Red-teaming an AI system at scale needs tooling. This guide covers the main AI red-teaming tools, what they do, and how to choose — and why tools support red-teaming rather than replace it. It's a companion to our AI red teaming guide and part of our guide to AI testing.
AI red-teaming tools automate the generation and running of adversarial attacks against an AI system — prompt-injection and jailbreak attempts, harmful-content probes, data-leakage tests — at a scale and speed no manual tester could match, and organise the results into something reportable.
Several established, mostly open-source tools are widely used:
Automated tools give you breadth — thousands of attack variations, fast, repeatably. Skilled human red-teamers give you depth — the creative, novel attacks a script would never think of. The strongest programmes combine both: tools for coverage, humans for the attacks that matter.
Evaluate on: the range of attack types they cover, support for your setup (LLMs, agents, your stack), the quality of their reporting and evidence, and how they integrate into your CI/testing pipeline. Favour tools that produce an evidence trail, not just a pass/fail.
A tool can run ten thousand known attacks; it can't reason about your specific system's misuse pathways or judge whether a finding is acceptable in context. Credible red-teaming pairs tooling with experienced testers.
AramGRC combines red-teaming tooling with expert adversarial testers to harden your AI — and can advise on the tools that fit your stack. See our AI red teaming guide.
Open-source tools like Garak (an LLM vulnerability scanner), PyRIT (Microsoft's risk-identification toolkit), and promptfoo (LLM evaluation and red-teaming), alongside fairness toolkits like Fairlearn and AIF360.
Garak is an open-source LLM vulnerability scanner; PyRIT is Microsoft's open-source toolkit for automating adversarial testing of generative AI; promptfoo is an open-source tool for evaluating and red-teaming LLM applications.
Both — automated tools provide breadth and speed, while skilled human red-teamers find the creative, novel attacks tools miss. The best programmes combine them.
Yes — Garak, PyRIT and promptfoo are widely used open-source options for adversarially testing AI and LLM systems.
The types of AI testing — evaluation, red-teaming, security, bias and drift — and how to use them.
How to evaluate an AI model's performance, quality and metrics.
How to test an AI system for bias and unfair outcomes.
Adversarial testing, prompt injection and jailbreak defence for AI.
Monitoring AI models in production for drift, performance and bias.
The tools used to red-team and adversarially test AI systems.
You're here
A step-by-step process for testing AI models before and after release.
I red-teamed the same LLM with Garak, PyRIT and promptfoo — what broke, and which I'd pick.