Key takeaways
- Top pick: Mindgard, best for: Security teams that need continuous red teaming of AI agents and apps. Mindgard says its research has produced more than 150 publicly disclosed AI vulnerabilities, and the company spun out of more than a decade of AI security research at Lancaster University.
- Also near the top: HiddenLayer, best for: CISOs and security teams; and Palo Alto Networks Prisma AIRS, best for: Enterprises already standardised on Palo Alto Networks security.
- Free to start: Repello AI and Confident AI.
- How it was ranked: 10 AI tools compared. Each verdict is below.
Mindgard is the best AI red teaming platform for most security and AI teams because it runs continuous, automated attacks against models, apps and agents, backed by research that has publicly disclosed more than 150 AI vulnerabilities.
HiddenLayer suits enterprises that want red teaming inside a full AI security platform,
Giskard fits teams that want open-source testing plus agent audits, and
Confident AI is the cheapest start with a free plan.
Top 10 AI Red Teaming Platforms, compared| RANK | ENTRY | BEST FOR | SCORE | FROM |
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| 1 |  MindgardMindgard says its research has produced more than 150 publicly disclosed AI vulnerabilities, and the company spun out of more than a decade of AI security research at Lancaster University. | Security teams that need continuous red teaming of AI agents and apps | 96.2 | Custom |
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| 2 |  HiddenLayerHiddenLayer says it holds more than 30 issued patents and has disclosed more than 50 CVEs through its research, and it has raised a $100M Series B. | CISOs and security teams | 94.8 | – |
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| 3 |  Palo Alto Networks Prisma AIRSPalo Alto Networks sells AI Red Teaming as one module of Prisma AIRS, next to model security scanning, runtime security and agent security. | Enterprises already standardised on Palo Alto Networks security | 93.5 | Custom |
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| 4 |  Cisco AI DefenseCisco launched AI Defense in January 2025, building on technology from an AI security startup it acquired in 2024. | Enterprises running Cisco security that want AI validation and runtime protection | 92.1 | Custom |
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| 5 |  GiskardGiskard's agent auditing runs more than 50 automated adversarial probes, including multi-turn attacks, and its case studies include a customer-facing banking chatbot for BPCE. | AI security and quality teams | 90.7 | – |
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| 6 |  Repello AIRepello says ARTEMIS tests AI systems against more than 15 million evolving attack patterns. | Teams testing multimodal AI apps and agents | 89.4 | Free red teaming scan; plans Custom |
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| 7 |  Confident AIConfident AI's Starter plan costs $200 a month and its Team plan $2,000 a month, with a free plan limited to two seats and one project. | Engineering teams that want red teaming alongside LLM evaluation in CI | 88.0 | Free plan; paid from $200/month |
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| 8 |  StraikerStraiker says it was listed as a Sample Vendor for agentic AI security in two 2026 Gartner Hype Cycle reports. | Security teams testing tool-using AI agents and MCP integrations | 86.8 | Custom |
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| 9 |  Gray Swan AIGray Swan says its Arena network has more than 15,000 adversarial researchers who have generated over three million attack attempts. | Model builders and enterprises that want attacks not yet seen in public datasets | 85.5 | Custom |
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| 10 |  Pillar SecurityPillar Security publishes the SAIL framework for securing the AI development lifecycle and offers cloud, self-hosted and hybrid deployment. | Organisations securing internal AI agents end to end | 84.3 | Custom |
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How to choose
Start from what you need it for. Where each entry fits best:
- Mindgard (#1): Security teams that need continuous red teaming of AI agents and apps
- HiddenLayer (#2): CISOs and security teams
- Palo Alto Networks Prisma AIRS (#3): Enterprises already standardised on Palo Alto Networks security
- Cisco AI Defense (#4): Enterprises running Cisco security that want AI validation and runtime protection
- Giskard (#5): AI security and quality teams
- Repello AI (#6): Teams testing multimodal AI apps and agents
- Confident AI (#7): Engineering teams that want red teaming alongside LLM evaluation in CI
- Straiker (#8): Security teams testing tool-using AI agents and MCP integrations
- Gray Swan AI (#9): Model builders and enterprises that want attacks not yet seen in public datasets
- Pillar Security (#10): Organisations securing internal AI agents end to end
Each entry's full verdict is below.
