Data as of Sep 21, 2026 · Based on 370 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
If you want automated, continuous red teaming—especially for runtime checks, CI/CD, or multi-modal models—choose 
Brands AI recommends here
Named in 67% of answers
Best when you need automated, continuous runtime testing (CI/CD) across LLMs and multimodal models to catch prompt injection, extraction, or evasion during development; not a replacement for expert human pentests.
Named in 55% of answers
Named in 53% of answers
Named in 49% of answers
Source | Citation share |
|---|---|
 Top AI Red Teaming Companies to Watch in 2025https://www.mend.io/blog/best-ai-red-teaming-companies/ | 5% |
Best 7 tools for AI Red Teaming in 2025 to detect AI vulnerabilitieshttps://www.giskard.ai/knowledge/best-ai-red-teaming-tools-2025-comparison-features | 5% |
Best AI Red Teaming Tools (2026): 31 Tools Compared - Mindgard - Mindgardhttps://mindgard.ai/blog/best-tools-for-red-teaming | 4% |
Top 10 AI Red Teaming Providers in 2026https://www.mend.io/blog/best-ai-red-teaming-providers-top-5-vendors/ | 4% |
AI Red Teaming Services: Top 7 Platforms & Services in 2026https://www.mend.io/blog/best-ai-red-teaming-services-top-6-services/ | 4% |
 Top Open Source AI Red-Teaming and Fuzzing Tools in 2025 | Promptfoohttps://www.promptfoo.dev/blog/top-5-open-source-ai-red-teaming-tools-2025/ | 3% |
 Red Teaming Tools: Key Features & Top 8 Tools to Know in 2025 | CyCognitohttps://www.cycognito.com/learn/red-teaming/red-teaming-tools/ | 3% |
Top AI Red Teaming Solutions and How to Choosehttps://www.mend.io/blog/ai-red-teaming-solutions/ | 3% |
 Best AI Red Teaming Services: Top 6 Platforms and Services in 2025https://securityboulevard.com/2025/07/best-ai-red-teaming-services-top-6-platforms-and-services-in-2025/ | 2% |
 The 10 best AI red teaming tools of 2026 - London Business News | Londonlovesbusiness.comhttps://londonlovesbusiness.com/the-10-best-ai-red-teaming-tools-of-2026/ | 2% |
Running a red teaming exercise on an AI application requires a totally different approach than traditional network or infrastructure penetration testing . You aren't just looking for open ports or buffer overflows; you're testing for prompt injections, jailbreaks, data exfiltration via RAG (Retrieval-Augmented…
Running a red teaming exercise on an AI application requires a totally different approach than traditional network or infrastructure penetration testing . You aren't just looking for open ports or buffer overflows; you're testing for prompt injections, jailbreaks, data exfiltration via RAG (Retrieval-Augmented Generation), and unwanted agentic behaviors.
The "best" service or platform depends entirely on whether you want an automated continuous scanning platform , an open-source developer tool , or a full-service human-led expert engagement.
To help narrow down the ideal choice, tell me:
If you’re red-teaming a **production AI application**, I’d shortlist platforms based on what you actually need to test rather than looking for a single universal “best.” ### Shortlist Platform | Particularly useful for | What stands out
If you’re red-teaming a production AI application, I’d shortlist platforms based on what you actually need to test rather than looking for a single universal “best.”
| Platform | Particularly useful for | What stands out |
|---|---|---|
| HiddenLayer | Enterprise AI/agent security | Automated attack simulation, agentic AI testing, model security, runtime protection, and professional red-team services |
| Lakera | LLM applications & prompt injection | Direct/indirect attack simulations, jailbreak and prompt-injection testing, threat intelligence |
| Mindgard | Continuous automated red teaming | Automated testing of LLMs, GenAI applications and agents, with compliance-oriented reporting |
| Giskard | Developer-led testing | Useful for integrating AI testing into development workflows |
| Promptfoo | Build-your-own / CI testing | Open-source, developer-oriented adversarial testing and continuous evaluation |
Current 2026 comparisons show meaningful differences in coverage: for example, HiddenLayer and Mindgard cover agent testing, while some platforms concentrate more heavily on conventional LLM testing.
