AI Red Teaming Tools Compared: The 2026 Guide

AI security incidents jumped 540% year-over-year in 2025 according to HackerOne, and 97% of enterprises expect an AI agent security incident within the next year. If you’re deploying LLMs or AI agents in production, red teaming isn’t optional — it’s a question of which tool fits your needs.

This guide compares seven AI red teaming tools across features, pricing, approach, and best-fit use cases. We’ve included our own product (AI Vyuh Security) for transparency, but the goal here is to help you make the right choice — even if that’s not us.

Quick Comparison Table

Tool	Type	Pricing	Open Source	Best For
Mindgard	Platform (automated + manual)	Enterprise custom pricing	No	Large enterprises, continuous automated + manual red teaming
Protect AI	Platform + community tools	Enterprise; Guardian is commercial, some OSS tools	Partial (Huntr, ModelScan)	ML pipeline security, model supply chain
Adversa AI	Platform (automated)	Enterprise custom pricing	No	Adversarial robustness testing, computer vision + NLP
Giskard	Testing framework	Free (open source); enterprise add-ons	Yes (Apache 2.0)	ML teams wanting automated bias/security testing in CI/CD
NVIDIA Garak	Vulnerability scanner	Free (open source)	Yes (Apache 2.0)	LLM vulnerability scanning, automated probe generation
Promptfoo	LLM eval + red teaming	Free (open source); cloud plans available	Yes (MIT)	Developers testing LLM apps, prompt-level red teaming
AI Vyuh Security	Service + automation	Audits from $8K; red teams from $16K	No	Startups to mid-market needing audit-ready reports, agentic AI focus

Detailed Breakdown

Mindgard

Mindgard offers an enterprise AI security testing platform that combines automated vulnerability scanning with manual red teaming services. Founded by academic researchers from Lancaster University, they focus on adversarial ML robustness and have built a threat intelligence database of AI-specific attack patterns.

Key strengths:

• Continuous automated testing with 100+ attack techniques
• Threat intelligence feed covering emerging AI vulnerabilities
• Integration with CI/CD pipelines for shift-left security
• Compliance mapping for EU AI Act and NIST AI RMF

Limitations:

• Enterprise pricing puts it out of reach for startups and SMBs
• Primarily focused on model robustness — less depth on agentic attack surfaces (MCP, tool poisoning, multi-agent lateral movement)
• Requires platform onboarding and integration effort

Best for: Large enterprises with dedicated AI security teams who need continuous automated testing alongside periodic manual assessments.

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Protect AI

Protect AI takes a platform approach to ML security, covering the entire ML lifecycle from model supply chain to production monitoring. Their acquisition of Huntr (a bug bounty platform for AI/ML) gives them a community-driven vulnerability database. ModelScan (open source) checks serialized models for unsafe operations.

Key strengths:

• ML supply chain security (model scanning, dependency checks)
• Guardian — commercial ML security platform with policy enforcement
• Huntr community provides crowd-sourced vulnerability research
• ModelScan is free and open source for basic model file scanning

Limitations:

• Strongest on ML pipeline security, less focused on LLM prompt-level attacks
• Enterprise pricing model — not transparent
• The breadth of the platform can mean complexity for teams that just need red teaming

Best for: Organizations with mature ML pipelines who need security across the entire model lifecycle, not just inference-time attacks.

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Adversa AI

Adversa AI specializes in adversarial robustness testing for AI models. Their platform runs automated adversarial attacks against both NLP and computer vision models to find edge cases and failure modes.

Key strengths:

• Strong adversarial attack research pedigree
• Covers both vision and language models
• Automated adversarial example generation
• Academic-grade robustness metrics

Limitations:

• Less focus on production LLM deployments (prompt injection, jailbreaks)
• Primarily a testing tool, not a red teaming service with human expertise
• Enterprise pricing — no self-serve tier
• Limited coverage for agentic AI and multi-model architectures

Best for: ML teams building safety-critical models (autonomous systems, medical AI, content moderation) where adversarial robustness is the primary concern.

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Giskard

Giskard is an open-source ML testing framework that automates quality, bias, and security testing for ML models and LLM applications. It generates test suites automatically and integrates into CI/CD pipelines.

Key strengths:

• Open source (Apache 2.0) — free to self-host
• Automatic test generation from model metadata
• Covers bias, performance, and security in one framework
• Python-native, integrates with Hugging Face, LangChain, and sklearn
• Active community and good documentation

Limitations:

• Primarily a testing/QA framework — not adversarial red teaming
• Security coverage is broad but not deep (won’t simulate sophisticated multi-step attacks)
• No professional services or human red teaming component
• Limited agentic AI coverage

Best for: ML engineering teams who want automated quality and security testing as part of their development workflow. Think of it as “pytest for ML” with security checks included.

