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Mindgard website screenshot

Mindgard

Mindgard is a UK-based offensive AI security company (London/Lancaster, spun out of Lancaster University) that provides an automated AI red-teaming and security testing platform for large language models, AI agents, and generative AI systems. Mindgard's platform combines AI Discovery and Recon (mapping the AI attack surface and shadow AI usage), continuous AI Red Teaming against evolving attacker techniques, AI Assessment, AI Runtime Protection, and Model Scanning, backed by a research-led attack library covering jailbreaks (ActorAttack, Crescendo, EvilConfidant, PersonGPT, DevModeV2, AsciiArtAttack, AntiGPT), prompt-injection techniques (Ascii85, AnsiEscaped, AnsiRaw and others), and policy/violation testing (MaliciousGeneration, PromptAlignment). Developers and security teams integrate Mindgard via a public REST API (projects, tests, multi-turn tests, datasets, findings, reconnaissance), a Python CLI (`pip install mindgard`), a Python SDK, a Burp Suite extension, and a GitHub Action for adding red-team checks to MLOps pipelines. The company also maintains and contributes to open-source security tooling including PyRIT integrations, an OpenAI-compatible LLM-Guard proxy, a chatbot API wrapper for testing web chatbots, and proof-of-concept vulnerability demonstrations (document RCE in LangChain agents, hidden audio jailbreaks, prompt-jailbreak demos). Mindgard's commercial model is enterprise SaaS with platform and services tiers; pricing is gated behind a sales conversation, and the platform is positioned for security, AppSec, and AI governance teams testing customer-facing or internally deployed AI systems.

human only

Index entry only — little beyond a description and a link, and nothing machine-readable enough for an agent to act on without a human reading the site first.

Kin Score

API Evangelist profiles Mindgard the way a machine reads it — 1 machine-readable artifact, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.

Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Mindgard scores 10.9/100 (minimal), with a separate agent-readiness read of 0/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.

Kin Score

This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 10.9/100 · minimal
Contract Quality 0.0 / 25
Developer Ergonomics 3.5 / 20
Commercial Clarity 0.0 / 20
Operational Transparency 0.7 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 0/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

How we profile Mindgard

Each block below is one kind of artifact we hold for Mindgard. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Mindgard Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Every other property we hold for Mindgard — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.

Documentation 3

Reference material describing how the API behaves

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Access & Security 2

Authentication, authorization, and security posture

Learn 1

Tutorials, courses, talks, and written guidance

Operate 1

Status, limits, changes, and where to get help

← All providers · Data indexed from github.com/api-evangelist/mindgard · machine-readable index on apis.io