I'd look particularly closely at HiddenLayer's professional services. They explicitly offer expert AI red teaming in addition to their platform, including adversarial testing of agentic, generative and predictive AI applications and remediation guidance.
Lakera is worth evaluating, particularly if your application is an LLM/agent application where prompt injection, data leakage, jailbreaks and indirect attacks are major concerns. Its current platform advertises risk-based red teaming and both direct and indirect attack simulations.
Mindgard is another strong candidate if continuous automated testing and mapping findings to security/compliance frameworks are important.
For a serious exercise, I wouldn't limit the scope to “can I jailbreak the model?” A useful red team should probe the whole application:
Recent industry comparisons specifically distinguish model-level testing from agent/MCP testing, which is increasingly important for applications that can actually take actions rather than merely generate text.
If you tell me what your AI application looks like (e.g. RAG chatbot, customer-support agent, coding agent, autonomous workflow, healthcare/finance application, etc.), whether you want a SaaS platform vs. a human-led penetration test, and roughly your company size, I can narrow this to 2–3 appropriate options and give you a concrete evaluation checklist.
I'd look particularly closely at HiddenLayer's professional services. They explicitly offer expert AI red teaming in addition to their platform, including adversarial testing of agentic, generative and predictive AI applications and remediation guidance.
Lakera is worth evaluating, particularly if your application is an LLM/agent application where prompt injection, data leakage, jailbreaks and indirect attacks are major concerns. Its current platform advertises risk-based red teaming and both direct and indirect attack simulations.
Mindgard is another strong candidate if continuous automated testing and mapping findings to security/compliance frameworks are important.
For a serious exercise, I wouldn't limit the scope to “can I jailbreak the model?” A useful red team should probe the whole application:
If you’re looking to **actively red-team an AI application**—rather than just monitor it—I’d put **HiddenLayer** at the top of the enterprise shortlist right now. ### My shortlist | Platform | Best for | Why I’d consider it |
If you’re looking to actively red-team an AI application—rather than just monitor it—I’d put HiddenLayer at the top of the enterprise shortlist right now.
| Platform | Best for | Why I’d consider it |
|---|---|---|
| HiddenLayer | Comprehensive enterprise AI red teaming | Automated attack simulation, prompt-injection/jailbreak testing, continuous testing, vulnerability tracking, plus runtime defense. www.hiddenlayer.comdocs.hiddenlayer.ai |
| Lakera | GenAI apps and agents | Strong focus on prompt attacks, data leakage, jailbreaks, application-specific risks, and continuous red teaming. www.lakera.ai |
| Protect AI | ML/AI supply-chain security + testing | Particularly interesting if your exercise needs to cover models, ML pipelines, and AI/ML supply-chain risks in addition to the application itself. www.wecompareai.com |
| Cisco / Robust Intelligence | Large Cisco security environments | Combines AI firewall/security controls with AI testing; Robust Intelligence is now part of Cisco. guptadeepak.com |
| CalypsoAI | Governance-heavy enterprises | Worth evaluating where AI governance, compliance and red-team testing need to be managed together. guptadeepak.com |
For a serious enterprise exercise, HiddenLayer is probably the strongest single platform to evaluate first. Its current offering combines:
Its attack simulation is explicitly designed to run adversarial tests and measure the resulting security posture, rather than merely providing a collection of static test cases.
There is also a notable recent signal: HiddenLayer announced a $100M Series B in September 2026, with the company saying the funding will expand its agentic AI security and attack-simulation capabilities.
I'd put Lakera ahead if your application is primarily a GenAI/agent application exposed through APIs, and your biggest concern is attacks such as prompt injection, jailbreaks, data leakage and unauthorized actions. Lakera specifically offers risk-based red teaming with direct and indirect attack simulations and collaborative remediation.
For your exercise, I'd separate automated AI red teaming from a human-led adversarial assessment. The best program is usually:
automated continuous testing → expert human red team → remediation → regression testing
That matters particularly if your application has agents, tool/function calling, RAG, sensitive data, or consequential actions. Automated jailbreak testing alone won't adequately exercise the entire attack surface.
If you tell me whether your application is (1) a chatbot, (2) RAG application, (3) AI agent with tools, (4) coding agent, or (5) an API/model, I can give you a much more specific 2026 comparison—including pricing, deployment model, attack coverage, and which one I'd actually buy.
