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. Of the 15 items here, 5 are critical and need fixing before launch. Six more matter within the first week. The remaining 4 are worth doing when you get the chance. This checklist applies to code from Cursor, Bolt, Lovable, ChatGPT, Copilot, or any AI assistant.

AI tools are incredible at getting you from zero to working prototype fast. The problem is that "working" and "secure" are two very different things. Run through this list before you ship. It takes 30 minutes and could save you from a very bad day.

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. Whether that code is secure isn't really part of the objective. They optimize for functionality and user satisfaction, and security best practices come lower on the list, if at all. On top of that, the training data itself is full of insecure code examples, and the model learned from those too.

The same patterns show up again and again in AI-generated code: example API keys left in place, authentication implemented only on the frontend, database security features never turned on, and input validation skipped entirely.

Is AI-generated code secure?

AI-generated code is functional but often lacks security best practices. A 2025 Stanford study found 40% of AI-generated code samples contained 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?

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