LVE Repository

A repository of Language Model Vulnerabilities and Exposures (LVEs).

Browse LVEs · Add LVEs · Verify LVEs

The goal of the LVE project is to create a hub for the community, to document, track and discuss language model vulnerabilities and exposures (LVEs). We do this to raise awareness and help everyone better understand the capabilities and vulnerabilities of state-of-the-art large language models. With the LVE Repository, we want to go beyond basic anecdotal evidence and ensure transparent and traceable reporting by capturing the exact prompts, inference parameters and model versions that trigger a vulnerability.

Our key principles are:

• Open source - the community should freely exchange LVEs, everyone can contribute and use the repository.
• Transparency - we ensure transparent and traceable reporting, by providing an infrastructure for recording, checking and documenting LVEs
• Comprehensiveness - we want LVEs to cover all aspect of unsafe behavior in LLMs. We thus provide a framework and contribution guidelines to help the repository grow and adapt over time.

The LVE repository is organized into different categories, each containing a set of LVEs. All LVEs are stored in the repository/ directory. LVEs are grouped into different categories, like trust, privacy, reliability, responsibility, security.

To start browsing the LVE repository, you can either browse LVEs on the web or clone the repository and browse locally.

To get started with LVE contributions, you need to install the lve CLI tool. To do so, run the following command:

pip install lve-tools

Once installed, you can run the lve command, to see the list of available actions. To help grow the LVE repository, there are two main ways to contribute:

• Document and create new LVEs
• Re-produce and verify existing LVEs

Other ways to contribute include:

• Transferring existing, model-specific LVEs to new models and model families.
• Re-producing existing LVE instances by running them yourself.
• General contributions to the codebase (e.g. bug fixes, new checkers, improved CLI tooling).

To reproduce and help verify an LVE, you can record instances of existing LVEs:

# make sure you have cloned a copy of the LVE repository
git clone [email protected]:lve-org/lve.git
cd lve

# run 'lve record' for an interactive prompting session
lve record repository/security/prompt_leakage/sys_prompt_leak_cipher/openai--gpt-35-turbo/

You can record multiple instances in one session by specifying --loop as an argument to lve record.

With the record command, your goal is to find inputs that break the safety condition checked by the chosen LVE.

If you have found a model vulenerability or failure mode that is not yet covered by an existing LVE, you can create and report a new LVE and submit it to the repository.

To create and submit a new LVE, follow these steps:

# make sure you have cloned a copy of the LVE repository
git clone [email protected]:lve-org/lve.git
cd lve

# prepare a new LVE directory
lve prepare repository/security/prompt_leakage/new_leak

# record and document initial instances of the LVE (interactive CLI)
lve record repository/security/prompt_leakage/new_leak

Repeat the lve record command until you have collected enough instances of the LVE.

Before commiting your LVE to the repository, run the unit tests to perform some basic checks:

# run the unit tests for the new LVE
lve unit-test repository/security/prompt_leakage/new_leak

Finally, commit and push the new LVE+instances to GitHub:

# commit the new LVE+instances to Git history
lve commit

# Create a PR to merge your new LVE+instances into the main repo
lve pr 

All LVEs can be found in the repository/ directory, grouped by category subfolders like trust and privacy.

Since LVEs can apply across models, we include corresponding test configurations for each affected model. For example, inside the directory repository/security/prompt_leakage/sys_prompt_leak_cipher/ we have subfolders for models meta--llama-2-7b-chat and openai--gpt-35-turbo which contain LVEs for respective models (together with configuration and instances).

Each LVE consists of:

• LVE configuration file test.json

• The instances/ directory containing the recorded instances for the LVE. Each instance is generated using lve record. To create an LVE instance, lve record instantiates the prompt template using concrete placeholder values as provided by the user. It then runs the prompt against the model using a provided inference configuration (e.g. temperature) and finally applies the LVE's checking logic to determine whether the model's response passes or fails the LVE. The result of the run can be added as a single line in .jsonl file in the instances/ directory.

Next to prepare, record, commit and pr, the lve CLI tool provides a number of utilities to help with the documentation and tracking of LVEs. The 'lve' command line tool can be used to record and document language model vulnerabilities and exposures (LVEs). This tool is designed to be used in a terminal environment, and is intended to be used by security researchers and language model developers. To see the list of all available commands together with the usage description, simply type:

lve

To install the very latest version of lve-tools directly from the source tree, run the following commands:

cd lve-tools # switch to the lve-tools directory
pip install -e . # install editable version of lve-tools