Choosing the "best" AI red teaming platform depends heavily on whether you need **automated continuous scanning** , an **open-source developer framework** , or a **human-led managed service** . Comprehensive AI red teaming requires testing for both safety (jailbreaks, toxicity) and security (prompt injection, data…
Choosing the "best" AI red teaming platform depends heavily on whether you need automated continuous scanning , an open-source developer framework , or a human-led managed service . Comprehensive AI red teaming requires testing for both safety (jailbreaks, toxicity) and security (prompt injection, data exfiltration, tool/agent misuse).
The top options on the market fall into distinct categories based on your organization's needs:
To help narrow down the right choice, tell me:
The “best” AI red teaming service depends on what you are testing: a chatbot, RAG application, AI agent with tools, a foundation model, or a regulated enterprise deployment. There is no single winner, but these are the platforms I would shortlist. [Expert…
The “best” AI red teaming service depends on what you are testing: a chatbot, RAG application, AI agent with tools, a foundation model, or a regulated enterprise deployment. There is no single winner, but these are the platforms I would shortlist.
| Platform | Best for | Strengths | Considerations |
|---|---|---|---|
| Mindgard | Enterprise continuous AI security testing | Automated adversarial testing, compliance-oriented reporting, agent testing, CI/CD workflows | Enterprise pricing; may require security-team involvement |
| Lakera | LLM apps needing both testing and runtime protection | Strong prompt injection/jailbreak testing ecosystem; connects testing with defenses | Best fit if you also want runtime AI protection |
| HiddenLayer | Large enterprises and model security | Broad AI security coverage beyond just prompts (model and supply-chain risks) | More oriented toward enterprise AI security programs |
| Giskard | Teams combining AI quality + security testing | Good governance, testing workflows, risk reporting | Less focused purely on offensive security |
| Promptfoo | Developers who want open-source CI/CD testing | Easy integration, automated adversarial tests, good for regression testing | Requires your team to operate and interpret results |
| Microsoft PyRIT | Security researchers building custom attacks | Powerful attack orchestration and research flexibility | More engineering-heavy |
Expert Insights Securetom## My recommendation by scenario
Choose a managed engagement from an AI security specialist or enterprise platform such as Mindgard, HiddenLayer, or Lakera. You should expect testing of:
Expert Insights NeuralTrust### 2. You are a startup or engineering team
Start with an automated framework:
Then consider a paid platform once you need compliance evidence or continuous monitoring.
Prioritize platforms that test agent behavior, not only prompts. Many older LLM scanners miss issues such as:
Platforms such as Mindgard, Lakera, HiddenLayer, and newer agent-focused security tools are better aligned with this threat model.
A strong AI red team exercise should map findings to frameworks such as:
The deliverable should not just be “here are bad prompts that worked”; it should include:
If I were selecting vendors for an enterprise evaluation, I would run a bake-off with:
Expert Insights SecuretomIf you tell me:
…I can narrow this to the 2–3 best options and suggest a red team test plan.
Choosing the "best" AI red teaming platform depends heavily on whether you need a **managed human service** , a **continuous automated platform** , or an **open-source engineering tool** to run pre-deployment scans.[](https://www.synack.com/blog/best-ai-red-teaming-tools/)…
Choosing the "best" AI red teaming platform depends heavily on whether you need a managed human service , a continuous automated platform , or an open-source engineering tool to run pre-deployment scans.
The market divides neatly into distinct categories based on your organization's technical depth, compliance needs, and architecture complexity:
To help narrow down the right choice, tell me:
If your goal is **a serious security red-team exercise against an AI application—not just a safety benchmark—I’d shortlist three vendors: HiddenLayer, Lakera, and Protect AI/Palo Alto Networks.** ### My top pick: HiddenLayer [hiddenlayer.com](https://www.hiddenlayer.com/platform?utm_source=chatgpt.com)
If your goal is a serious security red-team exercise against an AI application—not just a safety benchmark—I’d shortlist three vendors: HiddenLayer, Lakera, and Protect AI/Palo Alto Networks.
