You asked an AI to build a SaaS app.
A few prompts later, you have authentication, a dashboard, a database, an API, file uploads, Stripe payments, and a beautiful landing page.
You test it.
Login works.
The dashboard works.
You can create users and upload files.
You deploy it.
Then someone changes /api/users/1042 to /api/users/1043 and gets another customer's data.
That's not a hypothetical security lesson. It's one of the simplest ways an application can fail when authorization is implemented only where the developer expects users to behave.
And this is the uncomfortable part of vibe coding:
A working application is not necessarily a secure application.
AI is very good at producing code that satisfies the prompt. Security often depends on things you didn't put in the prompt, such as who is allowed to call an endpoint, what happens when a request is malformed, whether secrets are exposed in the client, and whether a user can manipulate an ID to reach someone else's data.
The 2025 OWASP Top 10 puts Broken Access Control at number one. OWASP's contributed testing data found that every application tested had some form of broken access control, with examples including ID tampering, missing authorization on APIs, privilege escalation, and forced browsing.
That's exactly the kind of problem a polished AI-generated app can hide.
Why vibe coding creates a security problem
The problem isn't that AI writes bad code all the time.
The problem is that AI can write a lot of code very quickly, and humans tend to review it based on whether the feature works.
That's a dangerous test.
Imagine you tell your coding agent:
> “Build a team dashboard where admins can manage members and regular users can view their own profile.”
The AI might create a beautiful frontend where regular users never see the admin button.
Looks secure.
But the browser isn't the security boundary.
A user can open DevTools, copy an API request, or simply call the endpoint themselves.
For example:
curl -H "Authorization: Bearer USER_TOKEN" \
https://example.com/api/admin/users
If the backend checks only whether the user is logged in, rather than whether they're an administrator, the frontend doesn't matter.
OWASP makes this point explicitly: access control has to be enforced in trusted server-side code. Hiding an admin route in JavaScript isn't an access-control mechanism.
This is one reason vibe-coded applications deserve a different review process.
You're not just reviewing code.
You're testing assumptions.
The first thing I'd test is authorization
If I get an AI-generated web application and have limited time to test it, I don't start by looking for obscure cryptographic bugs.
I test permissions.
Create two accounts.
Call the same API as both users.
Then start changing things.
Suppose User A owns:
/api/projects/4821
Try:
/api/projects/4822
Then try the same request with:
PUT /api/projects/4822
DELETE /api/projects/4822
Can User A read, modify, or delete User B's resource?
That's BOLA, or Broken Object Level Authorization, and it's one of the practical forms of broken access control that shows up in APIs.
The important thing is that you don't need an exotic exploit.
Sometimes the entire attack is changing one number.
OWASP's 2025 guidance specifically calls out modifying identifiers in URLs or requests to access another user's records, as well as APIs that lack proper controls on POST, PUT, and DELETE.
If your AI-generated app has users, teams, invoices, projects, documents, orders, or anything else represented by IDs, I'd test this before almost anything else.
Don't trust the AI when it says “security is implemented”
This is where I think developers need to change their habits.
Ask an AI:
> “Is this application secure?”
You'll often get a reassuring answer.
That's not a security assessment.
The model has access to the code it generated. It doesn't automatically have proof that the deployed application behaves safely under hostile input.
There is a huge difference between:
> “I added authentication middleware.”
and:
> “Every sensitive operation has server-side authorization, ownership checks, input validation, rate limits, secure session handling, and tests proving unauthorized users cannot perform it.”
The first is a coding statement.
The second is a security claim that needs evidence.
This distinction matters even more as AI coding agents become capable of creating entire applications instead of individual functions.
Recent Cloud Security Alliance research on AI-generated code found authorization flaws, missing access controls, and hardcoded credentials among the dominant failure patterns it observed. It also reported that security testing results varied substantially by methodology, which is another reason not to treat one automated scan as proof that an application is safe.
In other words, don't ask the AI whether it is secure. Test the application as if you don't trust it.
Five security holes I'd check before deploying an AI-built app
You don't need to become a penetration tester overnight.
Start with these.
1. Broken authorization
Test every sensitive endpoint with different users and roles.
Ask:
- Can a normal user call an admin endpoint?
- Can User A access User B's object?
- Can an unauthenticated visitor access private data?
- Can a user modify their own role?
- Does deleting something actually verify ownership?
This is the first thing I'd test.
2. Secrets in frontend code
Search your repository for things like:
API_KEY
SECRET
PASSWORD
TOKEN
PRIVATE_KEY
DATABASE_URL
Then inspect the browser bundle.
If a secret is required only by the server, it should not be shipped to the browser.
A .env file sitting in your project doesn't protect a secret if your build process accidentally embeds it into JavaScript.
And don't assume .gitignore fixes this after the fact. If a secret was already committed to Git, rotating the secret matters more than deleting the line in a later commit.
3. Injection
AI-generated applications still accept input from humans.
That means SQL injection, command injection, XSS, and other injection problems remain relevant.
