What is the best AI for red teaming?

Why 'Best AI for Red Teaming' Is the Wrong Question

If you're shopping for "the best AI for red teaming," you're already asking the wrong question — and the most credible research in the field says so explicitly. The market is booming, tools are multiplying, and every vendor claims victory, yet no independent ranking of red teaming AI tools by effectiveness even exists.

The numbers explain the confusion. Commercial analysts project the AI red teaming market growing from roughly USD 1.3 billion in 2025 toward USD 18.6 billion by 2035, with a competing estimate of USD 20.22 billion by 2035 — near-30% annual growth either way. Meanwhile, CSET at Georgetown notes that "red-teaming practices vary widely, and there are few established standards or best practices," even though Anthropic, Google DeepMind, and OpenAI all do it.

The deeper problem: the tool you pick shapes what you find. CSET, which evaluated 23 red-teaming resources firsthand, warns that "inappropriate tools can obscure evaluation results and lead to false conclusions." A scanner that runs 20,000 prompts against ~100 attack vectors sounds impressive, but if its fixed threat model doesn't match your actual risks, you'll walk away confident and unprotected.

That's why the right question isn't "which AI is best?" but "which tool fits my threat model and my team's skill level?" The landscape splits into archetypes with real trade-offs:

• Benchmark-style scanners (like NVIDIA Garak) offer convenience and volume, but lock you into fixed threat models.
• Customizable harnesses (like UK AISI Inspect or Microsoft PyRIT) give control, but demand technical expertise your team may not have.
• Agentic automation — where agents select and run attacks themselves — is the frontier, with one Dreadnode study logging 674 attacks in roughly three hours at an 85% success rate.

The same fit-over-ranking logic applies to the agent skills powering these workflows. Published research found roughly 26% of skills carry at least one vulnerability, so before any tool or skill touches your agent, it needs vetting — which is exactly what SkillzBench is building: a free, independent directory where skills are security-scanned, version-pinned, and benchmarked so you can match skills to your use case with evidence, not marketing.

Define your threat model first. Then pick the tool that answers it — not the one with the loudest ranking.

The Three Archetypes: Scanners, Harnesses, and Autonomous Agents

No single tool wins at AI red teaming — CSET's evaluation of 23 red-teaming resources found that fit matters more than any ranking, and the wrong tool can actively obscure results and lead to false conclusions. The 2025 landscape splits into three archetypes, each trading coverage, control, and required expertise differently.

Benchmark-style scanners trade control for convenience. NVIDIA's Garak tests roughly 100 attack vectors with up to 20,000 prompts per run across 20+ AI platforms, giving broad, high-volume coverage with minimal setup. The catch, per CSET's tool taxonomy, is a fixed threat model: you get the attacks the scanner ships with, not the ones your system actually faces.

Customizable harnesses trade convenience for control. Microsoft PyRIT, UK AISI's Inspect, Promptfoo, and CyberArk's FuzzyAI let teams compose multi-turn attack orchestration, converters, and CI/CD integrations — Promptfoo even maps findings to OWASP, NIST, and MITRE ATLAS. That flexibility demands real technical skill, which is precisely why scarce AI security expertise ranks among the top adoption barriers.

Autonomous agents trade control for scale. A Dreadnode study had a single operator execute 674 attacks against Meta's Llama Scout in roughly three hours, hitting an 85% overall attack success rate across 68 adversarial goals. But the same research notes full coverage runs closer to days, humans still beat agents on novel exploit chains and contextual social engineering, and highly aligned frontier models often refuse to compose offensive workflows at all.

Three trade-offs to weigh before choosing:

• Coverage: scanners probe widely but shallowly against fixed threat models; harnesses go deep where you point them.
• Control: agents generate and chain attacks autonomously; harnesses require you to configure each step.
• Expertise: scanners need little, harnesses need engineering skill, and agents shift humans toward triaging hundreds of findings.
• Trust in scoring: AI judges appear in over half of evaluated tools, yet zero documented reliability guarantees exist for their outputs.

That last point deserves emphasis. CSET found no tool offering explicit human-scoring infrastructure and no documented reliability guarantees for AI scorer outputs — so a dashboard full of auto-tagged findings is not the same thing as security. Whatever archetype you pick, pair automated probing with human triage, and vet every skill and tool before it touches an agent: published research elsewhere found roughly 26% of skills carry at least one vulnerability. That vetting gap is what SkillzBench is building to close — a free, independent directory that security-scans, version-pins, and benchmarks agent skills, plus a free MCP connector so your agent only ever sees the skills you chose.

Where Automation Stops and Humans Start

An agent can fire 674 attacks in three hours. It still can't tell you which of those results actually matter — and that gap is where your security program lives or dies.

The case for automation is real. A Dreadnode study showed a single operator running an agentic red teaming workflow against Meta's Llama Scout, achieving an 85% overall attack success rate across 68 adversarial goals. High-volume, repetitive probing is exactly what agents do best, and automated tooling is driving the market's shift toward continuous, lower-cost testing.

But the same research is explicit about the limits. Humans still outperform agents on nuanced long-horizon reasoning, contextual social engineering, and novel exploit chains — and no formal comparison against expert human operators has even been done yet. Generative AI attackers, Georgetown's CSET notes, "struggle with creativity, which is a fundamental skill required to perform effective AI red-teaming."

The scoring layer is equally shaky. CSET evaluated 23 red-teaming tools and found AI-powered "judges" in over half of them — yet zero tools documented reliability guarantees for those scores, and zero offered human-scoring infrastructure. As CSET puts it, an AI evaluator may not catch a mistake made by another AI.