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NVIDIA Garak

Garak (Generative AI Red-teaming and Assessment Kit) is NVIDIA’s open-source LLM vulnerability scanner. It probes LLMs with a library of known attack techniques and reports vulnerabilities.

Key strengths:

• Free and open source (Apache 2.0)
• Large library of probes: prompt injection, jailbreaks, data leakage, encoding attacks, and more
• Modular architecture — easy to add custom probes
• Can test any LLM endpoint (OpenAI, Anthropic, Hugging Face, local models)
• Active development backed by NVIDIA’s AI research team

Limitations:

• Automated scanning only — no human expertise or judgment
• Produces raw vulnerability data, not audit-ready reports
• No compliance mapping (OWASP, NIST, EU AI Act)
• No coverage for agentic workflows, MCP security, or multi-agent systems
• Can generate false positives that require manual review

Best for: Security engineers and developers who want automated LLM vulnerability scanning in CI/CD. Excellent as a first line of defense, but not sufficient as a standalone red teaming solution for production-critical systems.

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Promptfoo

Promptfoo is an open-source tool for evaluating and red-teaming LLM applications. It started as an LLM eval framework and expanded to include adversarial testing, making it one of the most developer-friendly options available.

Key strengths:

• Free and open source (MIT license)
• Red teaming plugin generates adversarial prompts automatically
• Eval + security in one tool — test both quality and safety
• Supports prompt injection, jailbreaks, PII leakage, harmful content, and more
• Cloud version available for teams wanting managed infrastructure
• Excellent documentation and active community

Limitations:

• Focused on prompt-level attacks — limited depth on model-level or infrastructure-level vulnerabilities
• No professional services or manual red teaming
• Cloud pricing not publicly listed (contact sales)
• Less suited for non-LLM ML models (vision, tabular, etc.)

Best for: Development teams building LLM applications who want to integrate security testing into their eval pipeline. The best developer experience of any tool on this list.

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AI Vyuh Security

AI Vyuh Security (that’s us) offers AI agent security assessments combining automated scanning with human-led red teaming. Our focus is on agentic AI systems — multi-agent architectures, MCP deployments, and tool-using agents — which is the fastest-growing and least-tested attack surface in production AI.

Key strengths:

• Specialized in agentic AI security (MCP, multi-agent, tool-use)
• Combines automated scanning with human adversarial expertise
• Audit-ready reports mapped to OWASP Top 10 for AI Agents, NIST AI RMF, EU AI Act
• Transparent pricing: audits from $8K, red teams from $16K, continuous from $5K/month
• 48-hour report delivery for standard engagements

Limitations:

• Smaller team than enterprise vendors — limited capacity for concurrent large engagements
• No self-serve automated scanning platform (yet)
• Less depth on traditional ML model robustness (adversarial examples for vision models)
• Newer market entrant compared to Mindgard or Protect AI

Best for: Startups and mid-market companies deploying AI agents in production who need professional-grade security assessments with transparent pricing and fast turnaround.

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How to Choose

The right tool depends on where you are in your AI security journey:

1. Just starting out? Start with Promptfoo or Garak in your CI/CD pipeline. They’re free, well-documented, and catch the most common LLM vulnerabilities.

2. Need compliance-ready reports? You need a professional service. Automated tools don’t produce reports that satisfy auditors, compliance teams, or enterprise customers asking about your AI security posture.

3. Running AI agents in production? The agentic attack surface (MCP, tool chaining, multi-agent communication) is poorly covered by most automated tools. Look for vendors with specific agentic AI expertise.

4. Enterprise-scale ML operations? Platforms like Mindgard or Protect AI offer the breadth and integration depth that large organizations need.

5. Budget-conscious but need professional testing? AI Vyuh Security offers engagements starting at $8,000 — significantly below the enterprise vendor price floor.

The strongest security posture combines automated scanning (Garak, Promptfoo, or Giskard in CI/CD) with periodic human-led red teaming. No single tool covers everything.

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Methodology

This comparison is based on publicly available information as of April 2026, including vendor documentation, published pricing, open-source repositories, and industry reports. Where pricing is listed as “enterprise” or “custom,” we note it as such rather than speculating. We’ve included our own product and tried to represent its limitations honestly — if you think we’ve been unfair to any vendor (including ourselves), let us know.

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