For teams shipping AI agents that call tools and take real actions, BotGauge is the best AI red teaming tool. It red teams agent behavior across multi-turn conversations, tools, and policies, then turns each finding into an evaluation and guardrail that runs on every release. For model-level scanning, use Garak (free, NVIDIA). For scripted multi-turn attack campaigns, use PyRIT (free, Microsoft). For red teaming inside CI alongside evals, use Promptfoo or DeepTeam. For continuous testing across many models and modalities, consider Mindgard. Enterprises standardized on one security vendor will usually get red teaming bundled from Palo Alto Prisma AIRS, Check Point (Lakera), or Zscaler (SPLX).

The market changed fast in the past year. OpenAI acquired Promptfoo in March 2026, Check Point bought Lakera, Zscaler acquired SPLX, and Palo Alto folded Protect AI into Prisma AIRS. Several “best tools” lists still describe these as independent startups. This guide reflects who owns what today.

AI red teaming tools compared

The table below compares ten tools for AI red teaming, from free LLM red teaming tools you run from the command line to commercial automated red teaming platforms built for agents.

Tool	Type	Owner	Tests model or agent	Multi-turn attacks	Interface	Best for
Garak	Open source	NVIDIA	Model and chat endpoints	Limited	CLI	Broad vulnerability scans of a model
PyRIT	Open source (MIT)	Microsoft	Model and app endpoints	Yes (Crescendo, TAP)	Python library	Scripted adversarial campaigns
Promptfoo	Open source (MIT) + enterprise	OpenAI	Apps and agents	Yes	CLI, YAML, CI	Red teaming plus evals in CI
DeepTeam	Open source + Confident AI platform	Confident AI	Apps and agents	Yes	Python, CLI	OWASP-mapped tests for Python teams
Giskard	Open source + commercial Hub	Giskard	Apps and agents	Yes	Python, web	Combining quality and security tests
BotGauge	Commercial	BotGauge	Agents (tools, actions, workflows)	Yes, adaptive	Web platform, SDK, API	Agent behavior red teaming tied to evals and guardrails
Mindgard	Commercial	Mindgard	Models, agents, multimodal	Yes	Web platform	Continuous red teaming across many AI assets
Lakera Red	Commercial	Check Point	Apps and agents	Yes	Platform, Check Point Infinity	Red teaming paired with runtime guard
Prisma AIRS AI Red Teaming	Commercial	Palo Alto Networks	Apps, agents, models	Yes	Prisma AIRS console	Palo Alto customers
SPLX	Commercial	Zscaler	Apps, agents, MCP servers	Yes	Zscaler Zero Trust Exchange	Zscaler customers needing discovery plus red teaming

Editor note: add a pricing column only with figures confirmed on each vendor’s pricing page at publish time. The open-source tools are free; LLM API costs for attacker and judge models still apply.

What AI red teaming tools actually test

AI red teaming tools send adversarial inputs to an AI system and score whether it fails. The failure might be a leaked system prompt, a jailbreak, toxic output, or exposed personal data. That much is common to every tool on this list.

Most modern tools do this through automated red teaming: an attacker model generates and adapts attacks, and a judge model or rule scores each response. That lets a team run thousands of attempts per release instead of the few dozen a human red teamer can manage. For a primer on the practice itself, see what AI agent red teaming is.

The split that matters is between testing what a model says and testing what an agent does. A chatbot that produces a bad sentence is embarrassing. An agent that issues a refund, rebooks a flight, or deletes a record because a support ticket contained hidden instructions is an incident. We cover this distinction in depth in LLM red teaming vs agent red teaming.

The OWASP Top 10 for Agentic Applications, released in December 2025 with input from more than 100 practitioners, is now the reference most tools map their test suites against. When you evaluate a tool, ask which of those risks it can actually reproduce against your system, not just which ones appear in its marketing.

A June 2026 Cloud Security Alliance evaluation of PyRIT puts the gap plainly: prompt-level tooling is a strong force multiplier for red teamers but does not replace system-level validation of agents, with tool misuse as the clearest example. That finding applies to most open-source scanners, and it is the main reason teams shipping agents end up combining an open-source tool with an agent-level platform.

Best open-source AI red teaming tools

1. Garak (NVIDIA)

Best for: fast, broad vulnerability scans of a model or chat endpoint.

Garak, short for Generative AI Red-teaming and Assessment Kit, is the closest thing the field has to nmap for LLMs. You point it at a model, and it runs its probe library against it, scoring each response with detectors matched to the probe. Coverage includes prompt injection, jailbreaks, data leakage, hallucination, and toxicity. Version 0.17.0 shipped in September 2026, and NVIDIA continues to back it.

It connects to Hugging Face, OpenAI-compatible APIs, local GGUF models, and generic REST endpoints, so you can scan most deployments without writing code.

Limitations: Garak tests the model’s responses. It does not know your agent’s tools, permissions, or business rules, so it cannot tell you whether a successful injection would have triggered a real action. Output is a scan report, not a regression suite.