The practical answer is a hybrid model, and it's already the standard of care at the frontier:

• Continuous automated probing for high-volume, repetitive attacks and regression checks after model updates
• Expert human triage to decide which of hundreds of auto-generated findings reflect real risk — the skill Raja Sekhar Rao Dheekonda, co-creator of Microsoft's PyRIT, says agents should push operators toward
• Vetted external experts for novel and high-stakes scenarios, mirroring OpenAI's practice of adversarial testing by vetted external experts before deployment

Fiddler frames the stakes with its "trust debt" concept: the gap between the autonomy you've granted your agents and your actual ability to oversee their behavior. Closing that gap means running automated tests continuously while reserving human testers for novel attack discovery.

Vetting matters on both sides of that hybrid. Since roughly 26% of published agent skills carry at least one vulnerability, what you connect to your agent is part of your attack surface. That's the problem SkillzBench is building for — a free, independent directory that security-scans, version-pins, and benchmarks agent skills so automated testing runs on components you can trust. The waitlist is open now for teams that want vetted red team skills the moment the platform launches.

Automation gives you scale. Humans give you judgment. You need both — and you need to know which skills deserve a place in either loop.

Vetting Your Arsenal: Why Skills and Tools Are Part of the Attack Surface

You are likely red teaming your AI models, but you are ignoring the tools they use. Every skill, plugin, and connected API expands your attack surface. If you do not vet these components, you are leaving the front door unlocked.

Agent red teaming is fundamentally different from standard LLM testing. You must test not just what an agent says, but what it does, what data it accesses, and how it chains decisions across multiple steps. This complexity means that traditional text-based safety testing is often insufficient for modern agents. A single injection attack can compromise thousands of agent sessions simultaneously.

The risk is not hypothetical. Published research indicates that approximately 26% of published agent skills carry at least one vulnerability. Furthermore, agent behavior changes dynamically after model updates or new tool additions. A vulnerability you patched last quarter might reappear after a minor update. This creates a growing "trust debt" between the autonomy you grant your agents and your ability to oversee their behavior.

To manage this risk, you need a rigorous vetting process for your agent stack. This approach focuses on verifying the integrity and safety of every skill before it touches your production environment. Key steps in this vetting process include:

• Security scanning to identify known vulnerabilities and malicious code within skill packages.
• Version pinning to commit SHAs ensures you are testing the exact code that will run, preventing silent updates.
• Sandbox benchmarking to test skills in isolated environments with consistent models, measuring actual performance lift.
• License and quality checks to ensure skills are legally usable and technically sound before deployment.

This is precisely what SkillzBench is building. It is a free, independent directory designed to help teams know what to trust before installing it. The platform security-scans skills, version-pins every release, and benchmarks performance in a sandbox. By publishing Lift scores, it allows you to see exactly how much value a skill adds versus its risk profile.

Additionally, SkillzBench is developing a free MCP connector. This tool allows you to toggle skills and tools per agent, ensuring your agent only sees what you have explicitly chosen. This granular control helps mitigate the expanded attack surface inherent in agentic systems. As the AI red teaming market grows, the ability to vet your arsenal becomes as critical as the red teaming tools themselves.

Your Red Teaming Decision Checklist

You cannot simply buy the "best" red teaming AI because the landscape splits into distinct archetypes with unique trade-offs. The most credible independent analysis argues that tool fit matters more than any ranking, emphasizing that inappropriate tools can obscure evaluation results and lead to false conclusions (cset.georgetown.edu). Before selecting a solution, you must define your specific threat model and assess your team’s current technical skill level.

Start by identifying whether your primary need is broad, high-volume coverage or deep, customizable control. Benchmark-style scanners like NVIDIA Garak offer convenience for sweeping ~100 attack vectors, while harness-style frameworks demand more technical expertise for precision (cset.georgetown.edu). Once you match the archetype to your capabilities, you can begin structuring your operational workflow.

The most effective modern approach combines automated scale with human nuance. A recent study demonstrated that a single operator could execute 674 attacks against a target model in roughly three hours, achieving an 85% success rate (helpnetsecurity.com). However, humans still outperform agents on novel exploit chains and contextual social engineering. You should automate repetitive probing tasks while reserving your skilled human testers for discovering new, high-stakes vulnerabilities that algorithms might miss.

Do not rely solely on automated scoring systems. Over half of evaluated red teaming tools use AI-powered scorers, yet none provide documented reliability guarantees for those outputs (cset.georgetown.edu). An AI evaluator may not catch mistakes made by another AI, creating a dangerous blind spot. You must build human triage into your workflow to ensure that dashboards full of auto-tagged findings do not create a false sense of security.

Regulatory pressure is shifting red teaming from a one-time event to a continuous, auditable process. Frameworks like the EU AI Act and NIST AI RMF expect documented testing before major deployments and on a recurring quarterly basis (finance.yahoo.com). Furthermore, agent behavior changes with every model update, meaning a vulnerability you patched last quarter might reappear today (fiddler.ai).

Finally, vet every skill and tool before it touches your agent. Published research indicates that approximately 26% of agent skills carry at least one vulnerability, making pre-deployment verification critical.

• Define your threat model and team skill level before selecting tools.
• Automate repetitive attacks but reserve humans for novel exploit discovery.
• Implement human triage to validate AI-scored results and prevent bias.
• Schedule continuous testing quarterly and before every major deployment.
• Verify the security and license status of every skill before installation.

SkillzBench is building a free, independent directory to address this vetting gap. The platform will security-scan, version-pin, and benchmark agent skills so you can see exactly which tools help and which carry risk. By using a free MCP connector, your agent only sees the skills you have explicitly chosen and verified. Skills, proven.