Choosing an AI red teaming platform: a buyer's guide

When a platform beats open source

Open-source scanners and frameworks are free to start and open to inspection, but your team has to choose the attacks, keep them current, run them and write up results. A platform takes over some of that work in exchange for some control. This section is our analysis.

A platform tends to make sense when nobody on the team has time to maintain attack content, when auditors or customers expect a report with findings, severity and fixes, when you test many agents and MCP servers that each need setup and judging, or when you want experts to run the tests and help read the results.

Open source tends to win when you need to read the code, keep every prompt and response in your own environment, or wire tests tightly into your build; our guide to open-source AI red teaming tools covers that side. The two mix well. Many teams run a free scanner on every build and a platform or service on a schedule. Both sit in the testing and red teaming layer of our AI Security Layers model.

The 12-point buyer checklist

OWASP’s GenAI Red Teaming Guide covers four areas: model evaluation, implementation testing, infrastructure assessment and runtime behavior analysis. Use them to see what a product leaves out. The questions are our analysis, and each can be checked against a vendor’s documentation.

1. What does it attack? A model endpoint, a deployed app, an agent, an MCP server, or all of these. Ask for the supported target types.
2. Which attack families are documented? Prompt injection, jailbreaks, data leakage, system prompt extraction, harmful content, misinformation.
3. Does it run multi-turn attacks? Many real attacks build over several turns, and single prompts miss them.
4. Can it test agents and tool use? Look for tests of unauthorized actions, tool misuse and exploit chains, not only chat answers. The controls those tests probe sit in the agent and tool access layer.
5. Does it handle more than text? If your product accepts images, audio or documents, those inputs need testing too.
6. How are findings judged? You want the prompt, the response and the verdict for each finding, so you can disagree with it.
7. Does it map to frameworks? The OWASP LLM Top 10, MITRE ATLAS, the NIST AI RMF, the EU AI Act. Treat a mapping as reporting help and a vendor statement.
8. Can you automate it? Check for an API, a CLI and a CI integration, so tests run on every change.
9. What does the report look like? Separate views for executives and engineers, with reproducible evidence and fix guidance.
10. Where does the data go? Cloud, your own cloud account or on-premises. Ask where target responses and attack logs are stored and for how long.
11. How is scope controlled? Testing should touch only systems you own or may test, with rate limits that protect production.
12. How is it priced? Free tier, credits, per application or contact sales. Ask what a service includes and what costs extra.

Platforms compared

These are the platforms and services in our red teaming category with enough sourced facts to compare, as our methodology describes. We compile from vendors’ public pages and do not run the products, so this is not a ranking of attack quality; the Coverage Score measures how much a vendor documents publicly. Some well-known vendors are missing because we could not verify enough facts at the source.

Hosted platforms with a CLI or API

Mindgard runs multi-turn attacks with the actor_attack and crescendo techniques from the CLI Source, checked October 21, 2026: docs.mindgard.ai · 2026-10-21 (opens in a new tab) , and it publishes an example GitHub Action that adds a Mindgard check to an MLOps pipeline Source, checked October 21, 2026: github.com · 2026-10-21 (opens in a new tab) . Pricing: not published in the sources we checked Source, checked October 21, 2026: mindgard.ai · 2026-10-21 (opens in a new tab) . Best for security teams that want a hosted platform with a CLI and Burp Suite extension to test chatbots and LLM endpoints they own.

Enkrypt AI generates adversarial prompts automatically for chosen risk categories, runs them against the target and judges the responses Source, checked October 21, 2026: docs.enkryptai.com · 2026-10-21 (opens in a new tab) , and it reports attack success rate by category, sub-category and attack method, with the prompt, response and judge verdict for each row Source, checked October 21, 2026: docs.enkryptai.com · 2026-10-21 (opens in a new tab) . Its pricing model is freemium, with the plan details as listed by the vendor on its tool page. On ownership, Anaconda announced on August 4, 2026 that it has acquired Enkrypt AI. Enkrypt AI remains available with no changes to existing products, plans or support. Source, checked October 21, 2026: anaconda.com · 2026-10-21 (opens in a new tab) Best for security and ML teams that want an API-driven red teaming service with scored reports and a free plan to start.

Repello AI discovers and validates multi-stage exploit chains in AI agents Source, checked October 21, 2026: repello.ai · 2026-10-21 (opens in a new tab) , and it tests text, files, voice and images, with multimodal tests in more than 100 languages Source, checked October 21, 2026: repello.ai · 2026-10-21 (opens in a new tab) . Pricing: not published in the sources we checked Source, checked October 21, 2026: repello.ai · 2026-10-21 (opens in a new tab) . Best for security teams that want autonomous red teaming of AI agents, with findings mapped to frameworks for reporting.