The ranking, in detail
01
Automated, attack-led red teaming for AI models, apps and agents
Mindgard is an AI security platform that acts as an autonomous red teamer, running reconnaissance and continuous attacks against AI models, agents and applications to find jailbreaks, prompt injections and other exploitable paths. It adds AI assessment, runtime protection and governance reporting, and its attack library is fed by the company's own vulnerability research.
From Custom
PROS- Continuous, automated attacks rather than one-off tests
- Attack library informed by original vulnerability research
CONS- Price not published
- Enterprise focus may exceed what a small team needs
- Best for
- Security teams that need continuous red teaming of AI agents and apps
- Where it falls short
- Enterprise focus may exceed what a small team needs.
- Pick something else if
- you are a small team.
96.2
02
The most comprehensive security platform for AI
HiddenLayer's platform covers AI discovery, AI supply chain security with model scanning, AI attack simulation for red teaming and runtime security that blocks prompt injection, data leakage and unsafe behaviour. Its red teaming continuously tests AI systems with adversarial simulations and plugs into CI/CD, MLOps pipelines and SIEM and SOAR tools.
PROS- HiddenLayer deploys in minutes without exposing intellectual property or customer data.
- The AI tool integrates natively with existing CI/CD and MLOps stacks.
- Best for
- CISOs and security teams
- Where it falls short
- It requires existing CI/CD and MLOps stacks for integration.
- Pick something else if
- you lack established CI/CD or MLOps infrastructure.
94.8
03
AI red teaming inside Palo Alto Networks' AI security platform
Prisma AIRS is Palo Alto Networks' AI security platform, and its AI Red Teaming module simulates real-world attacks to find weaknesses in AI agents and applications before they reach production. The platform also scans third-party models for tampering and malicious code, enforces runtime safeguards and secures AI agents, alongside an AI gateway.
From Custom
PROS- Red teaming, model scanning and runtime controls from one vendor
- Fits existing Palo Alto Networks security operations
CONS- Price not published
- Most value for existing Palo Alto Networks customers
- Best for
- Enterprises already standardised on Palo Alto Networks security
- Where it falls short
- The price is not published.
- Pick something else if
- you need to know the cost upfront.
93.5
04
Algorithmic red teaming and AI guardrails from Cisco
Cisco AI Defense includes AI model and application validation, which runs automated algorithmic red teaming to find jailbreaks, prompt injection, data leakage and harmful output, plus runtime guardrails and discovery of AI assets in use. It is sold as part of Cisco's security portfolio and works with Cisco's network and cloud security products.
From Custom
PROS- Automated validation of models and AI apps
- Pairs testing with runtime guardrails
CONS- Price not published
- Best suited to existing Cisco security customers
- Best for
- Enterprises running Cisco security that want AI validation and runtime protection
- Where it falls short
- It is best suited to existing Cisco security customers.
- Pick something else if
- you do not use Cisco security products.
92.1
05
Get your AI audited. Agentic AI risks: Handled.
Giskard is a French AI testing company with an open-source library and a commercial platform for continuous red teaming, evaluation and guardrails of LLM agents. Its agent audits run automated adversarial probes, including multi-turn attacks, to catch prompt injection, data leaks, inappropriate content, hallucinations and wrongful refusals.
PROS- The AI tool employs proprietary research to detect harmful content and prompt injections.
- The AI tool offers end-to-end encryption and GDPR adherence.
- Best for
- AI security and quality teams
- Where it falls short
- It currently supports only conversational text-to-text agents rather than all LLM types.
- Pick something else if
- you need support for non-conversational or multimodal LLM architectures.
90.7
06
Automated red teaming for text, image and audio AI apps
Repello AI's ARTEMIS product autonomously red teams AI applications and agents across text, image and audio inputs, and maps findings to OWASP, NIST and MITRE frameworks. The wider platform adds AI asset discovery with an AI bill of materials, ARGUS runtime defence and security for MCP servers and coding agents.
Free red teaming scan; plans Custom
PROS- Covers text, image and audio attacks
- Free scan to try on your own agent
CONS- Plan prices not published
- Younger company than the large security vendors
- Best for
- Teams testing multimodal AI apps and agents
- Where it falls short
- Plan prices are not published, so you cannot determine costs upfront.
- Pick something else if
- you need transparent pricing before committing.
