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https://meta-llama.github.io/PurpleLlama/

Set of tools to assess and improve LLM security.
https://meta-llama.github.io/PurpleLlama/

Last synced: 10 days ago
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Set of tools to assess and improve LLM security.

Host: GitHub
URL: https://meta-llama.github.io/PurpleLlama/
Owner: meta-llama
License: other
Created: 2023-12-06T21:29:41.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-10-22T15:30:25.000Z (about 2 months ago)
Last Synced: 2024-10-29T09:27:04.619Z (about 1 month ago)
Language: Python
Homepage:
Size: 13.5 MB
Stars: 2,659
Watchers: 44
Forks: 442
Open Issues: 7
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-MLSecOps - PurpleLLama3

README

🤗 Models on Hugging Face&nbsp | Blog&nbsp | Website&nbsp | CyberSec Eval Paper&nbsp&nbsp | Llama Guard Paper&nbsp

---

# Purple Llama

Purple Llama is an umbrella project that over time will bring together tools
and evals to help the community build responsibly with open generative AI
models. The initial release will include tools and evals for Cyber Security and
Input/Output safeguards but we plan to contribute more in the near future.

## Why purple?

Borrowing a [concept](https://www.youtube.com/watch?v=ab_Fdp6FVDI) from the
cybersecurity world, we believe that to truly mitigate the challenges which
generative AI presents, we need to take both attack (red team) and defensive
(blue team) postures. Purple teaming, composed of both red and blue team
responsibilities, is a collaborative approach to evaluating and mitigating
potential risks and the same ethos applies to generative AI and hence our
investment in Purple Llama will be comprehensive.

## License

Components within the Purple Llama project will be licensed permissively enabling both research and commercial usage.
We believe this is a major step towards enabling community collaboration and standardizing the development and usage of trust and safety tools for generative AI development.
More concretely evals and benchmarks are licensed under the MIT license while any models use the corresponding Llama Community license. See the table below:

## System-Level Safeguards

As we outlined in Llama 3’s
[Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/), we
recommend that all inputs and outputs to the LLM be checked and filtered in
accordance with content guidelines appropriate to the application.

### Llama Guard

Llama Guard 3 consists of a series of high-performance input and output moderation models designed to support developers to detect various common types of violating content.

They were built by fine-tuning Meta-Llama 3.1 and 3.2 models and optimized to support the detection of the MLCommons standard hazards taxonomy, catering to a range of developer use cases.
They support the release of Llama 3.2 capabilities, including 7 new languages, a 128k context window, and image reasoning. Llama Guard 3 models were also optimized to detect helpful cyberattack responses and prevent malicious code output by LLMs to be executed in hosting environments for Llama systems using code interpreters.

### Prompt Guard
Prompt Guard is a powerful tool for protecting LLM powered applications from malicious prompts to ensure their security and integrity.

Categories of prompt attacks include prompt injection and jailbreaking:

* Prompt Injections are inputs that exploit the inclusion of untrusted data from third parties into the context window of a model to get it to execute unintended instructions.
* Jailbreaks are malicious instructions designed to override the safety and security features built into a model.

### Code Shield

Code Shield adds support for inference-time filtering of insecure code produced by LLMs. Code Shield offers mitigation of insecure code suggestions risk, code interpreter abuse prevention, and secure command execution. [CodeShield Example Notebook](https://github.com/meta-llama/PurpleLlama/blob/main/CodeShield/notebook/CodeShieldUsageDemo.ipynb).

## Evals & Benchmarks

### Cybersecurity

#### CyberSec Eval v1
CyberSec Eval v1 was what we believe was the first industry-wide set of cybersecurity safety evaluations for LLMs. These benchmarks are based on industry guidance and standards (e.g., CWE and MITRE ATT&CK) and built in collaboration with our security subject matter experts. We aim to provide tools that will help address some risks outlined in the [White House commitments on developing responsible AI](https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/), including:
* Metrics for quantifying LLM cybersecurity risks.
* Tools to evaluate the frequency of insecure code suggestions.
* Tools to evaluate LLMs to make it harder to generate malicious code or aid in carrying out cyberattacks.

We believe these tools will reduce the frequency of LLMs suggesting insecure AI-generated code and reduce their helpfulness to cyber adversaries. Our initial results show that there are meaningful cybersecurity risks for LLMs, both with recommending insecure code and for complying with malicious requests. See our [Cybersec Eval paper](https://ai.meta.com/research/publications/purple-llama-cyberseceval-a-benchmark-for-evaluating-the-cybersecurity-risks-of-large-language-models/) for more details.

#### CyberSec Eval 2
CyberSec Eval 2 expands on its predecessor by measuring an LLM’s propensity to abuse a code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection. You can read the paper [here](https://ai.meta.com/research/publications/cyberseceval-2-a-wide-ranging-cybersecurity-evaluation-suite-for-large-language-models/).

You can also check out the 🤗 leaderboard [here](https://huggingface.co/spaces/facebook/CyberSecEval).

#### CyberSec Eval 3
The newly released CyberSec Eval 3 features three additional test suites: visual prompt injection tests, spear phishing capability tests, and autonomous offensive cyber operations tests.

## Getting Started

As part of the [Llama reference system](https://github.com/meta-llama/llama-agentic-system), we’re integrating a safety layer to facilitate adoption and deployment of these safeguards.
Resources to get started with the safeguards are available in the [Llama-recipe GitHub repository](https://github.com/meta-llama/llama-recipes).

## FAQ

For a running list of frequently asked questions, for not only Purple Llama
components but also generally for Llama models, see the FAQ
[here](https://ai.meta.com/llama/faq/).

## Join the Purple Llama community

See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.