Security Debt in AI-Generated Codebases — A Structural Problem, Not a Tooling Problem

"We passed the security review. Six weeks later, we found auth bypasses in three endpoints." Research shows 45% of AI-generated code contains security vulnerabilities. Not because AI is malicious — because security is a system-level property, and AI generates code at the function level. This post breaks down the structural mechanism behind security debt in AI-generated codebases, how to detect it, and the enforcement model that prevents it. The Structural Mechanism AI produces code that works. "Works" means it handles expected input correctly. It does not mean it handles unexpected input safely. Authentication, authorization, input validation — these are constraints that must be enforced globally, not function by function. Here's what happens in practice: Session 1: Auth middleware created for /api/users Session 12: New route /api/billing added — no auth middleware applied Session 25: Frontend validation added — backend accepts raw input Session 38: API key hardcoded in utils/stripe.ts