89.4
07
LLM evaluation platform with red teaming and open-source DeepTeam
Confident AI is an LLM evaluation and observability platform whose red teaming product stress-tests LLM apps against adversarial attacks and produces shareable risk assessment reports. It maintains the open-source DeepTeam red teaming framework and DeepEval evaluation framework, so engineers can test locally and move to the hosted platform.
Free plan; paid from $200/month
PROS- Free plan and published pricing
- Open-source DeepTeam framework for local testing
CONS- Red teaming sits beside evaluation rather than in a dedicated security tool
- Free plan limited to two seats and one project
- Best for
- Engineering teams that want red teaming alongside LLM evaluation in CI
- Where it falls short
- The free plan restricts users to two seats and one project.
88.0
08
Agent-focused red teaming and runtime security
Straiker's Ascend AI product continuously red teams AI agents across their tools, MCP servers and workflows to expose attack paths before production, while Defend AI blocks prompt injection, data leakage and tool manipulation at runtime. Its research team publishes threat reports on real-world AI-assisted attacks.
From Custom
PROS- Tests agents across tools, MCP servers and workflows
- Runtime protection from the same vendor
CONS- Price not published
- Focused on agents more than standalone models
- Best for
- Security teams testing tool-using AI agents and MCP integrations
- Where it falls short
- It focuses on agents rather than standalone models.
- Pick something else if
- you need to test standalone models.
86.8
09
Red teaming software backed by a large adversarial research network
Gray Swan AI sells Shade, automated software that uses LLM-based adversarial agents to attack models, agents and guardrails in their deployment context, and Cygnal, which blocks adversarial inputs and unsafe outputs in real time. It also runs the Arena, a competition network where researchers find new attacks, and offers adversarial evaluations for model builders.
From Custom
PROS- Attack data from a large crowd of red teamers
- Runtime filter available from the same vendor
CONS- Price not published
- Narrower product range than full AI security platforms
- Best for
- Model builders and enterprises that want attacks not yet seen in public datasets
- Where it falls short
- It offers a narrower product range than full AI security platforms.
- Pick something else if
- you need a comprehensive suite of AI security tools.
85.5
10
Agentic red teaming within an AI security and governance platform
Pillar Security's platform covers AI discovery and posture, red teaming and attack surface exposure, runtime guardrails, agent endpoint security and governance. Its agentic red teaming probes for unauthorised agent actions, excessive permissions, skill and MCP poisoning and malicious code execution, and it deploys in the cloud, self-hosted or hybrid.
From Custom
PROS- Self-hosted and hybrid deployment options
- Red teaming tied to posture management and runtime controls
CONS- Price not published
- Red teaming is one module of a wider platform
- Best for
- Organisations securing internal AI agents end to end
- Where it falls short
- It does not publish pricing information.
- Pick something else if
- you need transparent upfront costs.
84.3
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Frequently asked questions
How much do AI red teaming platforms cost?
Most enterprise platforms, including Mindgard, HiddenLayer and Palo Alto Networks Prisma AIRS, are priced on request. Confident AI publishes plans from free to $200 and $2,000 a month, and Giskard's open-source library and Repello AI's red teaming scan let you test at no cost first.
How do I choose an AI red teaming tool?
Match the tool to what you ship: agents with tools and MCP servers need agent-aware testing such as Straiker or Pillar Security, while chat apps and models are well covered by Mindgard or Giskard. Check that findings map to OWASP, NIST or MITRE, as Repello AI does, and decide whether you also want runtime guardrails from the same vendor.
What is the best AI red teaming tool for a small team?
Confident AI is the easiest start because it has a free plan, published paid tiers and the open-source DeepTeam framework. Giskard also offers an open-source library, and Repello AI runs a free red teaming scan on your agent.
What is the difference between AI red teaming and AI guardrails?
Red teaming attacks your AI before release to find jailbreaks, prompt injections and unsafe outputs, while guardrails filter inputs and outputs at runtime. Many vendors sell both, including HiddenLayer, Cisco AI Defense and Gray Swan AI with its Cygnal filter.
Can AI red teaming tools test multi-turn and agent attacks?
Yes, the stronger tools go beyond single prompts. Giskard runs multi-turn adversarial probes, Straiker tests agents across tools and MCP servers, and Mindgard runs continuous attacks against AI agents and systems.
Sources
Where we checked each company's facts, and when.
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