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Mindgard

Automated continuous AI red teaming from a Lancaster University spinout.

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About

Mindgard is an automated AI red-teaming platform spun out of Lancaster University, treating AI security testing as its own discipline ("DAST-AI" — dynamic application security testing for AI). It runs continuous adversarial attacks against LLMs, agents, and multimodal models — jailbreaks, prompt injection, data leakage, model theft — with an attack library maintained by its own research team, which has disclosed 150+ vulnerabilities in production AI products.

Findings map to OWASP LLM Top 10 and MITRE ATLAS and plug into CI/CD. The company raised a $30M Series A in August 2026. It is testing-only by design: no quality evals, no runtime gateway, no AI inventory — it finds attacks, and enforcement lives elsewhere.

What it can do

  • Continuous automated red teaming (LLM, agent, multimodal)
  • Research-maintained attack library (150+ disclosed vulns)
  • CI/CD integration with pass/fail thresholds
  • OWASP LLM Top 10 and MITRE ATLAS mapping
  • Remediation advice and quantified risk reports

Setup

  1. Book a demo at mindgard.ai.
  2. Provide an inference endpoint for your model or agent.
  3. Select attack scenarios or run the full library.
  4. Review quantified risk reports mapped to OWASP/MITRE ATLAS.
  5. Integrate with CI/CD for continuous re-testing on every change.

Prerequisites

Commercial account; an inference endpoint for the system under test.

Use cases

agentsresearchothers

Works with

Any agent (.md)

Trust score — 6/10

/10 trustMetrics-checked

Scored 6/10 (metrics tier, metrics-v1). Evidence: Lancaster University spinout; $30M Series A led by Album VC reported Aug 2026; Claims 150+ vulnerabilities disclosed in production AI products; Testing-only by design: no runtime gateway, no quality evals, no AI inventory; Commercial SaaS, demo-gated; independent verification of attack library not available. License: Proprietary SaaS.

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
6.0/10
Maintenance · recency, releases, issue load
7.0/10
Openness · license + docs
4.0/10
Manual review · sanity read + red-flag check
7.0/10
  • · Lancaster University spinout; $30M Series A led by Album VC reported Aug 2026
  • · Claims 150+ vulnerabilities disclosed in production AI products
  • · Testing-only by design: no runtime gateway, no quality evals, no AI inventory
  • · Commercial SaaS, demo-gated; independent verification of attack library not available

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.