Other

33 / 100

Hands-on AI security training for security consultants and engineers. Find and fix vulnerabilities in AI applications — chatbots, retrieval (RAG) systems and AI agents — then prove the fix holds.

Scanned 2h ago · 0 views · Open site

Checks

• HTTPS5/5

  Served over HTTPS.

• llms.txt12/12

  Found /llms.txt (1929 chars).

• Agent manifest0/15

  No /.well-known/ai-agent.json.

  Fix: Publish https://genaisecuritylab.com/.well-known/ai-agent.json with name, description, capabilities, contact, auth, and pricing.

• A2A agent card0/12

  No /.well-known/agent-card.json.

  Fix: Publish https://genaisecuritylab.com/.well-known/agent-card.json with name, url, and skills or capabilities.

• MCP endpoint12/12

  Discoverable MCP metadata or endpoint.

• OpenAPI spec0/12

  No OpenAPI or Swagger spec found.

  Fix: Serve /openapi.json (or /swagger.json) with a valid OpenAPI spec, or link it from the homepage.

• Named capabilities0/8

  No named capabilities, skills, or API operations.

  Fix: List named capabilities in the agent manifest, agent card skills, or OpenAPI path summaries.

• I/O schemas0/8

  No I/O schemas.

  Fix: Add components.schemas / requestBody schemas in OpenAPI, or input/output schemas on each skill.

• Machine-readable pricing4/8

  Pricing mentioned in llms.txt, but not as structured data.

  Fix: Add a pricing object to ai-agent.json, or publish /pricing.json with currency and product amounts.

• Auth, contact, rate limits0/8

  No contact, auth, or rate limit declaration.

  Fix: Declare contact, auth, and rateLimit in ai-agent.json (and OpenAPI info.contact / securitySchemes).

7 checks short of a perfect 100. The fix lines above are the report — what to publish, at which path.

How scoring works · Recently graded · Back to the board