PyRIT, the Python Risk Identification Tool for generative AI, is an open-source, MIT-licensed framework from Microsoft that security professionals and engineers use to proactively identify risks in generative AI systems through automated scans, adaptive multi-turn attacks, and a web UI.
What it is
PyRIT is a Python framework published by Microsoft under the MIT licence and catalogued here in the AI & Machine Learning / AI Security & Privacy category. Its stated purpose is to empower security professionals and engineers to proactively identify risks in generative AI systems, and it packages that work as a toolkit rather than a hosted service: the project's own topics describe it as an AI red team tool, an AI security testing tool, and an AI evaluation tool, with additional topics covering safety testing and responsible AI. Documentation lives on the project website at microsoft.github.io/PyRIT, and the repository has gathered 4,731 stars and 1,012 forks, with 281 open issues and a last push in November 2026.
The problem it addresses is the difficulty of testing generative AI systems for risk in a structured, repeatable way rather than through ad hoc probing. It does this by combining automated scans, adaptive multi-turn attacks, and a web interface in a single Python codebase, so a team running an AI red-teaming exercise works from one framework instead of assembling separate scripts and harnesses. The project sits in the same Python ecosystem as the generative AI applications it is used to test. The material reviewed does not name a specific commercial product that PyRIT is positioned to replace.
Key capabilities
- Automated scans of generative AI systems, as described in the project's own summary of the toolkit.
- Adaptive multi-turn attacks, which carry an attack across successive exchanges with a model rather than firing a single prompt.
- A web UI, giving a browser-based interface alongside the code-level framework.
- A Python library and framework foundation, reflected in the project's language and in its description of itself as a framework for engineers.
- AI evaluation support, indicated by the
ai-evals, ai-evaluation, and ai-evaluation-tools topics.
- Safety and responsible AI testing, covered by the
ai-safety, ai-safety-research, and responsible-ai topics.
- A documented private vulnerability reporting route through the Microsoft Security Response Center, set out in the repository's security policy, plus a
CITATION.cff file for citing the project in research.
Who uses it and how
- Security professionals and engineers conducting red-team exercises against generative AI systems, the audience the README names directly.
- AI safety and responsible AI researchers, who are expected to cite the project's whitepaper, "PyRIT: Democratizing AI Red Teaming Through Open-Source Tooling", using the
CITATION.cff file when publishing work that uses it.
- Teams that need a browser-based interface for attack runs rather than only a code-level library, served by the bundled web UI.
- Contributors and community members who coordinate through the project's Discord server, with 1,012 forks indicating downstream copies adapted by other users.
Getting started
The repository README does not itself contain an install or run command; it directs readers to the project website at https://microsoft.github.io/PyRIT/ for instructions on how to use, install, or contribute. Installation and first-run steps should therefore be taken from that site rather than from the repository landing page.
How it compares
No other AI red-teaming or AI security tool, paid or open source, is named in the material available for this entry, so PyRIT stands alone in this registry on the evidence given. Any comparison against commercial AI red-teaming offerings on licence, self-hosting, data ownership, or cost model would have to be made outside these facts.
When to use it — and when not
PyRIT is a reasonable choice for a security or safety team that wants an MIT-licensed, Microsoft-published framework for automated scanning and multi-turn attack work, and that is comfortable working in Python with a web UI on top. Prospective users should know that the repository README is a short landing page: it carries no installation steps, no usage examples, and no architecture detail, so onboarding depends entirely on the external website, and the 281 open issues suggest a backlog worth reviewing before adoption. Teams needing a self-contained quickstart inside the repository, or those unwilling to observe Microsoft's trademark and brand guidelines for modified versions, should look elsewhere.