2. PyRIT (Microsoft)

Best for: security teams scripting multi-turn attack campaigns.

PyRIT (Python Risk Identification Toolkit) came out of Microsoft’s AI Red Team and is MIT licensed. Its strength is orchestrated multi-turn attacks: an attacker model talks to your target, a scorer judges progress, and the attacker adapts. Published strategies such as Crescendo (gradual escalation) and Tree of Attacks with Pruning come built in, along with converters for encoding and multimodal attacks. Every prompt and response is stored, which gives you an audit trail.

Limitations: PyRIT is a framework, not a product. You write Python to define targets, objectives, and scorers, and a human still has to triage findings. The CSA evaluation linked above notes it does not on its own validate agent behavior such as tool misuse.

3. Promptfoo (now part of OpenAI)

Best for: developer teams that want red teaming and evals in the same CI pipeline.

Promptfoo is an MIT-licensed CLI that started as an eval tool and grew a red teaming engine covering prompt injection, jailbreaks, PII leaks, access control issues, and tool misuse. You define tests in YAML and run them on every pull request through a GitHub Action. OpenAI announced the acquisition on March 9, 2026, and both companies have committed to keeping the project open source and multi-provider.

Limitations: A testing tool owned by one model provider raises a fair neutrality question for teams running mixed models. Watch how provider support evolves. Like most config-driven tools, deeper agent scenarios require you to model the agent’s tools yourself.

4. DeepTeam (Confident AI)

Best for: Python teams who want tests mapped to OWASP and NIST out of the box.

DeepTeam is built on DeepEval and runs locally. It ships 20+ single-turn and multi-turn attack methods and lets you select a framework, such as the OWASP Top 10 for LLMs, the OWASP Top 10 for Agents, or NIST AI RMF, instead of picking vulnerabilities by hand. It also includes guardrails, so the same library covers testing and runtime checks. Results can be pushed to the commercial Confident AI platform for tracking.

Limitations: Attack generation and scoring both rely on LLMs, which adds cost and some judge variance. Teams outside Python will find it less natural than a CLI tool.

5. Giskard

Best for: teams that want quality and security testing in one workflow.

Giskard offers an open-source Python library for scanning LLM apps and a commercial Hub for collaborative testing of agents. It is strongest when you want hallucination and business-logic checks alongside adversarial ones.

Limitations: The deepest agent red teaming features sit in the commercial product, so confirm which capabilities are in the open-source library before you commit.

Editor note: confirm Giskard’s current open-source library scope and license on its GitHub page before publishing.

Best commercial AI red teaming platforms

6. BotGauge

Best for: product and engineering teams shipping agents that take real actions.

BotGauge red teams agent behavior rather than model output. It runs adaptive scenarios across inputs, context, tools, policies, and multi-turn conversations, then lets you trace each finding through prompts, tool calls, and execution paths. The part most teams care about is what happens next: a finding becomes an evaluation that runs on every release, and the boundary it exposed becomes a policy or guardrail.

It works with OpenAI, Anthropic, and Hugging Face models and with LangChain, LlamaIndex, LangGraph, CrewAI, and AutoGen, without changing how your agent is built. It covers RAG assistants, support agents, voice agents, multi-agent systems, and transactional agents. SOC 2 Type II, SSO/SAML, and fine-grained access control are included.

Limitations: BotGauge is built for agent and application teams, not as a network-level AI firewall. If your security team wants AI controls inside an existing Palo Alto, Check Point, or Zscaler deployment, one of those suites will fit procurement better. It is also not designed for benchmarking raw foundation models.

Try it: Start a free red team run against your agent, or book a 30-minute demo to see a finding traced and turned into an evaluation.

7. Mindgard

Best for: security teams running continuous red teaming across many models and agents.

Mindgard spun out of Lancaster University and raised a $30M Series A. It works in four phases: discover AI assets, profile them, attack, and defend. Testing extends beyond LLMs to computer vision, audio, and multimodal models, with findings mapped to MITRE ATLAS and OWASP. It also offers human-led red teaming services.

Limitations: Its scope is broad AI security, so teams that only need agent behavior testing tied to their release process may find it heavier than required.

8. Lakera Red (Check Point)

Best for: organizations that want red teaming and runtime protection from the same vendor.

Lakera built Lakera Red for pre-deployment assessments and Lakera Guard for runtime enforcement, informed by attack data from its Gandalf adversarial game. Check Point acquired the company and is integrating it into its Infinity platform.

Limitations: Roadmap and packaging now follow Check Point’s priorities. Non-Check Point shops should confirm whether standalone purchase is still supported.

9. Prisma AIRS AI Red Teaming (Palo Alto Networks)

Best for: enterprises already on Palo Alto.

Palo Alto acquired Protect AI in April 2025 and rebuilt its products into Prisma AIRS. The AI Red Teaming module runs continuous automated attacks, which Palo Alto says draw on more than 500 attack techniques, and ties into its runtime firewall and posture management.

