If you're still testing LLM guardrails by hand — retyping variations in a chat tab, logging results in a notebook, eyeballing responses — you're leaving throughput on the table. PyRIT fixes that.
Microsoft's Python Risk Identification Tool is an open-source framework for running structured attack campaigns against LLM systems. The AI Red Team that built it ran it against 100+ internal operations: Phi-3, Copilot, the full stack. It chains targets, converters, scorers, and orchestrators into automated multi-turn campaigns. Here's a working setup in under 30 minutes.
The Four Primitives
Everything in PyRIT maps to something from offensive tooling. Once the analogy clicks, the configuration is straightforward.
Targets are your scope — any LLM endpoint. Azure OpenAI, HuggingFace, a local Ollama instance, or a custom REST API via HTTPTarget. Swap targets without touching the rest of the campaign.
