best 10 ai red-teaming tools

mit-licensed with a genuinely usable free allowance — 10,000 red-team probes a month — covering more than 50 named vulnerability types: prompt injection, jailbreaking, rag document exfiltration, system-prompt override, malicious resource fetching, pii leakage, unauthorised data access and tool and function discovery.

that last one matters. promptfoo frames its testing as 'dynamic attacks tailored to your application' including agent tool discovery, which puts it at the application and agent layer rather than probing a bare model. garak, its closest free rival, tests a single model and cannot tell you anything about what an agent does with its tools.

two honest limits. scaling past the free tier means enterprise or on-premise, both quote-only with no published price — the same wall as every commercial vendor here. and while it claims mapping to owasp, nist and the eu ai act for audit-ready reporting, the specific mapping isn't itemised, so verify it covers what your auditor asks for.

apache 2.0, maintained under nvidia's github organisation with leon derczynski as co-copyright holder, and backed by a peer-reviewed preprint on arxiv that users are asked to cite. in a category where most vendors publish marketing copy, having a paper to point at is a meaningful difference.

the probe coverage is broad for model-level work: hallucination, data leakage, prompt injection, misinformation, toxicity, jailbreaks including dan-style attacks, encoding-based injection, malware generation, package hallucination and xss-style output, across more than twenty modules. it runs from the command line against a named target model with no account and no sales contact.

the boundary is firm and worth understanding before you rely on it. garak points at a single llm directly. it does not test the application wrapping that model, and it does not test what an agent does with its tools — so it cannot answer the questions that make agentic deployments risky. no compliance-framework mapping either.

the attack taxonomy is genuinely itemised rather than gestured at: model-level jailbreaks, adversarial prompts, poisoning and safety bypasses; agentic tool hijack, goal manipulation and inter-agent and tool-chain abuse; and application-level context contamination, auth bypass, data exfiltration and session abuse. after reading gray swan describe 'emerging attacks', that specificity is the differentiator.

it is also the only vendor here mapping to five named frameworks — owasp asi for agentic security, nist ai rmf, the eu ai act, cosai and mitre — and it publishes named vulnerability research, including a cursor deeplink remote-code-execution finding and a shell-injection issue it calls guardfall, plus a threat-intel feed it says ingests over 3,000 sources monthly.

two caveats. there is no pricing and no self-serve path at all. and the flagship has narrowed to runtime security for ai coding agents specifically — cursor, claude code, copilot — with general red-teaming positioned as a companion service, so confirm the scope matches what you actually need.

mit-licensed and maintained by microsoft, pyrit is a framework for building red-team orchestration against generative ai systems, covering prompt injection, jailbreaks and harmful-content generation among its stated risk categories. because it's a toolkit rather than a fixed probe catalogue, what it actually tests depends on what you build with it.

that is the trade against garak. garak ships twenty-plus probes you can run immediately; pyrit ships the machinery to construct attacks specific to your system and threat model. for a team with the engineering capacity to use it, that's more powerful. for a team wanting a scan this afternoon, it's more work.

one practical trap: the canonical repository moved from the azure organisation to the microsoft organisation, and the old azure/pyrit location was archived read-only on 27 march 2026. search results and older documentation still point at the archived one, so check which url you're installing from.

lakera is the only paid vendor in this ranking with a confirmed free self-serve entry point. that alone distinguishes it in a category where every other commercial option routes you to a demo booking before you learn anything.

what it tests: direct and indirect prompt injection, ai data leakage, toxic content generation, and multilingual and multimodal attacks, with model-agnostic guardrails that sit in front of whatever you're running. it maintains a 1,000-plus member practitioner community and publishes guides including an agentic ai security playbook.

two things to be clear about. the self-serve signup gets you the guardrail and firewall product; the 'ai red teaming' line — automated risk-based testing plus expert-led adversarial engagements — is marketed separately and is not what you get by signing up. and the pricing page renders without any numbers on it, so what the paid tiers cost is unknown.

