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

Docker

The latest version of LAVA's master branch is automatically built as a docker images based on Ubuntu 22.04 and published to Docker Hub. Most users will want to use the lava container which has PANDA and LAVA installed along with their runtime dependencies, but no build artifacts or source code to reduce the size of the container.

To use the lava container you can pull it from Docker Hub:

$ docker pull pandare/lava

Or build from this repository:

$ DOCKER_BUILDKIT=1 docker build -t pandare/lava:local .

If Docker Engine runs directly inside WSL and build steps cannot access the network (for example, APT reports Temporary failure resolving), try using WSL's host network for the build:

DOCKER_BUILDKIT=1 docker build --network=host -t pandare/lava:local .

This applies to build steps only; containers launched later use their own network settings.

Container workspace

From a workspace containing target_bins/ and target_configs/, use the launcher to open a shell or run a command. It mounts the workspace at the same absolute path so paths written to host.json remain valid between runs. Replace /path/to/lava with the checkout containing the launcher:

/path/to/lava/scripts/docker-shell.sh
/path/to/lava/scripts/docker-shell.sh -- lava-init
/path/to/lava/scripts/docker-shell.sh -- lava --help

Docker runs as your UID/GID and stores its writable home in .lava-container-home/ within the workspace. If your host ~/.panda/ exists, the launcher mounts it as the container's PANDA cache so existing QCOWs and kernel files are reused. Downloads and cache changes persist in that host directory. Use --panda-cache /path/to/cache to select another existing cache. Without a host cache, PANDA stores downloads in .lava-container-home/.panda/. The launcher uses pandare/lava:latest by default; use --image pandare/lava:v3.3.4 to select a release, or --image pandare/lava:local for the local build above.

PostgreSQL must already be running. Export POSTGRES_USER and POSTGRES_PASSWORD, to the database endpoint reachable from inside the container. For a database on a Docker network, pass --network NETWORK and use its container name as host. localhost inside Docker refers to the LAVA container itself. Once configured:

/path/to/lava/scripts/docker-shell.sh --network NETWORK -- lava -ak toy

-k deletes existing project data without confirmation. The launcher replaces the old project-name-first interface; commands now follow --.

HPC with Apptainer / Singularity

On an allocated compute node, use the runtime provided by the cluster. Pull a versioned image once into persistent storage, then reuse it for jobs:

apptainer pull /path/to/scratch/lava-v3.3.4.sif docker://pandare/lava:v3.3.4
/path/to/lava/scripts/docker-shell.sh --runtime apptainer \
  --image /path/to/scratch/lava-v3.3.4.sif -- lava-init
/path/to/lava/scripts/docker-shell.sh --runtime apptainer \
  --image /path/to/scratch/lava-v3.3.4.sif -- lava -tc toy

Use --runtime singularity if that is the installed command. These runtimes use the host user's identity and network; configure host.json for a reachable PostgreSQL service and export the same credentials. The launcher neither starts the database nor requests a scheduler allocation. Run it inside your cluster's interactive allocation or batch script. This does not establish that a release image supports the complete pipeline; verify the image installation and run a toy project before submitting a large mining job.

Ubuntu, Debian

Local Installation

On a system running Ubuntu 22.04, you should be able to just run bash install.sh. Note that this install script will install packages and make changes to your system. You can remove the binaries using sudo apt-get remove lava.

Once you finish installing the binary, then you can install locally running pip install ..

NOTE that the Python package requires a SQL file generated from compiling the binaries that is placed into python/src/pyroclastic/data/lava.sql. Without this file, the Python package will not work correctly.

Regular installation

Alternatively, you can manually install LAVA's dependencies and then build from source. Download the Debian packages located in the releases. Then install the python package pip install pyroclastic.

Final steps

Utilizing host.json

Next, run init-host to generate a host.json in your current working directory directory. 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, see vars.py.

A few values to keep in mind are the following:

• host is the name of the Postgres SQL database with all the LAVA bugs. Currently, it defaults to database, although if you installed LAVA locally, you likely should change this to localhost

NOTE: You also need two environment variables for the Postgres SQL database:

• POSTGRES_PASS This is the password for the Postgres SQL user`
• POSTGRES_USER This is the hostname for the Postgres SQL database

Project configurations

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.

Setting up postgres SQL database

As alluded to, you should create a Postgres SQL user. You can use a script to using the environment variables for the following:

• Create the user with provided username and password from environment variables.
• Update Postgres SQL database on host to accept traffic from external sources (e. g. LAVA Docker container)

Usage

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

lava -ak 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.

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
• Andrew Quijano
• Wil Robertson
• Aaron Sedlacek
• Rahul Sridhar
• Frederick Ulrich
• Ryan Whelan