For a broad enterprise exercise, HiddenLayer is probably the strongest all-around choice. Its AI Attack Simulation capability automatically tests LLMs and agents for prompt injection, jailbreaks, data extraction/leakage, unsafe tool use, and other adversarial behaviors, with reporting and vulnerability tracking.
It also offers human-led AI red teaming, which is important because automated prompt attacks alone won't find every application-specific vulnerability. HiddenLayer says its professional services emulate real-world attacks against agentic, generative, and predictive AI systems.
I'd consider Lakera particularly seriously if your application is an LLM/agent with RAG, tools, or MCP integrations. Its red-teaming product explicitly covers application-specific risks, prompt injection, jailbreaks, data leakage, unauthorized actions, safety/compliance failures, and regression testing.
A particularly useful feature is its red-team SDK, which lets you run adversarial testing programmatically in CI/CD rather than treating red teaming as a one-off exercise.
If you're already heavily invested in Palo Alto's security ecosystem, I'd evaluate Protect AI alongside HiddenLayer. It's particularly compelling when the scope extends beyond the LLM application itself into ML model security, supply chain, model scanning, and MLSecOps. Current market comparisons place it alongside HiddenLayer and Cisco's AI security offerings in this broader category.
| Your situation | I'd start with |
|---|---|
| Comprehensive enterprise AI red team | HiddenLayer |
| LLM/agent/RAG/MCP security | Lakera |
| Continuous automated red teaming | HiddenLayer or Lakera |
| Human experts attacking the application | HiddenLayer Professional Services |
| AI/ML supply-chain + application security | Protect AI / Palo Alto |
| You want to integrate testing into CI/CD | Lakera |
| You want runtime protection as well as testing | HiddenLayer or Lakera |
One important distinction: don't buy a platform solely because it can generate lots of jailbreak prompts. For a meaningful red-team exercise, I'd want it to attack the entire application, including:
For an agentic AI application, I'd put considerably more weight on tool-use and authorization attacks than on generic "can I make the model say something bad?" jailbreak testing. HiddenLayer, for example, specifically advertises testing for unauthorized agent actions and unsafe tool use.
I'd actually do both automated and human red teaming:
Phase 1: automated continuous scanning → establish a vulnerability baseline Phase 2: expert human red team → attack the application's architecture and business logic Phase 3: remediation → fix/high-risk findings Phase 4: automated regression suite → make sure vulnerabilities don't return
If you tell me what your AI application looks like (e.g. chatbot, RAG app, coding agent, customer-service agent, internal copilot, autonomous agent, APIs/tools it can call, cloud stack, etc.), I can narrow this down to the best 2–3 platforms for your architecture and give you a concrete red-team test plan.
A particularly useful feature is its red-team SDK, which lets you run adversarial testing programmatically in CI/CD rather than treating red teaming as a one-off exercise.
If you're already heavily invested in Palo Alto's security ecosystem, I'd evaluate Protect AI alongside HiddenLayer. It's particularly compelling when the scope extends beyond the LLM application itself into ML model security, supply chain, model scanning, and MLSecOps. Current market comparisons place it alongside HiddenLayer and Cisco's AI security offerings in this broader category.
| Your situation | I'd start with |
|---|---|
| Comprehensive enterprise AI red team | HiddenLayer |
| LLM/agent/RAG/MCP security | Lakera |
| Continuous automated red teaming | HiddenLayer or Lakera |
| Human experts attacking the application | HiddenLayer Professional Services |
| AI/ML supply-chain + application security | Protect AI / Palo Alto |
| You want to integrate testing into CI/CD | Lakera |
| You want runtime protection as well as testing | HiddenLayer or Lakera |
One important distinction: don't buy a platform solely because it can generate lots of jailbreak prompts. For a meaningful red-team exercise, I'd want it to attack the entire application, including:
For an agentic AI application, I'd put considerably more weight on tool-use and authorization attacks than on generic "can I make the model say something bad?" jailbreak testing. HiddenLayer, for example, specifically advertises testing for unauthorized agent actions and unsafe tool use.
I'd actually do both automated and human red teaming:
Choosing the "best" AI red teaming service or platform depends heavily on whether you want an **automated software platform** for continuous scanning, a **managed service** with human experts, or a **free open-source framework** for your internal engineering team to…
Choosing the "best" AI red teaming service or platform depends heavily on whether you want an automated software platform for continuous scanning, a managed service with human experts, or a free open-source framework for your internal engineering team to run.