50-plus disclosed cves and 30-plus issued patents is a concrete, checkable claim, and stronger evidence of real security work than the 'expert research team' language several competitors rely on. it also publishes an annual ai threat landscape report.

the platform spans four named pillars — ai discovery to inventory your ai assets, ai supply chain security for model and artifact risk, ai attack simulation, and ai runtime security. model supply-chain scanning is genuinely differentiated: most vendors here test behaviour, not the provenance of the artefacts you're deploying.

the specifics thin out under inspection. the attack-vector claims are described as covering the ai lifecycle rather than itemised by category, we could not locate a dedicated red-teaming methodology page — two url attempts returned 404 — and no compliance-framework mapping appeared on any page we reached. confirming what the red-teaming product actually does would require the demo.

splx publishes named red-team research against specific frontier models — gpt-5, claude opus 4.1 and grok 4 — plus an agentic red-teaming whitepaper. testing models people actually deploy, and saying what was found, is more useful than a vendor's own bypass-rate statistics.

agentic radar, its agent and tool-graph mapping tool, is open source on github and separable from the paid platform. for anyone trying to understand what their agent can reach before buying anything, that's a genuinely free starting point — though we did not fetch its licence file, so confirm the terms before depending on it.

the rest is the category norm. the core testing and guardrail platform is demo-gated with no published price, and its compliance language refers to 'global and custom security standards' without naming a framework. note also that the company now presents as splx rather than splxai, so the older name is legacy.

mindgard claims more than 100 public vulnerability disclosures against major ai systems, naming google antigravity, openai sora and xai grok among them. specific named targets are more persuasive than an unnamed count, and the company traces its methods to over a decade of ai-security research at lancaster university.

the product spans attack-surface enumeration, what it calls psychometric agent profiling, automated red-teaming across chatbots, applications and agentic workflows, and runtime protection with automated agent hardening — model, application and agent layers all covered on its own description.

two things to note. there is no pricing on the pricing page and no self-serve path. and the only compliance badge shown is aicpa soc 2 type ii, which is a general information-security certification rather than a mapping to any ai framework — easy to mistake for one when it appears next to red-teaming claims.

the arena is the interesting part: a crowdsourced attack competition with open enrolment and cash prizes, feeding discovered exploits back into gray swan's defences. it claims three million attack attempts and says most arena-discovered exploits remain unpublished. as a discovery mechanism for genuinely new jailbreaks that's more likely to work than an internal team, and it names google deepmind, openai, anthropic and meta as customers or partners.

it also publishes system cards for evaluations of frontier models including claude and gpt, and maintains a research hub it says holds 150-plus curated tools and datasets.

the commercial product is the opaque part. shade for red-teaming and cygnal for runtime protection are entirely sales-gated, and the testing scope is described in marketing terms — 'emerging attacks', protection tuned to 'your enterprise agent's tools, data and workflows' — rather than itemised attack categories. no named compliance framework appears anywhere, only references to 'emerging ai safety frameworks'.

the recon scan is a genuine self-serve starting point in a category that mostly refuses to give you one, and the described coverage is relevant: prompt injection, jailbreaks against safety guardrails, data exfiltration through context windows and rag pipelines, excessive agent autonomy, tool abuse and api exploitation, and resource exhaustion. testing is black-box and model-agnostic across openai, gemini, anthropic, llama and hugging face, and claims multimodal and 100-plus language coverage.

the headline numbers are where it loses ground. 270-plus vulnerability types and 15 million evolving attack patterns are large, specific-sounding figures with no paper, benchmark, disclosure record or methodology behind them that we could locate. against adversa's named cve-style findings and garak's peer-reviewed paper, that's assertion rather than evidence.

we also could not confirm basic corporate details — attempts to reach an about or company page returned 404 — so its independence is an absence of contrary evidence rather than a verified fact, and the full platform's price is unpublished.