How to review AI-generated code before you launch it

You finished building. The app runs, the demo looks good, and part of you wants to just push it live and move on. But there's this quiet voice asking whether it's actually safe, or whether you just haven't found the problem yet.

That voice is worth listening to.

Most people who build with Cursor, ChatGPT, Claude, or one of the full-stack generators like Lovable and Bolt never learn how to actually check the output. They learn how to prompt well. Checking is a different skill, and honestly, a more important one once real users are involved.

This is a practical, step-by-step way to review AI-generated code yourself, even if you're not a full-time developer. It won't catch everything a professional audit would, but it'll catch the big, obvious stuff, and it'll tell you whether you need to go further.

#Is AI-generated code safe?

Not automatically, no. AI-generated code can be safe, but only after someone checks it. Studies on AI coding tools have found that a large share of AI-generated code contains at least one real security vulnerability, and developer trust in AI-written code has actually gone down over the past year even as usage has gone up.

That's not meant to scare you off using these tools. It's meant to explain why the review step matters as much as the building step. Think of it less like "AI code is dangerous" and more like "AI code is unverified until someone verifies it."

#How to review AI-generated code, step by step

#Step 1: Check for hardcoded secrets and exposed API keys

This is the single most common issue in AI-generated apps, and also the easiest to check for. Search your codebase for the word "key," "secret," "password," and "token." If any actual values show up in plain text instead of environment variables, that's a problem.

Also check your frontend bundle, not just your backend. It's surprisingly common for an API key that should only live on the server to end up sitting inside client-side JavaScript, visible to anyone who opens dev tools.

#Step 2: Verify authentication and authorization on every route

Not just "can a logged-out user do this," but "can a logged-in regular user do something only an admin should be able to do." AI tools frequently get the first check right and miss the second one entirely. Go through every route in your app and ask both questions separately.

#Step 3: Look for missing input validation

If a form field, URL parameter, or API request body gets used directly in a database query or a file path without checking it first, that's an opening for injection attacks. You don't need to be a security expert to spot this. Just look for anywhere user input goes straight from the request into something powerful, like a database call or a system command.

#Step 4: Check error handling, not just the happy path

AI-generated code has a habit of failing silently. A function throws an error, nothing logs it, and the app just quietly does the wrong thing instead of crashing loudly. Silent failures are worse than obvious ones because you don't find out until a user complains, sometimes weeks later.

Try breaking things on purpose. Submit an empty form. Disconnect your internet mid-request. Enter text where a number should go. See what actually happens versus what you assumed would happen.

#Step 5: Test under something closer to real load

A demo with three test accounts tells you almost nothing about how the app behaves with real traffic. If you can, simulate a bit of concurrent usage, or at minimum look at your database queries and ask whether they'd still be fast with ten thousand rows instead of ten.

#Step 6: Review the data model for scale problems

AI tools are good at getting a data model working for the first version of a feature. They're less good at anticipating the second and third version. Look at your database schema and ask: if this feature grows, does the structure hold up, or does it fall apart the moment you add one more relationship nobody planned for.

#Step 7: Check for dead code and duplicated logic

If you re-prompted the same feature three or four times to get it right, there's a good chance old versions of that logic are still sitting somewhere in the codebase, unused but not deleted. This isn't dangerous by itself, but it makes the app harder to maintain and easier to break by accident later.

#AI-generated code review checklist

A shorter version you can actually run through in ten minutes:

#Common AI-generated code vulnerabilities

A few patterns show up again and again across AI-built apps, regardless of which tool generated the code:

• Missing rate limiting on expensive endpoints, especially anything that calls an LLM API per request
• Admin routes that check for a login but not for an admin role
• Environment files accidentally included in the deployed bundle
• Overly permissive CORS settings copied straight from a tutorial
• Session tokens that never expire, or expire far later than they should

None of these are exotic. They're the kind of thing an experienced developer spots in minutes and a first-time reviewer misses entirely, simply because nobody told them to look.

#Vibe coding best practices for your next build

A few habits make the next thing you build meaningfully safer, without slowing you down much:

Keep prompts specific about security requirements, not just features. Instead of "add a login system," try "add a login system with proper session expiry and role-based access control." You'll be surprised how much this changes the output.

Re-read generated code before accepting it, at least the parts touching auth, payments, or user data. You don't need to understand every line. You just need to slow down for the sensitive 20%.

Keep a running list of what you've never checked. Honestly, most people building solo have a mental list already; the trick is writing it down so it actually gets checked before launch instead of getting forgotten.

#Vibe coding vs. traditional coding: what changes for review

The core difference isn't that AI code is worse. It's that the traditional review habits most teams already have (pull requests, senior sign-off, style checks) were built around human-written code, and a lot of teams building solo with AI tools skip that step entirely because there's no second developer around to insist on it.

#A quick real-world example

Picture a small two-person team building a booking app for local tutors, the kind of project a lot of solo founders and small studios take on with Lovable or Bolt these days. The app worked. Bookings went through, payments looked fine, the demo impressed everyone.

Nobody had checked the admin panel's access control though. Turned out any logged-in user, tutor or student, could technically hit the admin API routes directly, because the check only confirmed someone was logged in, not what role they had. It never got exploited, because nobody malicious found it in time. But it sat there for weeks, live, waiting.

This is a genuinely common pattern, not an unusual one. Basically every vibe-coded app that skips a proper review has some version of this sitting somewhere. The good news is it's almost always a small fix once someone actually finds it. The bad news is finding it requires someone to specifically go looking, because it never shows up in normal testing.

#When to do it yourself vs. when to hire a vibe coding audit

The checklist above will catch a real amount of what's wrong. But there's a point where self-review stops being enough, usually right around the moment real money or real user data enters the picture. If you're about to onboard paying customers, raise a funding round, or hand this codebase to a new hire, it's worth getting a proper vibe coding audit done by someone who does this for a living. It's a lot cheaper than finding out the hard way.

#Frequently asked questions

A quick tip before you go: keep this checklist somewhere you'll actually find it again. Bookmark it, save it as a note, whatever works. Most people run through it once before their first launch and then completely forget it exists for the next feature. The second and third review matters just as much as the first one, maybe more, since that's usually when new endpoints and new auth logic quietly get added without anyone re-checking the basics.

#The short version

AI-generated code isn't automatically dangerous, and it isn't automatically safe either. It's just unverified until somebody actually looks. Run through the checklist above before you launch. If two or more red flags show up, or if you're heading toward real users and real money, it's worth having a professional look before you find out what you missed the expensive way.