Raccoon

Quick links:

• Examples
• Splendidbing

Using LLMs for a project is great, but not if it ends up costing you a lot because a malicious prompt gets in.

Are you concerned about the security of your AI-powered application or model? Worried that it might be vulnerable to attacks such as cross-site scripting? If so, the Prompt Stress-Test is just what you need!

The Prompt Stress-Test is a tool designed to evaluate prompts for AI models and applications. By subjecting your prompts to rigorous testing, it helps you identify potential vulnerabilities and prevent attacks.

The best way to prevent cross-site scripting attacks is by using an allow list of acceptable input types. The Prompt Stress-Test uses this same approach to ensure that all inputs are safe and secure.

Other ways to create good prompts include understanding prompt design principles which can be found in our Prompt Engineering Guide.

To use the Prompt Stress-Test, you can use our web app or simply follow these steps:

1. Configure run.py with your evaluation method.
2. Run cps.py.
3. Check results.

It's that simple!

Don't believe us? Try out our demo! The link should be by the Github description. OR: After cloning this repository, navigate into the root directory, run npm i, and create a configuration file called config.json with your OpenAI API key inside like so:

{
  "key": "OPENAI KEY"
}

Then start the server by running node server.js. Finally, run python3 cps.py in another terminal window and watch as we stress-test your chosen prompt(s)!

For more information on how to improve prompt performance and security in general, check out these other resources:

• Google Forms LLM
• Bing Chat Prompt

And if you have any contributions or feedback for us, please don't hesitate to make a pull request!