Running packages that are 12 months behind on security patches is not just a maintenance smell, it is an exploitable vulnerability. This article quantifies the risk and walks through a sustainable update cadence.
Ask any AI assistant to scaffold a REST API and you get a working server with zero protection against brute force or scraping. Rate limiting is consistently absent from generated boilerplate, even on login endpoints.
Network failures and retries mean any payment or email operation can be triggered more than once. Idempotency keys are the resilience pattern that makes repeated requests safe, and most vibe-coded integrations omit them entirely.
Lockfiles are treated as generated noise in most code reviews, but a tampered package-lock.json or pnpm-lock.yaml can silently swap a legitimate package for a malicious one without touching any source file.
A practical pre-production security checklist for AI-assisted developers who move fast and may have skipped the boring-but-critical basics: secrets, dependencies, error handling, and more.
Passing req.body directly to an ORM update call is the mass assignment vulnerability, and it appears in almost every AI-generated CRUD tutorial. This article explains the exploit and the DTO validation pattern that prevents it.
Large language models hallucinate package names, and attackers register those names and wait. Blindly copying an AI-suggested install command is a genuine supply chain risk that most teams have not considered.
console.log strings are invisible to log aggregators. Switching to structured JSON logging with consistent fields is the single change that most improves the operability of AI-assisted projects.
AI coding assistants write production code fast, but they also reproduce insecure patterns at scale. Here is a practical process for auditing AI-generated code before it ships.