PyRIT

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Score7.1

Rank#5 of 29

PriceFree

Free planYes

Runs onAPI, Linux, macOS, Self-hosted, Web, Windows

Summary

PyRIT is a free, open-source framework for automated and human-led red teaming of generative AI systems, aimed at assessing security and safety. It supports single- and multi-turn attack strategies such as Crescendo, TAP, and Skeleton Key. Scenarios combine strategies and datasets for repeatable assessments of content harms, psychosocial risks, data leakage, and other objectives. Users can run assessments with a command-line scanner or interactive shell, use the CoPyRIT graphical interface, or build workflows with the framework. Documented targets include OpenAI, Azure, Anthropic, Google, Hugging Face, custom HTTP endpoints and WebSockets, and web apps tested with Playwright. Its modular parts include targets, converters, scorers, memory, datasets, attacks, and scenarios. Converters can encode, obfuscate, translate, or semantically alter prompts, and convert among text, images, audio, video, and files. Scorers return Boolean or normalized 0.0–1.0 scores using LLMs, Azure AI Content Safety, or custom logic. Built-in memory can track conversations, scores, and attack results using SQLite or Azure SQL. PyRIT is self-hosted and requires a Python environment and configured AI endpoints; local installation lists Python 3.10 through 3.14 as prerequisites.

Who it is for

PyRIT suits teams assessing generative AI systems for security and safety through repeatable, automated or human-led red teaming. It is aimed at users able to configure AI endpoints and work with a Python environment.

What is good

Supports single- and multi-turn attack strategies.
Scenarios cover data leakage and content harms.
Offers command-line, shell, graphical, and workflow interfaces.
Targets include named services, custom endpoints, and web apps.
Scoring can use custom logic or listed services.

What to know first

Self-hosted use requires a Python environment and configured endpoints.
Local installation lists Python 3.10 through 3.14.
Frontend contributor setup requires Node.js 22 or higher.

Verdict

PyRIT provides multiple ways to run structured red-team assessments, with support for diverse targets, prompt transformations, and scoring approaches. Its self-hosted setup and environment prerequisites are important considerations.

PyRIT plans and pricing

All plans

PyRIT Free Open-source framework · requires a Python environment and configured AI endpoints microsoft.github.io · 30 Sept 2026

Compared on AI security testing tools

Free plan: Yesazure.github.io
Prompt injection tests: Yesazure.github.io
Jailbreak tests: Yesazure.github.io
Data leakage tests: Yesazure.github.io
Unsafe output tests: Yesazure.github.io
Custom test cases: Yesazure.github.io
Deployment mode: self_hostedazure.github.io

Facts

Purpose: PyRIT is an open-source framework for automated and human-led red teaming to assess the security and safety of generative AI systems.microsoft.github.io · 30 Sept 2026
Attack strategies: It supports single-turn and multi-turn strategies including Crescendo, TAP, and Skeleton Key.microsoft.github.io · 30 Sept 2026
Scenarios: Its scenarios package attack strategies and datasets for repeatable assessments of content harms, psychosocial risks, data leakage, and other objectives.microsoft.github.io · 30 Sept 2026
Interfaces: Users can run assessments with the command-line scanner and interactive shell, use the CoPyRIT graphical interface, or build workflows with the framework.microsoft.github.io · 30 Sept 2026
Targets: Documented targets include OpenAI, Azure, Anthropic, Google, Hugging Face, custom HTTP endpoints and WebSockets, and web apps tested with Playwright.microsoft.github.io · 30 Sept 2026
Components: The modular framework includes targets, converters, scorers, memory, datasets, attacks, and scenarios.microsoft.github.io · 30 Sept 2026
Prompt conversion: Converters transform prompts through text operations such as encoding, obfuscation, translation, and semantic changes, as well as conversions among text, images, audio, video, and files.microsoft.github.io · 30 Sept 2026
Scoring: Scorers can return true/false or normalized 0.0–1.0 scores and can use LLMs, Azure AI Content Safety, or custom logic.microsoft.github.io · 30 Sept 2026
Memory: Built-in memory can track conversations, scores, and attack results using SQLite or Azure SQL.microsoft.github.io · 30 Sept 2026
Security: PyRIT recommends Azure Key Vault for shared or deployed configuration and warns that plaintext .env files are less secure.microsoft.github.io · 30 Sept 2026
Credential handling: In CoPyRIT, an API key entered when creating a target is stored in memory only and is not persisted to disk.microsoft.github.io · 30 Sept 2026
Installation: The documentation provides local installation with pip or uv and separate Docker installation options.microsoft.github.io · 30 Sept 2026
Compatibility limit: The local installation page lists Python 3.10 through 3.14 as prerequisites, and the contributor setup page requires Node.js 22 or higher for the frontend.microsoft.github.io · 30 Sept 2026