This repository contains benchmarks for ZenGuard AI and information on how to run them.
There are two types of benchmarks that we run against ZenGuard AI:
Here you can find both benchmark results and how to run them yourself.
We are constantly monitoring Hugging Face for new datasets that relate to GenAI security. Then we run them against ZenGuard AI to find any potential security issues with our product.
| # | Dataset | Accuracy | Date Added |
|---|---|---|---|
| 1 | xTRam1/safe-guard-prompt-injection | 96% | 2024-07-01 |
| 2 | deepset/prompt-injections | 87% | 2024-05-15 |
| 3 | JasperLS/prompt-injections | 87% | 2024-05-15 |
| 4 | aporia-ai/prompt_injection | 87.68% | 2024-05-15 |
We have developed the ZenGuard Benchmarks PyPi package to help test and benchmark ZenGuard AI better.
Here are the instructions on how to use the package.
A much more detailed documentation is available at docs.zenguard.ai.
Test the capabilities of ZenGuard AI in our ZenGuard Playground. It's available to start for free to understand how our guardrails can enhance your GenAI applications.
Check out our Client library to get started with integrating ZenGuard AI into your project.
Book a Demo or just shoot us an email to hello@zenguard.ai
Topics we care about - LLM Security, LLM Guardrails, Prompt Injections, GenAI Security.
Developed with ❤️ by ZenGuard AI
FAQs
Test ZenGuard AI against different datasets and benchmarks.
We found that zenguard-benchmarks demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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