Best AI red teaming and LLM security testing tool
4 models · updated 2026-07-13
The verdict
Promptfoo leads — 2 of 4 models rank Promptfoo the top pick.
Not unanimous: Claude picks garak; Grok picks garak.
As of 2026-07-13, ChatGPT, Claude, Gemini and Grok collectively rank Promptfoo #1 for ai red teaming and llm security testing tool on ModelsAgree by aggregate score. The models' case: Best overall mix of broad attack coverage, agent/RAG testing, configurable multi-turn strategies, CI/CD automation, extensibility, and accessible open-source tooling. The models' main caveat: Make advanced red-team generation fully local and open-source instead of relying partly on hosted services. The strongest alternative is garak — The de facto open-source LLM vulnerability scanner — a huge, actively maintained probe library covering jailbreaks, prompt injection, data/PII. Not unanimous: Claude picks garak; Grok picks garak. Source: https://modelsagree.com/best/best-ai-red-teaming-tool (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #1Claude #3Gemini #1Grok #3
Best overall mix of broad attack coverage, agent/RAG testing, configurable multi-turn strategies, CI/CD automation, extensibility, and accessible open-source tooling
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GPT Best overall mix of broad attack coverage, agent/RAG testing, configurable multi-turn strategies, CI/CD automation, extensibility, and accessible open-source tooling
Gemini Leading developer-centric, CLI-first framework that treats adversarial testing like unit tests with excellent CI/CD integration and side-by-side prompt matrix evaluation.
Claude The best fit for developers hardening their own LLM application — config-driven, drops into CI/CD, auto-generates adversarial test cases mapped to the OWASP LLM Top 10 and other frameworks, and provides continuous regression testing with a large plugin set; open-source with a commercial tier, it delivers the most value per hour for app teams.
Grok Strong CI/CD integration, 50+ vulnerability plugins tailored to app-specific testing (RAG/agents), easy YAML/config-driven red teaming with auto-generated attacks; widely adopted for developer workflows and regression testing in production pipelines.
Where it falls shortper GPT Make advanced red-team generation fully local and open-source instead of relying partly on hosted services
per Claude Its strength is testing your own configured app (it needs that context) rather than open-ended model-vuln research; deep offensive discovery still favors garak/PyRIT.
per Gemini Primarily designed for pre-deployment, single-turn testing and lacks native live production monitoring or runtime guardrails.
per Grok More evaluation/red-team hybrid than pure deep offensive probing; less exhaustive on cutting-edge model-level or highly adaptive agentic attacks than specialized scanners.
- 2GPT #5Claude #1Gemini #2Grok #1
The de facto open-source LLM vulnerability scanner — a huge, actively maintained probe library covering jailbreaks, prompt injection, data/PII leakage, toxicity, encoding attacks, and package hallucination, runnable against virtually any model (HuggingFace, OpenAI, local, custom REST) with a single CLI command; free, model-agnostic, and NVIDIA-backed, it's the closest thing to "nmap for LLMs" and the fastest way for a security engineer to get real coverage of known attack classes. Ranked #1 on the assumption the typical practitioner is a security/ML engineer wanting broad, off-the-shelf coverage rather than a managed service.
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Claude The de facto open-source LLM vulnerability scanner — a huge, actively maintained probe library covering jailbreaks, prompt injection, data/PII leakage, toxicity, encoding attacks, and package hallucination, runnable against virtually any model (HuggingFace, OpenAI, local, custom REST) with a single CLI command; free, model-agnostic, and NVIDIA-backed, it's the closest thing to "nmap for LLMs" and the fastest way for a security engineer to get real coverage of known attack classes. Ranked #1 on the assumption the typical practitioner is a security/ML engineer wanting broad, off-the-shelf coverage rather than a managed service.
Grok Broadest probe library (120+ categories covering prompt injection, jailbreaks, hallucination, data leakage, toxicity, encoding attacks) with generators/detectors for systematic scanning of models and dialog systems; battle-tested, extensible, high public evidence of use in research/security workflows; strong for model-level and basic app testing.
Gemini The standard "nmap of LLMs" that provides rapid, automated baseline vulnerability scanning across hundreds of pre-built jailbreak, toxicity, and data leakage probes.
