Mindgard
AI red-teaming platform
Overview
Mindgard provides automated AI red-teaming — continuously attacking deployed AI systems to surface vulnerabilities before adversaries do. Founded out of Lancaster University AI Security Institute. UK-headquartered with growing US presence.
Use cases
What you can produce with Mindgard
- Point Mindgard at a deployed chatbot or LLM endpoint and run an automated red-team sweep covering prompt injection, jailbreaks and encoding-based attacks, receiving a ranked vulnerability report.
- Schedule continuous adversarial testing in your CI/CD pipeline so every new model version, system prompt or guardrail change is re-attacked before it ships.
- Discover and inventory AI assets across the organisation, including shadow AI deployments that never went through security review.
- Test whether an AI system leaks training data or can be coaxed into model extraction, and get reproduction steps for each finding.
- Map findings to frameworks like the OWASP LLM Top 10 and MITRE ATLAS to produce evidence for compliance and audit conversations.
- Commission a human-led AI penetration test through Mindgard's services arm when a regulator or enterprise customer demands manual validation.
ASEAN Perspective
Mindgard in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Mindgard is a specialist automated AI red-teaming and DAST-for-AI platform, spun out of Lancaster University, that tests LLMs, agents, and multimodal models against thousands of attack scenarios (prompt injection, jailbreaks, model manipulation) aligned to MITRE ATLAS and OWASP, with CI/CD and Burp Suite integration. For teams shipping AI features, it fills a real and growing security gap.
It is a focused security tool rather than a general one: realising its value assumes a security or ML-ops function that can act on findings, pricing is enterprise/sales-led, and it is most relevant to organisations actually deploying AI to users. ASEAN availability is fine as a SaaS. A strong, credible pick for AI security testing, but narrow in scope.
What people say
Mindgard occupies a credible, research-heavy corner of the AI security market. Spun out of Lancaster University in 2022 by Dr. Peter Garraghan and colleagues, it draws on roughly a decade of academic work in AI security, and that pedigree shows up consistently in third-party assessments: reviewers describe it as one of the more mature standalone platforms for what the industry now calls DAST-AI, or dynamic security testing aimed at runtime AI behaviour rather than static code. The company raised an $8 million round led by .406 Ventures in December 2024, bringing total funding past $11 million, and operates from London and Boston.
Users and analysts who evaluate the platform tend to praise the same things: a genuinely large attack library covering prompt injection, jailbreaks, encoding tricks, model extraction and agent misuse; the ability to test deployed systems continuously rather than as a one-off pentest; and reporting that surfaces risks traditional application-security tools simply do not see. Reviews on aggregator sites also mention responsive live-chat support and competitive pricing, though the company does not publish price lists, which makes budgeting harder for smaller teams.
Criticism is comparatively thin, partly because the independent review footprint is still small — this is niche enterprise security software, not a mass-market SaaS, and public review volume on Gartner Peer Insights and similar platforms is limited. The most concrete complaint that surfaces is that the product is English-only. Prospective buyers should also expect a sales-led motion rather than self-serve onboarding.
Mindgard fits security teams at organisations that have moved past AI experimentation into production — customer-facing chatbots, agentic workflows, fine-tuned models — and need continuous adversarial testing with evidence they can hand to auditors. Teams wanting a cheap self-serve scanner for a single hobby chatbot are not the target buyer.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Mindgard's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Mindgard unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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