SPLX runs automated probes that trigger and detect specific vulnerabilities in a target application Source, checked October 21, 2026: docs.probe.splx.ai · 2026-10-21 (opens in a new tab) , and it generates a shareable PDF report for each test run with suggested mitigation strategies Source, checked October 21, 2026: docs.probe.splx.ai · 2026-10-21 (opens in a new tab) . Its pricing model is contact sales, and the plan names the vendor lists are on its tool page. On ownership, Zscaler announced on November 3, 2025 that it has acquired SPLX. Source, checked October 21, 2026: zscaler.com · 2026-10-21 (opens in a new tab) Best for teams that want a managed platform for automated red teaming of AI applications, with on-prem or hybrid deployment on the Enterprise plan.

Platform plus managed service

Adversa AI tests agents for tool hijack, goal manipulation and inter-agent attacks, and MCP servers for tool misuse and privilege escalation Source, checked October 21, 2026: adversa.ai · 2026-10-21 (opens in a new tab) , and it provides executive, technical and compliance reports plus risk-ranked findings with reproducible attack artifacts Source, checked October 21, 2026: adversa.ai · 2026-10-21 (opens in a new tab) . Pricing: not published in the sources we checked Source, checked October 21, 2026: adversa.ai · 2026-10-21 (opens in a new tab) . Best for teams that want a vendor to run continuous red teaming across models, applications, agents and MCP servers, with on-prem or managed-service options.

For Gray Swan, the managed AI Red-Teaming service lists prompt injection among its attack types Source, checked October 21, 2026: grayswan.ai · 2026-10-21 (opens in a new tab) , and the managed service lists tool exploitation among its attack types, with scope that includes tools and retrieval systems Source, checked October 21, 2026: grayswan.ai · 2026-10-21 (opens in a new tab) . Its automated platform, Shade, is described with its reporting and retest facts on the Gray Swan tool page. It is closed source Source, checked October 21, 2026: grayswan.ai · 2026-10-21 (opens in a new tab) . Best for teams that want automated agent attacks and an expert-run engagement from the same vendor.

Expert-led service

Lakera Red covers agentic workflows with multi-step, tool-using agents, and tests for unauthorized actions Source, checked October 21, 2026: docs.lakera.ai · 2026-10-21 (opens in a new tab) , and it delivers a report with severity ratings and remediation guidance Source, checked October 21, 2026: docs.lakera.ai · 2026-10-21 (opens in a new tab) . On ownership, Check Point completed its acquisition of Lakera in the fourth quarter of 2025. Source, checked October 21, 2026: checkpoint.com · 2026-10-21 (opens in a new tab) Best for teams that want security experts to run adversarial tests against a chatbot or agent they own and deliver a report.

Cloud provider tool

For the AI Red Teaming Agent in Microsoft Foundry, the documentation says automated scans simulate adversarial probing of model and application endpoints Source, checked October 21, 2026: learn.microsoft.com · 2026-10-21 (opens in a new tab) . On agents, indirect prompt injection (XPIA) tests whether an agent follows malicious instructions hidden in external data such as emails or documents Source, checked October 21, 2026: learn.microsoft.com · 2026-10-21 (opens in a new tab) . It is closed source Source, checked October 21, 2026: learn.microsoft.com · 2026-10-21 (opens in a new tab) . Best for teams already building on Microsoft Foundry that want automated safety and agent red teaming scans before release.

Questions to ask vendors

The checklist covers the product. These questions cover what a feature list does not show, and they are our suggestions.

1. Can we see a sample report with real findings, including the exact prompts, responses and judge verdicts?
2. How do you reduce false positives, and how do we mark a finding as wrong?
3. Which target types and authentication methods are supported today, and which are planned?
4. Where are our target responses and attack logs stored, who can read them, and how do we delete them?
5. What happens when a test finds real sensitive data in a response?
6. How do you keep attack content current, and how often is it updated?
7. How do you keep tests from harming a production system?
8. What does the price include, and what changes it: applications, tests, credits or seats?

Get the answers in writing and keep them with your vendor review. For the defenses these tests probe, see prompt injection detection tools and the best LLM guardrails.

How to choose

1. Start from the target. If you run agents or MCP servers, shortlist the platforms that document agent testing.
2. Match delivery to your team. A self-serve CLI or API for engineering teams, a managed service when you want experts to run the work.
3. Check data handling early. Deployment options and storage terms often decide the shortlist.
4. Ask for evidence. A sample report and a trial on a staging copy you own tell you more than a feature list.
5. Keep an open-source scanner running. It gives you a baseline you control to compare the vendor’s findings against.