pyrit
The Python Risk Identification Tool for LLMs (PyRIT) is a library used to assess the robustness of LLMs
Decision gist · record as of 2026-08-14
Yes, if you are responsible for security assessment of generative AI systems. PyRIT is actively maintained, carries no known vulnerabilities, uses a permissive MIT license, and has low install friction. The large dependency footprint is typical for AI frameworks and should not deter use in development or test environments. Install it if you need systematic red-teaming capabilities; skip it if you are not evaluating LLM security.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Most use cases require API credentials (OpenAI, Azure, or other LLM providers) and network access to target systems.
- Low install friction with a pure Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—you can use, modify, and distribute PyRIT freely as long as you include the license notice.
last release 2026-07-30 (15 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 115,960 downloads/mo, #12,229 on PyPI
Alternatives
Verify before relying
pip install pyrit
from pyrit.prompt_target import OpenAITarget
from pyrit.orchestrator import PromptSendingOrchestrator
# Configure and run red-teaming scenarios against an LLM- Specific red-teaming attack types and scenarios supported beyond the framework's general capability
- Performance characteristics when testing large-scale or high-volume LLM deployments
- Integration maturity with LLM providers other than OpenAI and Azure
What it is and what it does
PyRIT is an open-source framework from Microsoft designed to help security teams systematically identify vulnerabilities and risks in generative AI systems. It provides orchestration tools to run red-teaming campaigns—automated attempts to find failure modes, prompt injection vectors, and robustness issues—against LLMs and other AI services. The framework abstracts away the mechanics of connecting to different AI providers (OpenAI, Azure, and others) and managing test scenarios, so you can focus on designing and running security assessments.
The package is built on a large dependency stack that includes cloud SDKs (Azure), document processing libraries (pypdf, python-docx), and AI frameworks (datasets, numpy, openai). It targets security professionals and engineers who need to validate AI system behavior before deployment, making it most useful in organizations running their own LLM services or evaluating third-party AI systems. The framework is actively maintained and supports modern Python versions.
Use it for
- Run automated red-team attacks against your LLM deployment to find prompt injection and jailbreak vulnerabilities before production
- Assess the robustness of a generative AI system by testing it against a library of adversarial prompts and scenarios
- Generate reports documenting AI safety and security findings for compliance or internal audit purposes
- Integrate AI risk assessment into your CI/CD pipeline to catch regressions in model behavior across versions
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are responsible for security assessment of generative AI systems.
PyRIT is actively maintained, carries no known vulnerabilities, uses a permissive MIT license, and has low install friction. The large dependency footprint is typical for AI frameworks and should not deter use in development or test environments. Install it if you need systematic red-teaming capabilities; skip it if you are not evaluating LLM security.
Install
pyrit on PyPI
Before you install
Low install friction with a pure Python wheel. Active maintenance as of 15 days ago. Supports Python 3.10 through 3.14. Carries 43 runtime dependencies including cloud integrations (Azure), document processing, and AI libraries, which may complicate deployment in restricted environments.
Requires Python 3.10 or later. Most use cases require API credentials (OpenAI, Azure, or other LLM providers) and network access to target systems.
License in practice
MIT license permits commercial and private use with minimal restrictions—you can use, modify, and distribute PyRIT freely as long as you include the license notice.
Quickstart
pip install pyrit
from pyrit.prompt_target import OpenAITarget
from pyrit.orchestrator import PromptSendingOrchestrator
# Configure and run red-teaming scenarios against an LLMVerify before relying
- Specific red-teaming attack types and scenarios supported beyond the framework's general capability
- Performance characteristics when testing large-scale or high-volume LLM deployments
- Integration maturity with LLM providers other than OpenAI and Azure
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 43 packagesaiofilesalembicappdirsartavazure-coreazure-identityazure-keyvault-secretsazure-ai-contentsafetyazure-storage-blobbase2048coloramaconfusablesconfusable-homoglyphsecojidatasetsexceptiongroupfastapihttpxjinja2numpyopenaiopenpyxlpillowpydanticPyJWTpyodbcpypdfpython-docxpython-dotenv |
| Maintenance | Actively maintained 15 days since the last release |
| First released | |
| Downloads | 115,960 / month, #12,229 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: pyrit-1.0.1-py3-none-any.whl
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