Which AI tool is best for red teaming?

Why There Is No Single "Best" AI Red Teaming Tool

If you're looking for a definitive "best" AI red teaming tool, three independent analyses have the same disappointing answer: there isn't one. Georgetown's CSET, General Analysis, and Braintrust all conclude that the right tool depends on your threat model, your team's skills, and where you are in the AI lifecycle.

CSET puts it bluntly: "appropriate tools can empower testers, but inappropriate ones can obscure evaluation results and lead to false conclusions." The field, the Georgetown researchers note, is "severely lacking in theoretical grounding and technical best practices." A prompt-injection scanner, for example, tells you little about autonomous-agent risks or email leakage.

General Analysis frames the market as different operating models, not interchangeable entries in a league table — and admits it ran no head-to-head benchmarks itself. That admission matters, because no one else has either. Every comparison you'll find is documentation-based or vendor-authored, and vendor blogs consistently rank their own products favorably.

Meanwhile, the market is exploding. AI red teaming services are projected to grow from $1.75 billion in 2025 to $6.17 billion by 2030, at roughly 28.5% annually, according to syndicated market research. Consolidation is already making older comparisons obsolete: Protect AI acquired SydeLabs, Zscaler acquired SPLX, and Promptfoo was reportedly acquired by OpenAI.

So what actually differentiates the tools? Three structural questions matter more than any feature checklist:

• Threat model fit — benchmark-style tools like Garak ship with fixed threat models, while harnesses like UK AISI's Inspect are customizable but demand coding skills.
• Operating model — open-source frameworks (PyRIT, Garak, Inspect, DeepTeam) trade engineering time for control; managed platforms trade cost for convenience.
• Lifecycle stage — a one-time launch scan leaves every later model update untested, so continuous coverage requirements should shape the choice.

There's a deeper trust problem, too. CSET found that AI-powered scorers appear in over half of surveyed evaluation tools, yet none document scorer reliability guarantees — meaning the "judge" deciding what counts as a vulnerability can itself hallucinate. Microsoft's own lesson from red-teaming 100+ products reinforces this: AI red teaming is not safety benchmarking — it probes deployer-specific failures in context.

For teams building AI agents, the same verification gap applies to the skills and tools agents consume. SkillzBench is building a free, independent answer to that adjacent problem: a directory of agent skills with commit-pinned versions, security scanning, and sandboxed benchmarking, so you can see what actually helps before an agent runs it. Red teaming your model only works if you can also trust what you've attached to it.

The Six Tools That Keep Coming Up — and What Each Is Actually For

Six names dominate nearly every credible comparison of AI red teaming tools, yet the strongest consensus in the research is that no single one wins. General Analysis frames the field as "different operating models, not ten interchangeable entries in a league table," and Georgetown's CSET warns that "inappropriate ones can obscure evaluation results and lead to false conclusions" (CSET).

Garak is the open-source scanner for broad vulnerability sweeps. CSET classifies it as a benchmark-style tool with a threat model centered on prompt injection, and it carries an Apache 2.0 license, so you pay only for model and infrastructure costs (Braintrust). PyRIT, MIT-licensed, suits teams building custom, adaptive, multi-turn attack campaigns — it uses an AI adversarially to mimic human "prompt hackers," which matters given that multi-turn attacks like Crescendo outperform single-turn methods by 29–71% depending on the model (Kili Technology).

Promptfoo earns consistent praise as the best all-around single command-line workflow, claiming coverage of 50+ vulnerability types. One caveat: a reported OpenAI acquisition appears only in a vendor source and remains unverified. DeepTeam fits Python teams already using DeepEval, while Inspect, from the UK AI Safety Institute, is a highly customizable evaluation harness — but you must supply datasets and write code.

Mindgard rounds out the list for teams wanting a managed platform with continuous automated red teaming — though that ranking comes largely from Mindgard itself. Vendor bias runs through this entire category: Mindgard, Braintrust, General Analysis, and Kili all rank their own products favorably, and no source has run independent head-to-head benchmarks.

