Lovable security audit
Built with Lovable? Review these security risks first.
Lovable + Supabase is a beautiful stack for speed. It is also the stack where almost every audit we run finds a Row Level Security gap. The most common Lovable failures aren’t bugs in code — they’re defaults left in production.
Common problems
What we keep finding in Lovable codebases.
Public database policies
Especially with Supabase: RLS is enabled, but the policy reads using (true). Anyone with your project URL can SELECT every row in the table.
Examplecreate policy “allow read” on profiles for select using (true)— exposes every profile row to any anon visitor.Anonymous access left enabled
Temporary dev settings — anon role with broad table grants — accidentally go to production unchanged.
Admin actions in client logic
Critical actions like deleting users or refunding payments are wired up in client code with the assumption that “admins are the only ones who see the button.”
Prompt fixes
What to tell the model.
Paste these into your next conversation. They steer generation toward safer defaults — but they aren't a substitute for review.
For: Public database policies
“Review every Row Level Security policy manually. Replace using (true) with explicit auth.uid() = owner_id checks for every operation.”
For: Anonymous access left enabled
“Disable anonymous access for production. Grant the anon role only the minimum read access required for unauthenticated pages.”
For: Admin actions in client logic
“Move admin actions into protected server functions only. Never rely on UI visibility as an authorization control.”
Manual verification
The Lovable checklist.
Run through each item by hand before you ship. If anything is unclear, treat it as a red flag, not a green light.
- Run a SELECT * against every public table from a Supabase anon client.
- Inspect RLS policies in the dashboard for any using (true) pattern.
- Confirm the anon role has no INSERT/UPDATE/DELETE on private tables.
- Replace every client-side admin action with a Postgres function or edge function.
- Test “delete user” and “refund” flows as a low-privilege user.
If issues are already live
Damage control, in order.
If you suspect any of the above already shipped to real users, work the list top-to-bottom. Don’t skip rotation.
- 1Rotate the Supabase service role key immediately.
- 2Disable anon access on every table that doesn’t need it.
- 3Replace permissive policies with auth.uid()-scoped ones.
- 4Pull access logs for unusual SELECT volume from the anon role.
- 5Force a session reset before re-enabling production traffic.
Why AI-generated fixes still fail
The model that wrote the bug rarely sees it.
Same blind spots
The patterns that produced the vulnerability are baked into the model's training. Asking it to audit itself reproduces the same assumptions.
Context windows lie
The model sees the file you paste, not your auth middleware, your RLS policies, or the route you forgot to protect. It can't review what it can't see.
Confidence ≠ correctness
AI fixes look polished and read well. That's a signal of fluency, not of safety. Real verification needs a human who can hit the endpoint.
Don’t ask AI to audit AI
You wouldn’t let an intern grade their own homework.
Most builders type “is this secure?” back into the same chat that wrote the code. You need independent verification — someone whose context isn’t poisoned by what just got generated. That’s Kleared.
Most Lovable apps don’t fail because of code. They fail because of configuration.
Our Supabase RLS analyzer is built for exactly this stack. Kleared cross-checks pg_policies against your migrations and opens a fix-PR for every gap.
Before you launch
Run a real human security review.
Not another prompt. Kleared checks the boring stuff that breaks production:
- auth
- permissions
- secrets
- uploads
- database exposure
- API security
- payment flows
- production configs
So you can ship without guessing.