GPT Mature open-source LLM vulnerability scanner with a large probe ecosystem, reproducible testing, broad model support, and strong value for baseline security assessments
Where it falls shortper GPT Expand beyond model-centric probes into stateful end-to-end agent, tool, and RAG exploitation
per Claude It's a static probe scanner — strong on cataloged attack patterns but weaker on adaptive multi-turn/agentic attacks and app-specific business-logic flaws, and triage/reporting is DIY, so it's not for teams wanting a polished dashboard or continuous managed testing.
per Gemini Lacks the ability to simulate complex multi-turn agent interactions and relies heavily on static detectors that can miss subtle exploits.
per Grok Primarily stateless/single-turn focused model scanning (less native multi-turn/agentic depth without heavy customization); not ideal for full production app pipelines or teams needing CI/CD regression without extra integration.
- 3GPT #4Claude #2Gemini #3Grok #2
The strongest automation/orchestration framework for red teaming, built and battle-tested by Microsoft's AI Red Team; its target/converter/scorer architecture lets you script automated, multi-turn adversarial campaigns and extend to novel attacks, making it the tool of choice when you outgrow canned scanners and need custom offensive tooling at scale.
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Claude The strongest automation/orchestration framework for red teaming, built and battle-tested by Microsoft's AI Red Team; its target/converter/scorer architecture lets you script automated, multi-turn adversarial campaigns and extend to novel attacks, making it the tool of choice when you outgrow canned scanners and need custom offensive tooling at scale.
Grok Excellent for agentic/multi-turn/multi-modal attacks (Crescendo, TAP, 50+ datasets, 70+ converters, orchestrators); built from real Microsoft red team experience on Copilot-scale systems; flexible Python framework for custom campaigns with strong orchestration and scoring.
Gemini Microsoft-backed Python framework that automates complex, stateful, multi-turn "AI-vs-AI" adversarial attack strategies for deep security research.
GPT Highly flexible, model-agnostic framework for orchestrating multi-turn and multimodal attacks, custom targets, converters, scorers, and human-led security research
Where it falls shortper GPT Add a polished turnkey interface and simpler production CI workflow
per Claude It's a library, not a turnkey scanner — real Python and adversarial-ML skill are needed to get value, so it's the wrong pick for non-developers or anyone wanting one-click results.
per Gemini Requires significant engineering effort and custom scripting, making it overkill and too complex for typical developers.
per Grok Research-oriented library requiring significant engineering skill to wire into workflows/CI/CD; steeper curve for non-expert practitioners compared to more turnkey options.
- 4GPT #2Claude #4Gemini #4Grok —
Strongest enterprise offensive-security platform for adaptive agent attacks, infrastructure reconnaissance, multimodal testing, exploit validation, and compliance-ready reporting
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GPT Strongest enterprise offensive-security platform for adaptive agent attacks, infrastructure reconnaissance, multimodal testing, exploit validation, and compliance-ready reporting
Claude The strongest dedicated commercial platform — continuous, automated AI red teaming ("DAST for AI") with offensive-research heritage, covering models, RAG, and agentic apps with managed attack libraries, triage, and reporting that enterprises need for repeatable assurance and compliance evidence.
Gemini Enterprise platform offering automated red teaming, reconnaissance, vulnerability mapping to NIST/OWASP, and integrated runtime threat detection.
Where it falls shortper GPT Offer transparent self-serve pricing and a meaningful community edition
per Claude Commercial and enterprise-priced with less transparency than open tools; overkill for individuals or small teams who can get most of the coverage free from garak plus Promptfoo.
per Gemini A high-cost commercial solution that is not suitable for practitioners seeking local-first or open-source tooling.
- 5GPT #3Claude #5Gemini —Grok —
Excellent automated scans for agents and RAG systems, strong hallucination and business-risk testing, actionable reports, and an approachable SDK-plus-dashboard workflow
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GPT Excellent automated scans for agents and RAG systems, strong hallucination and business-risk testing, actionable reports, and an approachable SDK-plus-dashboard workflow
Claude The most approachable open-source vulnerability scanner for QA/ML teams — a Python scan() detects hallucination, prompt injection, harmful content, and robustness/bias issues with clean reports, plus RAG-specific evaluation (RAGET), bridging quality assurance and security for teams without a dedicated red teamer. Near-tie for this slot with DeepTeam (see MISSED), which is more purely offensive but younger.
Where it falls shortper GPT Deepen adaptive multi-step exploitation of tool-using agents
per Claude Its adversarial/offensive depth is shallower than garak or PyRIT — more a safety-and-quality scanner than a serious offensive tool, so it won't satisfy dedicated red teamers.
