Much of the documentation is provided through notebooks, so you can use it interactively. This is an interesting alternative to traditional documentation, ready for use locally or on GitHub.
Using PyRIT to test your generative AI
The heart of PyRIT is its orchestrators, This is how you link data sets to targets, constructing the attacks a potential attacker might use. The tool provides a selection of orchestrators, from simple prompt operations to more complex operations that implement common attack types. Once you have experience with how orchestrators work, you can build your own as you experiment with new and different attacks. Results are scored, evaluating how the AI and its security tools respond to a prompt. Did it reject it, or did it deliver a harmful response?
Orchestrators are written in Python, using stored secrets to access endpoints. You can think of an orchestrator as a workflow, defining targets and prompts, and collating outputs for later analysis. One interesting option is the ability to convert prompts to different formats, to see the effect of, say, using a Base64 encoding rather than a standard text prompt.
