--- id: pyrit version: "1.0.1" license: MIT license_treatment: permissive maintenance: active --- # pyrit — The Python Risk Identification Tool for LLMs (PyRIT) is a library used to assess the robustness of LLMs License: permissive · Maintenance: active · Downloads: 116.0K/mo ## 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 above — verify before relying. PyRIT is a framework for security professionals to identify and assess risks in generative AI systems through automated testing and red-teaming capabilities. 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 pip install pyrit uv add pyrit poetry add pyrit ## Installing pyrit 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. 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 LLM Requires Python 3.10 or later. Most use cases require API credentials (OpenAI, Azure, or other LLM providers) and network access to target systems. Verify 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_current - Install friction: low - Maintenance: active - Downloads: 116.0K/month (top 15,000 on PyPI) - Known vulnerabilities: none known ## Tags llm security testing, ai red teaming framework, generative ai risk assessment, llm robustness testing, ai safety evaluation, prompt injection testing, ai vulnerability scanning, ai-security, red-teaming, llm-testing [View on SkillFed](https://skillfed.io/packages/pyrit) · [View on PyPI](https://pypi.org/project/pyrit/)