8 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
An open benchmark for testing how well large language models resist jailbreak attacks — prompts designed to get a model to produce harmful or unwanted content. It provides a public repository of jailbreak prompts, a standardized evaluation library with a defined threat model and scoring, and a leaderboard of attacks and defenses.
A standardized evaluation framework for automated red teaming of large language models, measuring how often attacks get models to comply with harmful requests and how reliably models refuse them.
Bugcrowd's library of security guides, ebooks, research reports and customer stories, including its Ultimate Guide to AI Security (2026) and Ultimate Guide to Red Teaming. A reasonable free way into how bug bounty and penetration testing programmes actually run.
Why I recommend it: Free, but several downloads ask for your work email and it is a vendor's marketing library, so the conclusion is usually that you need a bug bounty platform. Read it for the vocabulary and process, then check claims against a neutral source.
Microsoft's open-source toolkit for red-teaming AI systems: automated attack prompts, scoring of the responses, and repeatable runs. Free.
From the site: The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI system...
Why I recommend it: Built by the team that red-teams Microsoft's own AI products, and released as-is. Best paired with a written idea of what you are testing for.
An open-source scanner that probes a language model for weaknesses — prompt injection, data leakage, jailbreaks, toxic output — and reports what it found. Free.
From the site: the LLM vulnerability scanner. Contribute to NVIDIA/garak development by creating an account on GitHub.
Why I recommend it: Point it at a model you are about to rely on and see how it fails before your users do.
An open-source tool for testing and red-teaming prompts and AI apps — run the same prompts across models, compare answers, and catch regressions. Free and self-hosted.
From the site: The AI Security Platform that catches vulnerabilities in development. Trusted by 156 of the Fortune 500 and 300,000+ developers worldwide.
Why I recommend it: The practical one: if you have built anything on top of a model, this is how you check a prompt change did not quietly make it worse.
An OpenAI-compatible API for unrestricted language models aimed at red teaming, security research, evaluations, and synthetic data, paired with a policy gateway for per-project keys, audit logs, and no data retention.
Why I recommend it: I keep this in the ethics shelf on purpose. Seeing how guardrails get removed for testing is the clearest way to understand why they matter in the tools you actually use at work.
Public AI red-teaming arena where anyone can try to break frontier models in timed challenges.
Why I recommend it: A legitimate portfolio line for AI-security work: document what you tried and what broke, not just your score.