Limitations: The value is in the suite. Teams not running Palo Alto infrastructure lose most of the integration advantage.

10. SPLX (Zscaler)

Best for: Zscaler customers who need AI asset discovery and red teaming together.

Zscaler acquired SPLX in November 2025. The platform pairs AI asset discovery (including RAG pipelines and MCP servers) with automated red teaming that Zscaler says uses more than 5,000 attack simulations, plus prompt hardening and governance features.

Limitations: Like the other suite acquisitions, it fits best inside its parent’s platform.

Also worth a look: the Confident AI platform if you already use DeepTeam, and HiddenLayer for teams that also need model supply chain security.

How to choose an AI red teaming tool

Start with what you are shipping, then who will run the tests. Most mature teams end up with two tools: an open-source scanner for breadth and a platform for depth on the system they actually deploy.

If you are…	Start with	Add later
Evaluating or fine-tuning a model	Garak	PyRIT for custom multi-turn attacks
A security team building an internal red team practice	PyRIT	Mindgard for continuous coverage
Developers adding security checks to CI	Promptfoo or DeepTeam	An agent-level platform once agents touch real systems
Shipping an agent that calls tools, APIs, or takes actions	BotGauge	Garak for model-level baseline scans
Standardized on Palo Alto, Check Point, or Zscaler	That vendor’s AI module	A developer-side tool for pre-release testing

Four questions separate tools quickly during a trial:

Can it reproduce your agent’s real actions? Ask the vendor to show a test where an injected instruction leads to a tool call, not just a bad reply.
Does a finding become a regression test? A report you read once is far less valuable than a check that runs on every release.
Can you trace why it failed? You need the prompt, context, and tool call sequence behind a finding to fix it.
What does a run cost? Attacker and judge models consume tokens. Ask for the LLM spend of a typical full run, open source included.

For guidance on timing, see when to red team an AI agent.

If your agent already calls tools or takes actions for users, the fastest way to see where it breaks is to run an adaptive red team campaign against it. Try BotGauge free and turn your first findings into release checks the same day.

Frequently asked questions

What is the best free AI red teaming tool?

Garak is the best free starting point for scanning a model, because it runs a broad probe library with almost no setup. PyRIT is better if you need multi-turn attacks and can write Python. Promptfoo and DeepTeam are better if you want red teaming inside CI next to your evals.

Is Promptfoo still open source after the OpenAI acquisition?

Yes. Promptfoo’s founders and OpenAI both stated the project remains open source under its MIT license and will keep supporting other model providers. The enterprise features are being integrated into OpenAI Frontier.

What is the difference between Garak and PyRIT?

Garak is a scanner: you point it at a model and it runs a library of known attacks. PyRIT is a framework: you script adaptive, multi-turn campaigns where an attacker model adjusts based on the target’s responses. Many teams use Garak for breadth and PyRIT for depth.

Can open-source tools red team AI agents?

Partly. Promptfoo, DeepTeam, and PyRIT can send attacks to an agent endpoint, but they test what the agent says unless you model its tools and actions yourself. Validating whether an attack causes a real tool call or policy violation usually requires an agent-level platform.

How often should you red team an AI agent?

Before launch, and again whenever the model, prompt, tools, data sources, or autonomy level changes. Teams shipping weekly should run automated red team checks on every release and reserve manual campaigns for major changes.

Do AI red teaming tools map to OWASP?

Most do. DeepTeam, Mindgard, Promptfoo, and commercial suites map findings to the OWASP Top 10 for LLM Applications, and newer releases also map to the OWASP Top 10 for Agentic Applications published in December 2025.

Which AI tool is best for red teaming?

It depends on the target. BotGauge is best for red teaming AI agents that use tools and take actions. Garak is best for free, broad scans of a model. PyRIT is best for scripted multi-turn attacks, and Promptfoo or DeepTeam are best for red teaming inside CI.

What is automated red teaming for AI?

Automated red teaming uses an attacker model to generate, send, and adapt adversarial prompts against an AI system, while a judge model or rule scores each response. It scales testing to thousands of attempts per run and can be repeated on every release, which manual red teaming cannot.

What is agentic AI red teaming?

Agentic AI red teaming tests what an autonomous agent does, not just what it says. It checks whether attacks can make the agent misuse tools, exceed its permissions, skip approvals, or take unintended actions. Read our full guide to AI agent red teaming.

Is AI red teaming legal?

Red teaming your own AI systems, or systems you have written permission to test, is standard security practice. Attacking AI systems you do not own or have authorization for can breach terms of service and computer misuse laws. Get written scope and approval before testing third-party systems.

How we built this list

We selected tools that are actively maintained, documented publicly, and used for adversarial testing rather than general evaluation. We checked ownership, licensing, and release status against each project’s repository, vendor announcements, and independent reporting as of the date at the top of this page. BotGauge is our product; we have described its limitations alongside its strengths and ranked other tools first where they fit better.

Best AI Red Teaming Tools: Open Source and Commercial