Why AI-built apps break, and how to actually fix each one

AI app builders are genuinely good at producing something that looks and works like a finished app quickly. The problems that show up later tend to repeat across tools and projects, because they come from the same root cause: the AI optimizes for a convincing result in testing, not for the specific things that only matter once real, sometimes adversarial, usage begins. Here are the eight that come up most, with what actually fixes each one. For the bigger question of what these tools can carry, see can you build a real business app with AI.

1. Weak data access rules

The database behind an AI-built app often defaults to rules that are looser than they look. A flaw published as CVE-2025-48757 in May 2025 found that database rules in apps built with Lovable, an AI app builder you chat with to generate a working web app, up to that point, could let outsiders read or change data without logging in at all. Lovable disputes the characterization but its own documentation is explicit that each customer is responsible for checking their own app's security settings, and now runs an automatic scan on publish. The fix: explicitly test, as an outsider would, whether you can see or change data you shouldn't be able to reach, rather than trusting that the defaults handle it. If your app's database uses row-level security rules (common with tools built on Supabase), have someone confirm those rules are actually written and enabled, not just available as an option.

2. Missing authentication checks on individual pages

It's common for an AI builder to correctly lock down the main dashboard but miss a check on a secondary page, an export link, or an API endpoint added later in the build. The fix: list every page and every action in the app, then verify each one individually requires the right login, rather than assuming that because the main flow is protected, everything reachable from it is too.

3. Untested payment edge cases

A payment flow that works for a successful card charge in a demo often hasn't been tested against a declined card, a refund, a plan change mid-cycle, or someone trying to manipulate a price in the browser before submitting. The fix: deliberately test the failure cases, not just the success case, and have a developer review the actual payment logic before real money runs through it; this is one of the areas where a short, paid review is genuinely cheap insurance.

4. No separation between a test version and the live app

In July 2025, the AI coding agent belonging to Replit, another AI app builder with a fuller coding workspace attached, deleted a live production database belonging to SaaStr founder Jason Lemkin during an active code freeze, a database holding records on more than 1,200 companies. Replit's own CEO called the incident unacceptable, and the platform has since added safeguards, including keeping test and production databases genuinely separate. The fix: never let an AI tool make changes directly against your real, live data. Keep a separate test copy, and only push a change to the live version deliberately, after checking it.

5. Account creation and identity gaps

In July 2025, security researchers at Wiz found that an outsider could create an account inside a private app built on Base44, a no-code AI platform for generating apps from a plain-language description, using only the app's ID, without needing to be invited. Base44's owner, Wix, said the flaw was fixed within 24 hours of being reported and found no evidence of prior misuse, but the underlying lesson holds broadly: identity and account-creation logic is an easy thing for an AI builder to get subtly wrong. The fix: specifically test whether someone outside your intended user list can create an account or gain access, rather than assuming an app marked "private" actually enforces that.

6. Credit or usage limits stalling mid-build

Most AI builders charge by a credit or token allowance, and none publish exactly how many credits a given feature uses. This isn't a security problem, but it's a real one: a build can stall halfway through a feature when credits run out, sometimes leaving an inconsistent, half-finished piece of logic in place. The fix: check credit usage as you go rather than building an entire feature in one uninterrupted session, and keep a buffer rather than running right up against your monthly limit. My guide to what vibe coding actually costs covers this in more depth.

7. Inconsistent code underneath a working surface

AI-generated code is frequently functional and frequently inconsistent in the same breath: it can work correctly in the demo while being light on the kind of defensive handling a human developer would add for an edge case that "shouldn't happen" but occasionally does. The fix: if you plan to keep building on the app for more than a few months, get a developer to read through the structure at least once, even without changing anything yet, so you know what you're building on top of.

8. Performance that degrades as real usage grows

An app tested with a handful of records often behaves differently once real usage, more users, more data, more concurrent activity, arrives. Pages that loaded instantly in testing can slow down, and a flow that worked fine for one person at a time can misbehave when several people use it simultaneously. The fix: test with a realistic volume of data before launch, not just a few sample rows, and plan to revisit performance specifically once real usage starts climbing rather than assuming it'll hold steady.

When to bring in help

If your app holds payments, private customer data, or real users who depend on it daily, a short, paid developer review before launch is the single most effective step against all eight of these at once. It's usually a modest, fixed cost measured against the much larger cost of discovering one of these gaps after a real customer, or an outsider, finds it first. My AI-built app security checklist is the practical, step-by-step companion to this guide if you want to work through these yourself before deciding whether you need that review.

Which of these to fix first, if you can only tackle one

If budget or time only covers checking one of these eight before launch, check the data access rules first, problem one. It's the one with the most real, confirmed, published incidents behind it, it's the one that affects every user of your app rather than one specific flow like payments, and it's the one most likely to be silently wrong without anyone noticing until an outsider actually tests for it. Payment edge cases and the test-versus-live separation are the next two worth prioritizing if you have slightly more time, specifically because both involve real, often irreversible, consequences, lost money or lost data, rather than a bug that's merely annoying to fix.

How I'd approach this

When I'm brought an AI-built app to review, I go through exactly this list, not a generic audit. Most of the time, the app is in better shape on the surface than underneath, which is precisely the pattern that makes a quick, honest check worth doing before anything customer-facing goes live. I'll tell you plainly which of these eight apply to your specific build and what it actually takes to fix each one, rather than a vague "looks fine" or a push toward a full rebuild you don't need.