A quick guide to matching tool to job:

• Garak — free, broad vulnerability scanning of models
• PyRIT — custom adaptive, multi-turn attack campaigns
• Promptfoo — all-in-one CLI with a generous free tier (10,000 probes/month)
• Inspect — customizable harness for teams with coding capacity
• Mindgard — managed, continuous testing for non-engineering teams

One warning cuts across all six: over half of surveyed tools rely on AI-powered scorers, and none document scorer reliability guarantees (CSET's evaluation of 23 tools). Treat automated findings as leads, not verdicts.

For teams testing AI agents rather than just models, remember that agent skills and MCP-connected tools expand the attack surface too — SkillzBench is building a free directory with security scanning and benchmarking for exactly that gap, so you can verify what your agents run before you red team what they say.

What Your Threat Model Should Tell You Before You Compare Features

The most expensive red-teaming mistake isn't buying the wrong tool — it's running the right tool against the wrong question. Georgetown's CSET puts it bluntly: "appropriate tools can empower testers, but inappropriate ones can obscure evaluation results and lead to false conclusions" — a warning grounded in their first-hand evaluation of 23 AI red-teaming resources.

Before you compare feature lists, know where a tool sits structurally. CSET divides the landscape into two categories: benchmark-style scanners like NVIDIA's Garak, which ship with fixed threat models, and evaluation harnesses like the UK AI Safety Institute's Inspect, which are highly customizable but demand coding skills and your own datasets. Independent analysis frames these as different operating models, not interchangeable entries in a league table.

Your threat model must also reflect how attacks actually work today — and that means multi-turn and agentic. Microsoft's Crescendo technique outperforms single-turn attacks by 29–61% on GPT-4 and 49–71% on Gemini-Pro, while research on LLM red teaming shows low-resource-language translations bypass GPT-4 safeguards 79% of the time. OWASP, meanwhile, ranks prompt injection #1 on its LLM Top 10 for the second consecutive year and concludes complete prevention isn't currently feasible.

If your organization runs AI agents, the stakes compound, because every connected tool and instruction package widens that injection surface. Verifying what your agents actually execute — the kind of security scanning and skill vetting SkillzBench is building for its free skills directory — belongs in the same threat model as your model-level red teaming.

One more caveat deserves a place in your requirements doc: the scorer problem. CSET found AI-powered scorers appear in over half of evaluation tools surveyed, yet not a single tool documents reliability guarantees for them — and these judges can hallucinate or make biased calls. A clean report may simply mean a lenient scorer.

Before shortlisting any vendor, make your threat model answer these questions:

• Does the tool test multi-turn and agentic attacks, or only single-turn prompts?
• Can it observe application-level outcomes (like an unauthorized record change), not just model responses?
• Does it cover non-English and low-resource-language bypasses?
• Who scores the results — an AI judge with no reliability guarantee, or a documented human review process?

Match the tool to the threat model first; features second. A mismatched tool doesn't just waste budget — it manufactures false confidence, which is the one outcome red teaming exists to prevent.

Choosing and Running Your Tool: Costs, Pitfalls, and a Practical Checklist

The license fee is the smallest number on your red teaming budget. Georgetown's CSET warns that "inappropriate tools can obscure evaluation results and lead to false conclusions," so your first decision isn't which tool has the most features — it's which tool matches your threat model. A prompt-injection scanner built for chat leakage tells you nothing about whether an autonomous agent will approve a fraudulent customer record change; the sharpest question to ask any vendor is whether the tool observes application-level outcomes or just model responses.

Next, pick your operating model. If you have engineering capacity, open-source frameworks like Garak (Apache 2.0) and PyRIT (MIT) cost nothing in licensing — but as General Analysis puts it, "a free repository does not mean a free evaluation." The real spend is integration time, inference costs for attacker and judge models, and ongoing maintenance of your attack library. If you lack that capacity, managed options trade money for control: Promptfoo's Community plan is free at 10,000 probes per month, Lakera Red offers 10,000 free API requests monthly, and enterprise platforms like Mindgard run on custom annual contracts.

