AI Generated Code Security Checklist: 15-Item Guide Before Production

TL;DR

AI coding tools write functional code quickly, but security is often an afterthought. Before deploying AI-generated code: search for hardcoded secrets, verify database access controls, test authentication server-side, and validate all user inputs. 5 critical items must be fixed before launch, 6 important items within the first week, and 4 recommended items when you can. This checklist applies to code from Cursor, Bolt, Lovable, ChatGPT, Copilot, or any AI assistant.

Quick Checklist (5 Critical Items)

Search for API key patternsSearch for: sk_, pk_, api_key, apiKey, secret, password, token

Verify .env is gitignoredCheck .gitignore includes .env, .env.local, and similar files

Check RLS is enabled on ALL tablesAI often enables RLS on some tables but forgets others

Verify server-side auth checksFrontend route protection is not security. Backend must verify user.

Test data isolationAs User A, try to access User B's data by modifying IDs

Hardcoded Secrets 5

Search for API key patternsSearch for: sk_, pk_, api_key, apiKey, secret, password, token, SUPABASE_, OPENAI_. How to secure API keys

Check for placeholder valuesLook for: YOUR_API_KEY, xxx, TODO, FIXME in configuration. How to find placeholder secrets

Verify .env is gitignoredCheck .gitignore includes .env, .env.local, and similar files. How to secure .env files

Check browser DevTools Network tabRun your app and verify no secrets appear in request headers or bodies. How to inspect network requests

Verify public vs private key usageFrontend should only have NEXT_PUBLIC_, VITE_, or similar prefixed vars. How to separate client/server keys

Database Security 4

Verify database access controls existRLS for Supabase, Security Rules for Firebase, or equivalent. How to set up Supabase RLS

Check RLS is enabled on ALL tablesAI often enables RLS on some tables but forgets others. How to audit RLS on all tables

Test data isolationAs User A, try to access User B's data by modifying IDs. How to test data isolation

Check for SQL injectionVerify queries use parameterized statements, not string concatenation. How to prevent SQL injection

Authentication 3

Verify server-side auth checksFrontend route protection is not security. Backend must verify user. How to implement server-side auth

Test accessing protected routes directlyTry /dashboard, /admin, /settings without logging in. How to test protected routes

Check session handlingLogout should invalidate sessions. Sessions should expire. How to configure sessions

Input Handling 3

Test XSS in text inputsEnter <script>alert(1)</script> and verify it displays as text. How to prevent XSS

Check for server-side validationAI often only adds client-side validation. Test submitting bad data directly to API. How to validate on server

Verify file upload restrictionsIf uploads exist, check type restrictions and size limits are enforced. How to secure file uploads

Why AI Code Needs Extra Review

AI coding assistants are trained to produce working code, not secure code. They optimize for functionality and user satisfaction, not security best practices. Additionally, training data includes plenty of insecure code examples that the AI learns from.

Common patterns in AI-generated code: leaving example API keys in place, implementing authentication only on the frontend, forgetting to enable database security features, and skipping input validation.

Is AI-generated code secure?

AI-generated code is functional but often lacks security best practices. Studies show 40% of AI-generated code contains at least one vulnerability. Common issues include exposed API keys, missing database access controls, and frontend-only authentication. Always review AI code before production.

Which AI tools are safest?

All AI coding tools require security review. Some (like Cursor) have better context awareness, but none are immune to security issues. The difference is in degree, not kind. Review code from any AI tool using this checklist.

Should I avoid AI coding tools?

No, AI tools dramatically increase productivity. The key is treating AI output as a draft that needs review, not production-ready code. Use AI for rapid development, but always run security checks before deploying.

TL;DR

Quick Checklist (5 Critical Items)

Hardcoded Secrets 5

Database Security 4

Authentication 3

Input Handling 3

Why AI Code Needs Extra Review

Is AI-generated code secure?

Which AI tools are safest?

Should I avoid AI coding tools?

Related Articles

Cursor Security Checklist

Bolt.new Security Checklist

Set Up Supabase RLS

Scan Your AI-Generated Code

AI Generated Code Security Checklist: 15-Item Guide Before Production