PyRIT

PyRIT is an open-source toolkit for probing the security and safety of generative AI. It suits security engineers, developers, and evaluators who can operate Python-based infrastructure and supply their own model endpoints. Its breadth and composability are compelling for bespoke red-team programs, while teams wanting a hosted, turnkey service should choose another approach.

Overview

PyRIT combines automated and human-guided red teaming in a framework you assemble around your assessment goals. You can work from its command-line scanner or interactive shell, use the CoPyRIT graphical interface, or construct workflows directly in the framework. This gives experienced teams several ways to move from exploratory testing to repeatable runs, but it also means configuration and operational ownership stay with you.

Targets documented by the project cover OpenAI, Azure, Anthropic, Google, and Hugging Face, plus custom HTTP endpoints, WebSockets, and web applications exercised through Playwright. That range makes it suitable for comparing providers or testing an application’s own interface rather than only a single API. Prompt-injection, jailbreak, data-leakage, unsafe-output, and custom test cases are all within its stated scope.

Key features

Strategies and repeatable scenarios

Single-turn and multi-turn attack strategies include Crescendo, TAP, and Skeleton Key. Scenarios combine strategies with datasets so teams can rerun assessments against defined objectives such as content harms, psychosocial risks, and data leakage. The result is a better basis for consistent evaluations than a collection of ad-hoc prompts, while the framework still leaves you to select suitable datasets and attack paths.

Prompt conversion

Converters can encode, obfuscate, translate, or semantically alter prompts. They also support transformations between text, images, audio, video, and files. This is useful when a test needs to vary how an instruction reaches a model or cross modality boundaries, although designing meaningful conversions remains part of the evaluator’s work.

Scoring and memory

Scorers can produce Boolean results or normalized scores from 0.0 to 1.0. They may rely on another LLM, Azure AI Content Safety, or custom logic, allowing teams to balance automated judging with domain-specific rules. Built-in memory records conversations, scores, and attack results in SQLite or Azure SQL, giving repeated campaigns a place to retain results and context.

Interfaces and modular architecture

The framework separates targets, converters, scorers, memory, datasets, attacks, and scenarios. That modularity supports replacing one part without rebuilding the whole assessment. The command line and shell favor scripted or interactive work, while CoPyRIT offers a graphical route for users who prefer not to begin in a terminal.

Setup and credential handling

Local installation is documented with pip or uv, and Docker installation is also available. Local prerequisites cover Python 3.10 through 3.14; contributors working on the frontend need Node.js 22 or newer. For shared or deployed configurations, PyRIT recommends Azure Key Vault and warns that plaintext .env files are less secure. In CoPyRIT, an API key entered while creating a target is held in memory and is not written to disk, which reduces persistence risk during that session.

Pricing

There is no license fee or subscription charge for the framework. The trade-off is that hosting, environment maintenance, endpoint access, and any provider usage costs remain your responsibility. This is a strong fit for organizations with existing engineering and security infrastructure, not for buyers seeking bundled hosted capacity or a managed testing allowance.

Platforms

PyRIT supports API, Linux, macOS, self-hosted, web, and Windows deployments. Its self-hosted model works across those environments, with installation available through Python tooling or Docker. The platform breadth helps distributed teams standardize on the same framework, but compatibility still depends on meeting the stated Python and, for frontend contributors, Node.js requirements.

Who it's for

Choose PyRIT if you need automated and human-led testing across several model providers or custom application endpoints, and you want to define your own attacks, conversions, scoring rules, datasets, and scenarios. It is particularly appropriate for security teams and developers building an internal evaluation process that must be rerun and retained.

Look elsewhere if you need a hosted workflow, a fixed quota, or minimal infrastructure work. PyRIT supplies the framework rather than a managed service, so teams without Python operations experience or endpoint credentials will face a steeper start.

Pros and cons

Pros

• Broad target coverage: Provider integrations, custom HTTP and WebSocket endpoints, and Playwright-tested web apps support varied assessment programs.
• Repeatable, extensible testing: Scenarios, datasets, modular attacks, converters, and custom test cases let teams standardize and adapt campaigns.
• Flexible evaluation: Boolean or 0.0–1.0 scoring can use LLMs, Azure AI Content Safety, or bespoke logic.
• Multiple working styles: CLI, interactive shell, CoPyRIT, and framework APIs accommodate scripted, graphical, and programmatic use.

Cons

• Operational burden: Users must provide the Python environment, configure endpoints, and manage deployment themselves.
• Provider credentials remain your responsibility: The free framework does not include model access or usage allowances.
• Security requires deliberate setup: Shared deployments should use Azure Key Vault rather than less-secure plaintext environment files.
• Frontend contribution requirements are current and specific: Node.js 22 or newer is required for contributor setup.

Alternatives

AIRTA Red Team is another free, open-source option across API, desktop, web, and Windows environments; choose it when you want no license fee and are comfortable bringing your own LLM API key.

OpenSecureAI Scanner suits teams that want a freemium hosted tier, with a Pro plan at $49 a month, 50,000 API requests per month, and hosted remediation and gateway completions.

Project Moonshot is a free choice for teams seeking a similarly broad, self-hosted platform footprint.

Promptfoo is the better fit when a freemium community plan with 10,000 red-team probes per month and broad provider integrations matches your workflow.

PromptGuard favors teams wanting a managed free allowance of 20,000 scans per month, one API key, one project, and 24-hour log retention.

Augustus is a free Apache 2.0 open-source alternative for Linux, macOS, Windows, and self-hosted use.

Basilisk is a free AGPL-3.0 tool distributed through CLI, desktop, Docker, or source packages.

AgentScan is a freemium API and web service with a free tier of five scans per month and 185 attack vectors.

Verdict

PyRIT is a strong choice for technically capable teams that want an open, modular foundation for repeatable generative-AI red teaming across providers and custom interfaces. Choose it for control over attack design, scoring, memory, and deployment. Choose an alternative when you need hosted execution, included quotas, or a simpler path from signup to results.