Which AI App Builders Keep Your Data Private?

Is Your Data Actually at Risk With an AI App Builder?

Yes, in a way the industry now tracks: Georgia Tech's Vibe Security Radar has confirmed 74 AI-linked CVEs so far, with 35 in March 2026 alone, more than all of 2025 combined. Most of these are not exotic AI failures. They are ordinary access-control mistakes that AI app builders wire straight into the database by default.

Of those 74 confirmed cases, 14 are rated critical and 25 are rated high severity. The trend line matters as much as the total: the tool found about 18 cases across the second half of 2025, then 56 in the first three months of 2026 alone, with March 2026 on its own accounting for 35 of those 56. This is a category that is getting worse, not settling down, as more non-technical builders ship real apps with real user accounts and real payment data attached.

The reason this keeps happening is structural, not a bug in any one model. A prompt-to-app builder wires the front end straight to the database so a non-coder can see results instantly, and that same wiring is what turns a missing permission into an exposed table. The builder that made shipping fast is the same builder that made the mistake reachable from any browser tab.

Here is how three AI app builders actually differ on the dimensions that matter once real data is involved, based on each vendor's own product and the OWASP Top 10:2025 risk category most of these incidents fall under, Broken Access Control:

That table is the short version. The rest of this article walks through the actual mechanism behind these exposures, what the code-generation research does and does not measure, and what to check before you put real user data in front of any of these builders.

Why Do Vibe-Coded Apps Leak Data in the First Place?

Most exposures trace to one missing setting: Row Level Security. Supabase's own docs are blunt about it, a table in an exposed schema without RLS is readable and writable by any role with a grant on it. CVE-2025-48757 documented exactly that gap in deployed Lovable projects, and the Moltbook breach showed what it costs in practice.

CVE-2025-48757, filed by security researcher Matt Palmer, describes a vulnerability in deployed Lovable-generated projects caused by default insufficient Row Level Security (RLS) policies on client-controlled direct database requests, the exact mechanism Supabase's documentation warns about. Because Lovable wires the browser directly to a Supabase database, a missing or weak RLS policy means anyone who opens the browser console can reach tables that were never meant to be public. That is not a flaw in Supabase itself, Supabase's own guide tells every developer to enable RLS on every table in an exposed schema. It is a default that a fast-shipping builder can leave off, and a non-technical founder has no reason to know to check.

The cost of that gap is not theoretical. Wiz Research disclosed that Moltbook, a vibe-coded app running on an unprotected Supabase table, exposed 35,000 emails and 1.5 million API keys from a single database. A separate table, agent_messages, exposed 4,060 private DM conversations between users. Wiz reported the exposure and it was patched within hours, but the data was live and reachable the entire time it sat unprotected. One exposed database produced three different categories of leaked data at once, credentials, personal contact information, and private conversations, because all three lived behind the same missing permission.

Moltbook is not an outlier case, either. Joylo's own research into The Rescue Economy of vibe-coded apps found builders describing the same failure points across independent threads: authentication, database and payment integrations breaking, and security holes shipped straight to production. An app that held up fine in a demo and then failed at first real traffic is the same pattern Moltbook shows at scale, a gap nobody caught until real users, and real data, hit it.

This is not a closed chapter. The Next Web reported in April 2026 that a Lovable exposure affecting projects created before November 2025 had been open for 48 days by the time it was reported, meaning older projects can carry this risk long after a fix ships for new ones. An app built and forgotten months ago does not get safer just because it has been quietly running. Broken access control, the OWASP category this falls under, sits at A01 on the OWASP Top 10:2025, the same list's number-one web application risk, and it has held that top spot precisely because it is easy to miss and expensive to discover after the fact.

How Secure Is AI-Generated Code, Really?

Not especially, and the industry's own numbers say so. Veracode's 2026 report puts the average security pass rate at 56%, with roughly 44% of generation tasks introducing a risky vulnerability. That figure measures isolated code at generation time, not a deployed app builder, so it's a baseline risk, not a verdict on any specific product.

What Veracode's numbers actually measure

Veracode's 2026 GenAI Code Security Report, Summer 2026 dataset, tested isolated code generation across four vulnerability categories: SQL injection, cross-site scripting, log injection, and insecure cryptography. GPT-5.5 led the current snapshot at 68%, while more than half of the models tested sat at 50-53%. That's a ceiling on how careful the AI itself is with a single generation task, not a measurement of what Lovable, Replit, or Joylo ship as a finished app, and it never tested any of those builders directly.

The pattern is not new. Veracode's earlier 2025 GenAI Code Security Report found that 45% of code samples failed security tests and introduced OWASP Top 10 vulnerabilities, with Java the riskiest language tested at a 72% failure rate. A year on, the average pass rate has crept up rather than jumped, evidence this is a slow, structural problem in how models generate code, not a solved one.

