There's a promise that repeats across the entire "vibe coding" ecosystem: describe your idea, watch it assemble itself, ship before lunch. Lovable is probably the most polished example of that promise, and we won't caricature it — for testing an idea over a weekend, it's genuinely good.

The problem isn't the prototype. The problem is what happens once that prototype gets real users.

At THE ENDURING FOUNDER, the premise we start from never changes: build yourself to build what lasts. That's not a slogan for the header. It means choosing tools you can understand, audit, and defend as your product grows — not tools that make you feel productive right up until the day a security researcher finds your database sitting open on Google.

For Lovable, that day has already come. More than once.

This isn't a theoretical risk. It's a documented, repeated pattern, with named researchers and specific dates.

In February 2026, researcher Taimur Khan found 16 vulnerabilities — 6 of them critical — in an app built on Lovable to manage exams, used by students and staff at UC Berkeley, UC Davis, and several K-12 schools. The app had over 100,000 views and roughly 400 upvotes before anyone discovered that any unauthenticated visitor could access user records, send bulk emails, delete accounts, and even modify grades. The root cause, per the report, was that the AI-generated Supabase backend never implemented Row Level Security or role-based access control. The authentication function itself had the logic inverted: it blocked legitimate users while letting anyone else through. When asked, Lovable's CISO said the company offers "a free security scan before publishing," but that acting on it "remains at the discretion of the user" — and noted that "the vulnerable database is not hosted by Lovable." Technically true. Little comfort for the 18,697 user records exposed.

That wasn't an isolated case. Weeks earlier, a broken object-level authorization (BOLA) flaw in Lovable's API went 48 days unpatched after being reported, affecting thousands of projects created before November 2025: with as few as five API calls, an attacker with a free account could pull the source code and database credentials of other people's projects, because those Supabase credentials were hardcoded directly into exposed source. A separate study cited in the same coverage found that roughly 70% of apps built on Lovable had Row Level Security disabled entirely. The company's response, documented step by step: first it denied there was a breach and called the exposed data "intentional behaviour"; then it blamed unclear documentation; then it blamed its own bug bounty partner for "closing reports without escalation"; finally, a partial apology.

In May 2026, security firm RedAccess widened the lens beyond Lovable alone: scanning apps built with Lovable, Base44, Replit, and Netlify, it found 380,000 publicly accessible assets, roughly 5,000 of them containing sensitive corporate data — everything from shipping-port schedules and active UK clinical trials to unredacted patient-doctor conversations, classroom recordings involving minors, and internal financial data from a Brazilian bank. The cause, again, wasn't a sophisticated attack. It was privacy settings that default to public, combined with search engines quietly indexing the result. As RedAccess CEO Dor Zvi put it: "the idea that anyone can just create something that easily and put it into production on behalf of their company, without getting any permission — there's no limit to that."

And the pattern isn't unique to Lovable. Broader 2026 research on AI-generated code finds that 40–62% of it contains vulnerabilities, that 60% of vibe-coded apps expose API keys or database credentials in public repositories, and that AI-written code produces flaws at 2.74 times the rate of human-written code. Lovable is simply where the problem shows up most clearly, because it's where the largest number of non-technical founders are shipping products with real user data attached.

It isn't that Lovable's engineers are careless. It's structural.

When a tool optimizes for showing you a working result in seconds, every design decision quietly pushes toward permissive defaults: open databases, RLS turned off, credentials wherever they're easiest to read from. None of that is visible while you're the only one testing the app. It becomes visible the day you have real users — and one of them can, without hacking anything, using nothing more than a URL or a simple API call, see another user's data.

That specific risk — a user having the privilege to see another user's data — is the one we most want to flag here, because it's silent. Nothing breaks, nothing throws an error, nothing warns you. Your product keeps "working" perfectly while it leaks information. And by the time you find out, if you find out, it has already passed through hands it shouldn't have.

The technical cause, once you look closely, is always the same. Supabase — the database Lovable runs on under the hood — ships with an excellent security system called Row Level Security, which decides row by row who can read or write what. But that system has to be turned on and configured on purpose. If nobody does that — because the tool never required it, or because whoever built the app didn't know it existed — the database ends up with the equivalent of an unlocked front door. Anyone with the address can walk in.

We're not suggesting a return to hand-coding everything from scratch. That would ignore a decade of real progress. We're suggesting something simpler: use AI tools that generate real, auditable code that belongs to you — not a black box living inside the platform that sold it to you.

The stack we recommend at THE ENDURING FOUNDER has three layers, each with a clear job:

Design → Claude Design. This is Anthropic Labs' product for going from an idea or a prompt to a polished visual design: interfaces, mockups, decks. It reads your brand system — colors, typography, components — if you already have one, and you refine it through conversation or direct editing until it's right. The key difference from a single-shot "generate my app" tool: the design is its own artifact, exportable to PDF, PPTX, Canva, or HTML, then handed off to Claude Code with a single instruction to turn it into a real product.

Code → Claude Code. This is a coding agent that works directly inside your own repository, from your terminal, your IDE, or your GitHub workflow. The central difference from an all-in-one platform is that the code it produces is yours from the first commit: you can read it, version it, review it, and — this is what matters today — explicitly ask it to audit the security of what it built, including the access policies on your database.

Data → Supabase, configured on purpose. Supabase itself isn't the problem — it's an excellent Postgres database, and Row Level Security is one of its best features. The problem is publishing without turning it on and without writing the policies. Doing it right isn't hard; it's a handful of concrete steps, which in our downloadable PDF we've turned into prompts you can hand directly to Claude Code.

The difference between this stack and a platform like Lovable isn't day-one speed — both can take you from idea to something working in a single day. The difference is what happens six months later, when you have real users, investors asking about security, or simply the responsibility of not leaking the data of the people who trusted you. That's where "fast" and "lasting" stop being synonyms.

We put together a downloadable PDF, written for founders without a technical background, with the exact steps and prompts to:

Design your product in Claude Design with a coherent visual system from day one; hand that design to Claude Code and have it build the real application, connected to Supabase; and — the part we care about most — correctly configure Row Level Security in Supabase, with the specific prompts to ask Claude Code to enable RLS, write per-user access policies, and verify that no one can see or modify data that isn't theirs, before you publish anything.

It also includes a pre-launch checklist — the same checks that would have prevented every single case described above.

Because at THE ENDURING FOUNDER, we don't measure a product's success by how fast it shipped. We measure it by whether it's still standing — with its users' data intact — a year later.