Lovable Security Checklist: What to Check Before Launch

Lovable generates a full-stack app — UI, Supabase tables, auth, and API calls — from natural-language prompts. That speed is the point. But the model optimizes for something that runs and looks correct, not something that holds up when a curious user opens dev tools or replays a request. Most of the risk is not exotic: it is missing server-side checks, over-permissive database access, and secrets that leak into the browser bundle. The checklist below is ordered roughly by how often these issues block a safe launch.

1. Move authentication and authorization to the server

The single most common Lovable issue is auth that only exists in the UI. The app hides an admin button behind a role check in React, but the underlying API route or Supabase query is wide open. Anyone who reads the network tab can call it directly.

Authentication answers "who are you?" and authorization answers "are you allowed to do this?". Both have to run on the server for every sensitive read, write, and billing action. A hidden button is a UX detail, not a security control.

• Confirm every API route verifies the session before doing work, not just the page that renders the button.
• Check that role and ownership checks (is this user an admin, does this record belong to their workspace) happen server-side.
• Look for actions that trust a userId, role, or workspaceId sent from the client instead of deriving it from the verified session.

2. Enable Supabase Row Level Security before inviting users

Lovable leans heavily on Supabase, and Supabase tables are reachable directly from the browser through the client SDK. Without Row Level Security (RLS), the anon key can read and write rows it should never touch. This is the fastest way for one tenant to read another tenant's data.

• Make sure RLS is enabled on every table that holds user or tenant data — not just the ones you remember.
• Replace permissive policies like USING (true) with policies scoped to auth.uid() or a workspace membership check.
• Verify that insert and update policies use WITH CHECK so users can't write rows they couldn't read.
• Confirm the Supabase service-role key is never imported into client or edge code that ships to the browser.

3. Get secrets out of the client bundle and git history

AI-generated apps frequently inline a key where it was convenient, and frontend frameworks happily ship anything prefixed for public exposure. In Lovable and Vite-based projects, anything exposed as a public/VITE_ variable ends up in the browser bundle.

• Audit every key that reaches the client. Publishable Stripe keys and the Supabase anon key are designed to be public; service-role keys, secret API keys, and webhook secrets are not.
• Search the repo and git history for sk_live_, service_role, JWT secrets, OpenAI and GitHub tokens.
• Rotate any credential that was ever committed, even if you later removed it — history is forever until the key is rotated.

4. Close API route exposure and ID-based access

Every route shaped like /api/projects/[id], /api/invoices/[id], or /api/files/[id] is a chance for an insecure direct object reference (IDOR). If the handler queries only by the ID from the URL, a user can increment or guess IDs and read other people's records.

1. Query by both the requested ID and the authenticated owner or workspace.
2. Return 404 (not 403) for records the user can't access, so you don't confirm they exist.
3. Validate that the resource type matches the route — don't let a file ID resolve through an invoice handler.

5. Lock down admin routes and test accounts

Prototypes ship with a back door: a seeded admin account, a /admin page with no real gate, or a test user whose password is in the README. These are fine in development and dangerous in production.

• Remove or disable seeded test accounts and demo passwords before launch.
• Protect /admin and internal tooling with a real server-side role check, and consider IP or auth-provider restrictions.
• Make sure no "impersonate user" or debug endpoint is reachable in production builds.

6. Validate input and handle errors without leaking internals

Forms generated by AI tools tend to trust the frontend. The server should treat every payload as hostile: validate it against a schema, reject unexpected fields, and enforce types and limits before writing to the database. Pair that with error handling that fails closed.

• Validate request bodies with a shared schema (such as Zod) at the API boundary, not just in the UI.
• Return generic error messages to users; never surface stack traces, SQL errors, or raw exception text in production responses.
• Add error states for empty, loading, and failed cases so the app doesn't render a blank screen or a half-built page when something breaks.

7. Ship the boring launch-readiness basics

Security launch readiness is not only vulnerabilities. A real launch needs the operational layer that AI tools rarely add on their own.

• Security headers, a sensible robots.txt, and a sitemap.xml.
• Rate limits on auth, upload, and any AI-backed endpoint so launch-day traffic can't run up cost or lock out users.
• A privacy page, a security contact, and a basic plan for what happens if something goes wrong.

None of these checks require you to become a security engineer. They require going through the app the way an attacker would — request by request — instead of the way a happy user does. That review is exactly the kind of repeatable work a scanner is good at.