Whatever you choose, don't trust the findings blindly. CSET's survey of 23 red teaming resources found that AI-powered scorers appear in over half of evaluation tools, yet zero tools document scorer reliability guarantees — and scorers can hallucinate or make biased judgments. Always ask for denominators and review processes rather than comparing raw failure counts across tools, and put a human in the loop before acting on any AI-flagged result.

The case for human verification extends beyond prompts entirely. In a real client engagement, ethical hacker Rob Shapland needed just 3 seconds of audio to clone a senior IT leader's voice with ElevenLabs and defeat Microsoft Authenticator MFA — no prompt injection involved. For agent builders, that lesson cuts twice: skills and MCP-connected tools expand what your agents can touch, and published research found roughly 26% of agent skills carry at least one vulnerability. That's exactly why SkillzBench is building a free directory that pins every skill to a commit SHA and security-scans it before you ever load it into an agent.

Run your red team continuously, not once at launch. A scanner run once at launch is cheap — and it leaves every later model update untested. Given that multi-turn attacks like Crescendo outperform single-turn tests by 29–61% on GPT-4 and agentic prompt injection tops OWASP's LLM risk list, one-and-done testing is a false economy.

A practical pre-run checklist:

• Write your threat model first; match the tool to it, not the other way around.
• Budget for engineering time, API spend, and attack-library upkeep — not just the license.
• Verify AI-scorer findings with human review before remediating.
• Cover multi-turn, agentic, and multilingual attacks, not just single-turn English prompts.
• Re-test after every model or skill update.

Red teaming well is a discipline, not a purchase. Skills, proven — browse and test free agent skills on SkillzBench, and join the waitlist to get security-scanned, benchmarked skills the moment we launch.

Red Team the Model — Then Verify What Your Agents Actually Run

Red teaming your model is only half the job. Even the best scan tells you nothing about the skills and tools your agents load at runtime — and that's where the attack surface quietly expands.

Agent skills are instruction packages your coding agents and chat-app assistants pull in to do their jobs, and the supply around them has grown faster than anyone's ability to vet it. Published research found that roughly 26% of published skills carry at least one vulnerability — meaning about one in four packages an agent might install could open a door you didn't know existed. OWASP ranks prompt injection as the #1 LLM risk for the second consecutive year, and its conclusion is blunt: complete prevention isn't currently feasible.

This matters because agents don't just respond to prompts — they act. They call MCP-connected tools, load third-party instructions, and execute on your behalf. A red team scan of the underlying model won't catch a malicious instruction buried in a skill, or a tool that exfiltrates data the model itself would never leak. The CSET evaluation of 23 red-teaming resources makes the point generally: tools matched to the wrong threat model can "obscure evaluation results and lead to false conclusions." A model-only threat model, applied to an agent stack, is exactly that mismatch.

So what does verifying the agent layer actually require?

• Knowing where each skill came from, and pinning it to an exact version
• Security-scanning and license-checking every package before it runs
• Measuring whether a skill genuinely improves results — not just trusting its description
• Controlling which skills and tools each agent can see, per agent
• Re-checking everything when upstream code changes

That's the gap SkillzBench is being built to close. It's a free, independent directory of agent skills that continuously crawls GitHub, dedupes what it finds, pins every version to a commit SHA, and runs security scans, license checks, and quality judgments on each package. Every skill gets a trust score from 0 to 100 built on security, quality, license, maintenance, and community signals — and money never touches those scores. A companion MCP connector, also free, lets you toggle exactly which skills and tools each agent can access, so an agent only ever sees what you deliberately chose.

The red-teaming tools covered in this guide — Garak, PyRIT, Promptfoo, and their peers — probe what your model does under attack. Verifying what your agents actually run closes the other half of the loop. If you want to know when the directory, scanning, and benchmarking go live, the SkillzBench waitlist is open now.