OWASP's 2025 Top 10 ranks Injection at number five and notes that the category includes XSS and SQL injection.
For example, this is a bad pattern:
const query = `SELECT * FROM users WHERE email = '${email}'`;
The fact that an AI wrote it doesn't make it safer.
Use parameterized queries or your framework's safe database APIs instead.
4. Dangerous file uploads
If your app accepts profile pictures, PDFs, invoices, ZIP files, or anything else uploaded by users, test the upload system.
Check:
- File type validation
- File size limits
- Storage location
- Filename handling
- Whether uploaded files can execute as code
- Whether users can access another user's uploads
“It's just an image upload” is not a security control.
5. Security misconfiguration
AI-generated projects often come with development conveniences that shouldn't reach production.
Look for:
DEBUG=true
Verbose error messages.
Open database ports.
Permissive CORS.
Default credentials.
Exposed API documentation.
Public storage buckets.
Test endpoints.
Development routes.
OWASP moved Security Misconfiguration to #2 in its 2025 Top 10, reflecting how common configuration-driven security problems have become.
This is one area where “it works on my machine” can become “the entire database is publicly reachable.”
Your frontend is not a security boundary
This deserves its own section because AI-generated applications do this surprisingly often.
Imagine your React app contains:
if (user.role === "admin") {
showDeleteButton();
}
That's fine for controlling what the interface displays.
It does not mean the delete operation is protected.
An attacker doesn't need your button.
They can call:
DELETE /api/users/42
directly.
The backend should independently verify:
Is the requester authenticated?
Is the requester allowed to delete this resource?
Does this resource belong to their organization?
Is this action allowed for their role?
Every time.
OWASP's access-control guidance explicitly recommends enforcing authorization in trusted server-side code and denying access by default.
If your security depends on the user not clicking a hidden button, you don't have security.
You have a UI preference.
A security scan should happen before the first real customer
Here's the workflow I'd use for a vibe-coded project.
Step 1: Build quickly.
Use whatever AI coding tool you like.
Don't slow down the prototype unnecessarily.
Step 2: Freeze the feature set before launch.
Once the app actually works, stop adding random features for a moment.
Now test what exists.
Step 3: Scan the code and dependencies.
Look for secrets, dangerous patterns, vulnerable packages, insecure configuration, and obvious application-security issues.
Step 4: Test the deployed application.
Don't rely entirely on source-code analysis.
Attack your own API.
Test authentication.
Test authorization.
Test input handling.
Test uploads.
Test error responses.
Step 5: Test the browser experience.
A scanner can tell you an endpoint exists.
A real browser can tell you whether the login flow, checkout, dashboard, redirects, JavaScript, and other user journeys actually work.
Step 6: Fix before launch.
Not after the first security alert.
This is where automated security scanning becomes useful. It doesn't replace human review. It gives you a much faster way to find the obvious problems before they become someone else's discovery.
Torlyx is built around this lifecycle, with pre-deployment code scanning followed by vulnerability scanning and real-browser checks after deployment. The goal isn't to tell a vibe coder to stop building quickly. It's to make security part of the same workflow. Torlyx features
Vibe coding doesn't need less security. It needs earlier security.
I actually like vibe coding.
It lowers the cost of building software. Someone with a good idea can now get from an empty directory to a working product incredibly quickly.
That's useful.
But the old development workflow assumed that writing software was expensive enough that someone would probably review it carefully.
AI changes that equation.
You can generate authentication code, database models, API routes, payment flows, admin panels, background jobs, and deployment configuration in an afternoon.
The speed is great.
The security debt accumulates at the same speed.
Verizon's 2026 DBIR found that vulnerability exploitation accounted for 31% of breaches in its dataset, making it the leading initial access vector. The report also found that only 26% of critical vulnerabilities tracked in CISA's Known Exploited Vulnerabilities catalog were fully remediated during 2025.
So the answer isn't to stop using AI.
It's to change what “done” means.
Done shouldn't mean the app runs.
It should mean the app runs, the important paths work, unauthorized users can't cross permission boundaries, secrets aren't exposed, known vulnerabilities are addressed, and you've tested the deployed application rather than trusting the code generator's confidence.
And you don't need a security team to start doing that.
If you've just vibe-coded an application and you're about to put it online, run a free scan before you give the URL to your first real customer.
Then try to break your own authorization.
Start with /api/users/{id}.
Change the ID.
That's five minutes that can save you a very uncomfortable conversation later.
FAQ
Is vibe coding safe?
Vibe coding itself isn't inherently unsafe. The risk comes from shipping AI-generated code without testing the security assumptions around authentication, authorization, input validation, secrets, dependencies, and configuration.
Can AI-generated code be secure?
Yes, AI can generate secure code, but generated code still needs review and testing. A model saying that a feature is secure isn't evidence that the deployed application actually enforces the intended security controls.
How do I secure an app built with AI?
Start with authorization testing, secret scanning, dependency checks, input validation, secure file uploads, and production configuration. Then test the deployed application, because source-code review alone won't tell you how the real system behaves.