
Build a Secure MCP Server
Secure an MCP server with deliberate transport choices, audience-bound tokens, per-tool authorization, session isolation, and execution-time approval.
Read LyraShield AI research and practical guidance on securing AI-built apps, interpreting security evidence, verifying findings, and retesting fixes.

Secure an MCP server with deliberate transport choices, audience-bound tokens, per-tool authorization, session isolation, and execution-time approval.

Protect public AI APIs with object authorization, bounded work, cost controls, constrained tools, safe output handling, and tested failure paths.

Configure host-scoped session cookies, choose SameSite deliberately, rotate identifiers, and test server-side expiry and revocation.

Design scoped audit events that reconstruct sensitive actions, resist silent changes, protect private data, and survive collection failures.

Build a pull-request security gate around immutable commits, explicit coverage, scanner failures, reviewable results, and one required decision.

Retest a security fix against a fresh build, preserve equivalent coverage, check adjacent paths, and record confirmed or inconclusive outcomes.

Build browser security headers from application behavior, stage CSP safely, and verify policies by route instead of chasing a score.

Turn scoped security evidence into an accountable go, conditional, no-go, or not-evaluated decision with owners and rollback readiness.

Define security events, route actionable alerts to an owner, and test the complete monitoring path before an AI-built app launches.

Reduce security scanner false positives with context, verification, narrow tuning, recorded suppressions, baselines, and fresh retests.