Introduction to Purple Llama
Purple Llama is an ambitious and comprehensive project designed to bring together tools and evaluations aimed at fostering responsible development of open generative AI models. This project plans to provide essential resources, especially in areas like Cyber Security and Input/Output safeguards. As the Purple Llama project progresses, it aims to expand its contributions to various facets of AI safety and responsible usage.
Why the Name Purple Llama?
The name "Purple Llama" draws inspiration from the concept of "purple teaming" in cybersecurity. Traditionally, cybersecurity strategies involve "red teams," who simulate attacks, and "blue teams," who focus on defense. By merging these two approaches, purple teaming promotes collaboration to evaluate and mitigate potential risks effectively. This collaborative strategy is reflected in Purple Llama’s approach to generative AI, emphasizing holistic risk management and safe AI deployment.
Licensing Details
Components of Purple Llama are distributed under permissive licenses, promoting both research and commercial usage. This approach is aimed at encouraging community collaboration and establishing standardized tools for developing trust and safety in AI applications. The models within the project, for example, employ the Llama Community license, while certain evaluations and benchmarks are available under the MIT license.
System-Level Safeguards
A key focus of Purple Llama is ensuring that AI models operate safely within defined content guidelines. Key safeguards include:
Llama Guard
Llama Guard 3 is a suite of advanced moderation models for input and output. These models are adept at identifying content violations and are optimized to support multiple languages, large context windows, and image reasoning capabilities. Importantly, Llama Guard 3 models protect against cybersecurity threats by detecting harmful cyberattack responses and regulating code execution in AI systems.
Prompt Guard
Prompt Guard is a tool designed to protect AI applications from malicious inputs, such as prompt injections and jailbreak attempts.
- Prompt Injections: These involve exploiting untrusted data inputs to manipulate the model's behavior.
- Jailbreaks: These are deliberate attempts to bypass the model's inbuilt safety mechanisms.
Code Shield
Code Shield serves as a protective measure against insecure code outputs from large language models (LLMs). It provides filtering and prevention measures to reduce risks related to insecure code suggestions and unauthorized code execution.
Evaluations and Benchmarks
Purple Llama includes several evaluation tools to assess the cybersecurity risks associated with LLMs:
CyberSec Eval
CyberSec Eval is a pioneering set of cybersecurity safety evaluations. It offers metrics and tools to assess LLM vulnerabilities, including their potential to suggest insecure code or assist in cyberattacks. This framework supports the responsible development of AI, as highlighted by global AI safety commitments, and provides insights into the existing security risks with LLMs.
Subsequent versions of CyberSec Eval have expanded capabilities, assessing areas such as code interpreter misuse, prompt injection vulnerabilities, and offensive cyber capabilities.
Getting Started with Purple Llama
For those wishing to explore and integrate these safeguards into their own systems, resources are available as part of the Llama reference system. These resources offer guidance on deploying the provided security measures effectively and responsibly.
Join the Purple Llama Community
Engagement with the Purple Llama community is encouraged. Prospective contributors can find information on how to participate in the community efforts through the project's contributing guidelines.
In summary, Purple Llama positions itself as a crucial initiative in the development of secure and responsible AI technologies. While building upon innovative concepts from cybersecurity and AI, Purple Llama provides a robust framework to navigate the complexities of generative AI safely and effectively.