Common Security Issues In AI Generated Apps

A checklist tells you what to look for. It doesn’t replace someone actually opening the code and reading it, line by line, the way a burglar reads a house before deciding which window is unlocked. This is the kind of work we do every day at Sidekick Interactive, and across enough vibe coded apps we’ve noticed a pattern: the same handful of vulnerabilities show up again and again, regardless of who built the app or which platform they used.

This article isn’t about how we run a technical audit, and it isn’t an audit checklist to run through yourself. It’s a look at the specific AI code vulnerabilities we keep finding when an AI writes most of the code and nobody with a security background reads it afterward.

Why AI Writes Insecure Code With Total Confidence

Here’s the part that catches founders off guard. The mistakes aren’t subtle. A hardcoded password, an endpoint that trusts whatever ID the app sends it, a “temporary” AI feature that never got removed: this is the kind of thing a junior developer would get corrected on in their first code review.

What makes it dangerous is the delivery. The AI writes these mistakes with the same confidence as it writes the parts that work perfectly. There’s no hedge, no comment saying “this probably needs a second look.” Everything ships looking equally finished, which means the founder has no way to tell the solid 90% from the 10% that’s quietly exposed.

Hardcoded Credentials And Secrets Sitting In The Code

The most common issue by far. Database passwords, API keys, and session tokens end up typed directly into application files instead of living in environment variables. Once that happens, they tend to spread: the same key gets copied into a test script, then into a utility file, then into a second script nobody remembers writing.

We’ve also found full database dumps committed straight into the repository, complete with real email addresses and password hashes. And when a session secret was supposed to come from an environment variable that didn’t get set, the app fell back to a fixed default string instead of refusing to start.

None of this shows up in the UI. The app looks and behaves exactly the same whether the keys are hardcoded or not. The exposure only becomes visible the day someone with access to that code (a contractor, a leaked archive, an old collaborator) decides to look for it. And rotating the password afterward doesn’t undo the fact that it was exposed.

Broken Object-Level Authorization

This is the one that surprises non-technical founders the most, because it’s invisible from inside the app. AI-generated backends are usually good at authentication, confirming that you’re logged in. They’re far less consistent at authorization, confirming that you actually own the specific thing you’re trying to read or change.

In practice, this looks like an endpoint that accepts a record ID from the client and acts on it without checking who that record belongs to. Change a number in the request, and you’re looking at someone else’s data. We’ve also seen admin-level cleanup endpoints that any logged-in user could reach, and API responses that returned entire user records to the client, password hashes and billing fields included, because nobody built a filter to strip that data out before sending it.

A regular user clicking through the app will never trigger any of this. Someone with browser developer tools open for thirty seconds will.

SQL Injection And Prompt Injection

SQL injection is a solved problem in traditional development. It’s making a comeback in vibe coded apps, because the same discipline that eliminated it years ago (validating and escaping every input before it touches the database) isn’t something an AI reliably applies unless it’s explicitly told to.

Prompt injection is the newer version of the same idea, and it’s specific to apps with an AI feature built in. Say a form is supposed to collect someone’s name. Instead of typing a name, a user types an instruction: something like asking the system to export its database and send it to an external address. If that input gets fed straight into a prompt without separating “data” from “instructions,” the AI can follow it. We’ve seen apps where the only defense against this was a basic keyword filter, the kind that both misses obvious attacks and flags perfectly innocent text.

No Defense In Depth At The Database Layer

Most managed database platforms offer row-level security, a database-level rule that blocks a query from returning another user’s data even if the application code forgets to check. Vibe coded apps almost never turn this on. Every protection lives in the application layer instead, which means a single missed check in a single route can expose everything behind it.

On top of that, we routinely find personal information (dates of birth, behavioral data, full conversation history) stored in plain, unencrypted columns. Account-deletion flows often make things worse rather than better: they walk through a list of related tables manually, miss a few, and aren’t wrapped in a transaction, so a failure partway through leaves orphaned personal data behind. For apps handling data about children or subject to GDPR, that kind of incomplete deletion is a compliance failure with a paper trail attached to it.

AI Features Running Without Guardrails

If the app has a chat feature or any kind of AI-generated content, prompt injection isn’t the only risk. Once the model responds, few vibe coded apps screen that output before it reaches the user, and fewer still cap how much a single account can spend generating it. One abusive user can run up the AI bill for everyone, and content that should never have made it to a screen can slip straight through.

For an app aimed at families, health topics, or anything a store reviewer will scrutinize closely, an unmoderated AI feature with no spending limit isn’t a minor gap. It’s the kind of thing that gets flagged in review, or worse, after launch.

Why This Keeps Slipping Through

These issues aren’t exotic. Every one of them is the kind of thing an experienced engineer catches on a first read of the code. They survive because the code was never read that way. The AI generates something that looks finished, the interface works, and the assumption becomes that if the buttons all click correctly, the backend behind them must be solid too. It usually isn’t, and the gap only becomes visible once someone goes looking for it, which is exactly what a security-focused audit is for. For some apps, the pattern is bad enough that patching individual holes isn’t the right move at all, which is a separate fix-versus-rebuild decision worth having early.

Frequently Asked Questions

What is the most common security vulnerability in AI generated apps?

Hardcoded credentials show up more often than anything else we look at: API keys, database passwords, and session tokens typed directly into application files instead of stored as environment variables. Broken object-level authorization comes right behind it, where an endpoint trusts whatever ID a request sends it without checking who actually owns that data.

Can AI generated code have SQL injection vulnerabilities?

Yes, even though SQL injection has been a solved problem in traditional development for years. Vibe coding tools don’t reliably apply the input validation and escaping needed to prevent it unless a developer explicitly asks for it, which is why the vulnerability keeps resurfacing in AI generated backends.

Do vibe coding platforms fix these security issues on their own?

No. These platforms are built to generate features quickly, not to enforce security discipline. Catching hardcoded secrets, missing authorization checks, or unmoderated AI output requires someone reading the actual code, which is what a technical audit is for.

How do I know if my AI generated app has security vulnerabilities?

You generally can’t tell just from using the app. Every issue in this article is invisible from the interface, which behaves identically whether the backend is secure or not. The only reliable way to know is to have someone open the code and check it directly, ideally before you submit to the App Store or Google Play.

Fixing AI Code Vulnerabilities Before They Reach The App Store

You don’t need to memorize this list. What you need is someone who already knows where these mistakes hide, going through your code before Apple, Google, or your first real users find them for you.

That’s what we do at Sidekick Interactive. We open the code, trace how it talks to your database and any third-party services, and come back with a plain-language list of what’s actually at risk and what it takes to fix it.

If your app handles user accounts, payments, or any kind of personal data, it’s worth knowing what’s actually in there before you launch, not after.