Garak
Garak is an open-source LLM vulnerability scanner developed by NVIDIA. It is designed to test the security of large language models (LLMs) by probing them with various attacks and evaluating their responses. Garak supports a wide range of LLMs, including those from Hugging Face, OpenAI, and Replicate, and can be used to identify potential vulnerabilities such as hallucination, data leakage, prompt injection, and toxicity generation. The tool is highly customizable, allowing users to specify the target model, probe, and detector to use, and provides detailed logging and reporting capabilities. Garak is developed in Python and is available on GitHub, with a user guide and reference documentation available on the project's website.
- Category
- LLM
- Pricing
- Open Source
Problem solved
Garak solves the problem of identifying potential security vulnerabilities in large language models, which is a critical issue in the development and deployment of AI systems. By providing a comprehensive testing framework, Garak helps developers and researchers to identify and address potential weaknesses in their models, reducing the risk of security breaches and other adverse consequences.
Use cases
- Testing the security of LLMs
- Identifying potential vulnerabilities in AI systems
- Evaluating the robustness of LLMs to various attacks
- Developing and deploying secure AI systems
- Researching and improving the security of LLMs
Audience
- Developers
- Researchers
- Data Scientists
Pros
- Highly customizable
- Supports a wide range of LLMs
- Comprehensive logging and reporting capabilities
- Open-source and actively maintained
Cons
- Steep learning curve
- Requires significant expertise in AI security
- May not detect all potential vulnerabilities
Official website: https://github.com/NVIDIA/garak
Canonical LambdaBase page: https://www.lambdabase.com/tools/garak