The industry is generally split into four distinct tiers:
Which approach should you pick?
To help narrow down the right choice, tell me a bit more about your application:
Yes. The best choice depends heavily on whether you want **a one-time expert red-team engagement** or **a platform your security/engineering team can run continuously**. For an enterprise AI application in 2026, my shortlist would be: Platform / service | Best for | My take
Yes. The best choice depends heavily on whether you want a one-time expert red-team engagement or a platform your security/engineering team can run continuously.
For an enterprise AI application in 2026, my shortlist would be:
| Platform / service | Best for | My take |
|---|---|---|
| HiddenLayer | Enterprise-grade AI red teaming + security | Best overall for a serious security program |
| Lakera | GenAI/agent red teaming + runtime protection | Best for LLM apps and agents |
| Mindgard | Continuous automated AI security testing | Excellent for DevSecOps-style testing |
| NVIDIA garak | Open-source vulnerability testing | Best free starting point |
| Microsoft PyRIT | Building customized AI red-team campaigns | Excellent for security research/internal teams |
HiddenLayer is probably where I'd start if you're asking for the best professional AI red-teaming capability, rather than simply the cheapest scanner.
Its platform includes automated AI attack simulation, and it can test LLMs, agents and traditional ML systems for things such as prompt injection, jailbreaks, extraction and other adversarial behaviors. It also offers an actual professional-services engagement where its researchers conduct AI red teaming for you.
I'd also put Lakera very high on the list. Its red-team offering specifically covers GenAI applications, including direct and indirect prompt attacks, and it has capabilities around agents and MCP. Lakera also combines red teaming with runtime protection, which is attractive if you want to find vulnerabilities and then continuously defend against them.
I'd actually run garak regardless of which commercial service you choose. It's free/open source and specifically designed to probe LLMs for jailbreaks, prompt injection, data leakage, hallucination, misinformation and other weaknesses.
And PyRIT is particularly useful if your security team wants to build customized attack scenarios rather than relying entirely on a vendor's predefined tests. Its ecosystem can also incorporate Garak-style scenarios.
Rather than buying a tool and declaring the application "red teamed," I'd use a three-layer approach:
This is especially important if your application uses RAG, tools/function calling, browsing, memory, MCP, or autonomous agents. Testing the underlying LLM alone can miss vulnerabilities in the application architecture.
For example, I'd explicitly test:
If you tell me what your AI application looks like (e.g. customer-facing chatbot, RAG application, coding agent, autonomous agent, healthcare/finance application, internal copilot, etc.), I can narrow this down to the top 2–3 vendors and tell you exactly what I'd test and how I'd structure the red-team exercise.
Its platform includes automated AI attack simulation, and it can test LLMs, agents and traditional ML systems for things such as prompt injection, jailbreaks, extraction and other adversarial behaviors. It also offers an actual professional-services engagement where its researchers conduct AI red teaming for you.
I'd also put Lakera very high on the list. Its red-team offering specifically covers GenAI applications, including direct and indirect prompt attacks, and it has capabilities around agents and MCP. Lakera also combines red teaming with runtime protection, which is attractive if you want to find vulnerabilities and then continuously defend against them.
I'd actually run garak regardless of which commercial service you choose. It's free/open source and specifically designed to probe LLMs for jailbreaks, prompt injection, data leakage, hallucination, misinformation and other weaknesses.
And PyRIT is particularly useful if your security team wants to build customized attack scenarios rather than relying entirely on a vendor's predefined tests. Its ecosystem can also incorporate Garak-style scenarios.
Rather than buying a tool and declaring the application "red teamed," I'd use a three-layer approach:
Choosing the best AI red teaming service or platform depends heavily on whether you need **automated continuous scanning** , an **open-source developer toolkit** , or a **managed human-led service**. The leading platforms and services are categorized below by how they fit different enterprise needs: 1. Specialized…
Choosing the best AI red teaming service or platform depends heavily on whether you need automated continuous scanning , an open-source developer toolkit , or a managed human-led service.
The leading platforms and services are categorized below by how they fit different enterprise needs:
How to Choose the Right Approach
To help narrow down the best choice, tell me:
