PyRIT is Microsoft's open-source framework designed to help security professionals and AI developers systematically identify risks in generative AI systems. Unlike one-off prompt testing, PyRIT provides an automated, repeatable approach to red teaming that supports multi-turn attack strategies — chaining prompts across conversation turns to discover vulnerabilities that single-prompt tests would miss. The framework includes orchestrators that manage attack flows, scorers that evaluate whether attacks succeeded, and converters that transform prompts to evade safety filters.
The toolkit supports testing against multiple target types including Azure OpenAI, Hugging Face models, and custom API endpoints. Attack strategies include crescendo attacks that gradually escalate content, pair attacks that use one LLM to generate adversarial prompts for another, and tree-of-attacks that explore multiple attack paths simultaneously. PyRIT also includes built-in scoring for evaluating response safety, truthfulness, and policy compliance, making it useful for both offensive testing and defensive validation of AI guardrails.
PyRIT is open-source under MIT license and backed by Microsoft's responsible AI research team, with academic publications supporting its methodology. The framework is designed for security researchers, red teams, and AI safety practitioners who need to systematically test AI systems before deployment. It integrates with Azure AI Content Safety for automated evaluation and provides detailed logging for audit trails. For organizations deploying LLM-powered applications, PyRIT offers a structured approach to identifying and documenting AI-specific vulnerabilities.