Developers already sense the gap between what AI code produces and what they can trust. Stack Overflow's 2025 Developer Survey found 84% of respondents were using or planning to use AI tools in development, but 46% actively distrust the accuracy of AI-generated output against 33% who trust it. The single biggest frustration, cited by 66% of respondents, was AI solutions that are "almost right, but not quite," the exact category of subtle bug that turns into a missing access-control check nobody caught. Generation-time accuracy and deployed-app security are two different questions, but they share a root cause: code that looks finished and was never actually reviewed.

What Should You Check Before Trusting a Builder With Real Data?

Building the app is the fast part, any of these tools can generate a working prototype in minutes. What decides whether that app is safe to put real data in front of is what you check before it goes live. Four things, all checkable today: Row Level Security enabled on every exposed database table, secrets kept server-side rather than in client-side code, broken access control treated as the top risk it is, and clarity on who's actually responsible. GDPR's Article 32 puts that security duty on you, the app owner, not on the builder tool.

Walk each of the four checks with the actual incident behind it in mind, not as an abstract audit item.

Row Level Security on every table. Go through each table in your database and confirm a policy actually restricts who can read and write it. Supabase's own docs are explicit that a table without RLS is readable and writable by any role with a grant on it, and that gap is exactly what CVE-2025-48757 documented in deployed Lovable projects.

Secrets server-side, never in client-side code. Moltbook's 1.5 million exposed API keys sat reachable because the app's client-side JavaScript, and the database behind it, held credentials a browser should never have direct access to. Anything an API key, a service role token, or a payment secret should live on the server, not in code a visitor's browser can read.

Broken access control as the top priority. It is not one risk among many, OWASP ranks it A01 on the 2025 list precisely because it is the easiest mistake to make and the most damaging one to miss. Treat every new table, every new endpoint, as a question of who can reach it before it ships.

Clarity on who is responsible. GDPR's Article 32 requires the data controller, the business collecting user data, to implement appropriate technical and organizational security measures. That responsibility sits with the app owner, not with whichever AI app builder generated the code, so the assumption that the platform "handles security" is the assumption worth checking first.

Which AI App Builders Actually Handle This Differently?

Not much, with one exception: none of the major builders except Joylo pairs the AI build with an in-house engineer and a guarantee that starts once you buy the recommended hours. Lovable and Replit hand off to community forums or freelancers when something breaks. Joylo audits every build for security and adds a named engineer on request.

On the AI generation itself, the builders are closer than the marketing suggests. Every one of them sits in Veracode's 50 to 68% pass-rate band, because they are all drawing on similar underlying models. The real split shows up after the AI stops, in what happens when the generated code turns out to have a gap in it.

Where the risk actually sits

Every AI builder in this set hands you your code, so code ownership was never the real question. Code portability is. Lovable builds on Supabase for both the database and the API layer, so the app talks to Supabase through its client SDK, its auth, and its RLS policies. Moving off that stack means either operating Supabase yourself or rewriting the entire data and API layer against a new backend, the SDK calls, the auth flows, and every RLS policy. Joylo generates a conventional React front end, Node API, and Postgres via Neon, standard components that move with a standard pg_dump and pg_restore, no proprietary runtime to unwind, and no rewrite required to move to your own AWS or Azure account.

Joylo's AI Confidence Score is a strong fit for anyone who wants that risk surfaced before it ships, not after a breach report. It scores every build across five domains, scalability, security, reliability, integrations, and code quality, on every plan including Free, and flags uncertain code before real users touch it. That audit runs automatically. What does not run automatically on a self-serve plan is a human reviewing that code, the certified architect review, database schema review, and CI/CD setup stay part of Co-Build or the Expert Assist add-on, not a default on Free, Solo Builder, or Starter.

When a finding needs a human, Expert Assist puts a named in-house Forward Deployed Engineer in your codebase, already familiar with the build, with a 24-hour first-response SLA. Joylo's written production guarantee begins once that scope is assessed, agreed, and the recommended engineering hours are purchased, it is not automatically included with a self-serve subscription. Within that agreed scope, if the engineering work runs longer than estimated, Joylo carries the overrun rather than handing back an unfinished app.

The engineering pedigree behind that guarantee is real and checkable: the same engineers behind Joylo built Waystone's ERMA risk-management platform at HST, for a client managing over $2 trillion in assets.

Three ways this plays out

A solo founder testing whether a subscription idea has real demand keeps everything on a free plan while the app is still a prototype, running on the AI Confidence Score's automatic checks, then runs a full security check before the first paying customer's card details touch the database.

A small team that imported an existing app after a bug-bounty scare on their previous builder needs the database access rules audited immediately, not eventually, which is exactly what the first block of Expert Assist hours is built to assess before any new feature work starts.

A funded startup handling health, financial, or other regulated personal data from day one skips the wait-and-see approach entirely, GDPR's Article 32 duty falls on the business the moment real user data is collected, not once the app has traction, so the architect review belongs in the launch plan, not the post-launch backlog.