- 6GPT —Claude —Gemini —Grok #4
Clean, actively maintained open-source framework with strong OWASP/NIST alignment, multi-turn/agent support, and 40+ vulnerabilities; simple Python API for quick integration into eval workflows; pairs well with platform for observability.
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Grok Clean, actively maintained open-source framework with strong OWASP/NIST alignment, multi-turn/agent support, and 40+ vulnerabilities; simple Python API for quick integration into eval workflows; pairs well with platform for observability.
Where it falls shortper Grok Younger ecosystem with comparatively less probe depth/breadth than Garak/PyRIT; best as framework rather than standalone enterprise platform without add-ons.
- 7GPT —Claude —Gemini —Grok #5
All-in-one platform merging automated red teaming (50+ vulns, OWASP/NIST/EU AI Act) with evals and production observability; enables continuous testing/regression in one workflow for teams needing managed lifecycle coverage.
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Grok All-in-one platform merging automated red teaming (50+ vulns, OWASP/NIST/EU AI Act) with evals and production observability; enables continuous testing/regression in one workflow for teams needing managed lifecycle coverage.
Where it falls shortper Grok Commercial/enterprise pricing focus (less accessible for pure open-source or budget solo practitioners); platform dependency vs. lightweight framework flexibility.
- 8GPT —Claude —Gemini #5Grok —
UK AI Safety Institute's declarative, code-first framework built for highly rigorous, reproducible evaluations of frontier model capabilities and agent safety.
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Gemini UK AI Safety Institute's declarative, code-first framework built for highly rigorous, reproducible evaluations of frontier model capabilities and agent safety.
Where it falls shortper Gemini Designed for academic and safety research audits rather than rapid application-level vulnerability scanning.
Rank history
Just missed the top 5
GPT HiddenLayer AISec Platform — strong enterprise attack simulation and broader AI security coverage, but less accessible and developer-friendly than the top five · DeepTeam — easy open-source red teaming with useful attack methods, but its ecosystem, maturity, and enterprise workflow remain thinner
Claude DeepTeam by Confident AI — OWASP/NIST-aligned open-source red teaming framework built on DeepEval with 40+ vulnerabilities and attack enhancements — genuinely on-topic and rising, but a smaller community and shorter track record than the top open-source picks kept it just off · Lakera — well-known commercial player with Lakera Red plus the Gandalf challenge, but post-acquisition it leans toward runtime guardrails/detection with Lakera Guard, so its standalone red teaming is bundled rather than best-in-class
Gemini Confident AI — focused on general LLM observability and quality evaluation rather than dedicated adversarial security testing · Stingrai Snipe — prioritizes hybrid web/API penetration testing and human-led validation rather than automated LLM-specific vulnerability tooling
Grok Lakera — strong enterprise chatbot/LLM focus but narrower agentic depth
By model
ChatGPT
- 1.Promptfoo
- 2.Mindgard
- 3.Giskard
- 4.PyRIT
- 5.garak
Claude
- 1.garak
- 2.PyRIT
- 3.Promptfoo
- 4.Mindgard
- 5.Giskard
Gemini
- 1.Promptfoo
- 2.garak
- 3.PyRIT
- 4.Mindgard
- 5.Inspect AI
Grok
- 1.garak
- 2.PyRIT
- 3.Promptfoo
- 4.DeepTeam
- 5.Confident AI
Common questions
What is the best ai red teaming and llm security testing tool according to AI models?
Promptfoo leads. 2 of 4 models rank Promptfoo the top pick. The current top 3: Promptfoo, garak, PyRIT. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-07-13. Source: modelsagree.com.
Which ai red teaming and llm security testing tool did each AI model pick first?
ChatGPT: Promptfoo. Claude: garak. Gemini: Promptfoo. Grok: garak.
Do the AI models agree on the best ai red teaming and llm security testing tool?
Not unanimous. Claude picks garak; Grok picks garak.
What changed in the latest ai red teaming and llm security testing tool ranking?
In the latest poll (2026-07-13): garak climbed 3 spots, Giskard climbed 1 spot; PyRIT dropped 1 spot, Mindgard dropped 1 spot; DeepTeam and Confident AI entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this ai red teaming and llm security testing tool ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best AI red teaming and LLM security testing tool” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-07-13. https://modelsagree.com/best/best-ai-red-teaming-tool (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand