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LAVA: Large Scale Automated Vulnerability Addition

Evaluating and improving bug-finding tools is currently difficult due to a shortage of ground truth corpora (i.e., software that has known bugs with triggering inputs). LAVA attempts to solve this problem by automatically injecting bugs into software. Every LAVA bug is accompanied by an input that triggers it whereas normal inputs are extremely unlikely to do so. These vulnerabilities are synthetic but, we argue, still realistic, in the sense that they are embedded deep within programs and are triggered by real inputs. Our work forms the basis of an approach for generating large ground-truth vulnerability corpora on demand, enabling rigorous tool evaluation and providing a high-quality target for tool developers.

LAVA is the product of a collaboration between MIT Lincoln Laboratory, NYU, and Northeastern University.

Quick Start

On a system running Ubuntu 16.04, with the appropriate dependencies installed (see docs/setup.md for details), you should be able to just run python2 setup.py. Note that this install script will install packages and make changes to your system. Once it finishes, you should have PANDA installed into panda/build/ (PANDA is used to perform dynamic taint analysis).

Next, run init-host.py to generate a host.json. This file is used by LAVA to store settings specific to your machine. You can edit these settings as necessary, but the default values should work.

Project configurations are located in the target_configs directory, where every configuration is located at target_configs/projectname/projectname.json. Paths specified within these configuration files are relative to values set in your host.json file.

Finally, you can run ./scripts/lava.sh to actually inject bugs into a program. Just provide the name of a project that is in the target_configs directory, for example:

./scripts/lava.sh toy

You should now have a buggy copy of toy!

If you want to inject bugs into a new target, you will likely need to make some modifications. Check out How-to-Lava for guidance.

Documentation

Check out the docs folder to get started.

Current Status

Version 2.0.0

Expected results from test suite:

Project       RESET    CLEAN    ADD      MAKE     TAINT    INJECT   COMP
blecho        PASS     PASS     PASS     PASS     PASS     PASS     PASS
libyaml       PASS     PASS     PASS     PASS     PASS     PASS     PASS
file          PASS     PASS     PASS     PASS     PASS     PASS     PASS
toy           PASS     PASS     PASS     PASS     PASS     PASS     PASS
pcre2         PASS     PASS     PASS     PASS     PASS     PASS     PASS
jq            PASS     PASS     PASS     PASS     PASS     PASS     PASS
grep          PASS     PASS     PASS     PASS     PASS     FAIL
libjpeg       PASS     PASS     PASS     PASS     FAIL
tinyexpr      PASS     PASS     PASS     PASS     FAIL
duktape       PASS     PASS     PASS     FAIL
tweetNaCl     PASS     PASS     FAIL
gzip          FAIL

Authors

LAVA is the result of several years of development by many people; a partial (alphabetical) list of contributors is below:

  • Andy Davis
  • Brendan Dolan-Gavitt
  • Andrew Fasano
  • Zhenghao Hu
  • Patrick Hulin
  • Amy Jiang
  • Engin Kirda
  • Tim Leek
  • Andrea Mambretti
  • Wil Robertson
  • Aaron Sedlacek
  • Rahul Sridhar
  • Frederick Ulrich
  • Ryan Whelan