical report (available here).

Use cases

VDiscover aims to be used when there is a large amount of testcases to analyze using a costly vulnerability detection procedure. It can be trained to provide a quick prioritization of testcases. The extraction of features to perform a prediction is designed to be scalable. Nevertheless, this implementation is not particularly optimized so it should easy to improve the performance of it.

Requirements

Trace extraction is working only in x86 (x86_64 support should be simple to extend and it is planned)

Quickstart

Before starting, it is recommended to manually install binutils, scikit-learn and setuptools (to perform a local installation). For instance, in Ubuntu/Debian:

# apt-get install python-numpy python-matplotlib python-setup python-scipy

Then we can execute:

git clone https://github.com/CIFASIS/VDiscover.git
cd VDiscover
python setup.py install --user

By default, the local installation of the command line utilities of VDiscover is performed inside ~/.local/bin, so it is recommended to add this directory into the PATH variable. Our tool is composed by two main components:

  • fextractor: to extract dynamic and static features from test cases.
  • vpredictor: to train a new vulnerability prediction model or predict using a previously trained one. It can be used to cluster and visualize a set of test cases.

Some examples of testcases of very popular programs (grep, gzip, bc, ..) can be found in examples/testcases. For example, to extract raw dynamic features from an execution of bc:

fextractor --dynamic bc 

And the resulted extracted features are:

/usr/bin/bc isatty:0=Num32B0 isatty:0=Num32B8 setvbuf:0=Ptr32 setvbuf:1=NPtr32 setvbuf:2=Num32B8 setvbuf:3=Num32B0 ...

This raw data can be used to train a new vulnerability prediction model or predict using a previously trained one. Additionally, more detailed (but outdated) documentation is available here.

License

GPL3