How I Would Fix database rules leaking customer data in a Lovable plus Supabase AI-built SaaS app Using Launch Ready.

How I Would Fix database rules leaking customer data in a Lovable plus Supabase AI-built SaaS app Using Launch Ready

The symptom is usually blunt: one user logs in and sees another customer's rows, files, or profile data. In a Lovable plus Supabase app, the most likely root cause is broken Row Level Security (RLS), often because a table was left public, a policy was written too broadly, or the frontend is calling Supabase with the wrong auth context.

The first thing I would inspect is not the UI. I would check the Supabase tables, RLS status, and policies for every table that stores customer data, then confirm whether the app is using the anon key correctly and whether any server-side code is bypassing RLS with service role access.

Triage in the First Hour

1. Check which tables are exposed.

• Open the Supabase dashboard.
• Review every table that contains customer records, billing data, messages, uploads, or internal notes.
• Confirm RLS is enabled on each one.

2. Inspect all policies on those tables.

• Look for `using (true)`, `with check (true)`, or policies that only filter by role instead of user ownership.
• Check whether select, insert, update, and delete policies are all present where needed.

3. Verify auth identity in the browser.

• Open the app in an incognito window.
• Sign in as two different test users.
• Confirm `auth.uid()` matches the logged-in account expected by each request.

4. Review recent deploys from Lovable.

• Check what changed in the last release.
• Look for new tables, renamed columns, copied prompts, or generated CRUD pages that may have skipped security checks.

5. Check Supabase logs and API activity.

• Look for requests returning more rows than expected.
• Watch for queries hitting protected tables without filters.
• Note any spikes in reads from anonymous traffic.

6. Inspect storage buckets if files are involved.

• Confirm buckets are private unless there is a strong reason not to be.
• Check storage policies separately from table policies.

7. Review environment variables and client keys.

• Confirm the anon key is used in frontend code only.
• Confirm service role keys never ship to Lovable-generated client code or browser bundles.

8. Check edge functions or backend routes.

• If any route uses service role access, confirm it re-applies ownership checks before returning data.

9. Capture evidence before changing anything.

• Save screenshots of current policy settings.
• Export SQL for affected tables and policies if possible.

10. Freeze new releases until containment is done.

• A leaking data issue is not a styling bug.
• Shipping more frontend changes before fixing access control usually makes cleanup slower.

-- Quick RLS sanity check
select schemaname, tablename, rowsecurity
from pg_tables
join pg_class on relname = tablename
join pg_namespace on pg_namespace.oid = pg_class.relnamespace
where schemaname = 'public';

Root Causes

1. RLS is disabled on one or more customer tables.

• How to confirm: In Supabase, check whether `rowsecurity` is off for affected tables.
• Why it happens: Lovable-generated apps often create working CRUD flows first and security second.

2. Policies are too broad.

• How to confirm: Search for policies that allow all authenticated users to read all rows.
• Common mistake: A policy like `using (auth.role() = 'authenticated')` without checking ownership.

3. Ownership column does not match auth identity.

• How to confirm: Compare `user_id`, `owner_id`, or `account_id` values against `auth.uid()`.
• Common mistake: The table stores a profile ID while the policy checks a user ID, so nothing actually matches safely.

4. Service role key is being used in the wrong place.

• How to confirm: Search Lovable-generated code and any custom API routes for `SUPABASE_SERVICE_ROLE_KEY`.
• Common mistake: The app uses service role access from client-side code or returns raw query results without filtering them again.

5. Nested relations are leaking through joins or views.

• How to confirm: Inspect views, RPC functions, and joined queries that combine public and private tables.
• Common mistake: The base table has RLS, but a view exposes more than intended.

6. Storage bucket rules are open even if table rules are correct.

• How to confirm: Review bucket privacy settings and storage policies separately from database policies.
• Common mistake: Records are protected but uploaded invoices or avatars are publicly readable.

The Fix Plan

My approach would be containment first, then repair, then verification. I would not try to "patch" this by adding frontend filters alone because that only hides bad access control and leaves the database exposed.

1. Contain exposure immediately.

• Disable public reads on affected tables if business operations can tolerate it briefly.
• If needed, temporarily block risky endpoints behind maintenance messaging while preserving login and support access.

2. Turn on RLS everywhere customer data lives.

• Enable RLS on all relevant tables first.
• Then add explicit allow-list policies per action:
• select only own rows
• insert only with matching owner ID
• update only own rows
• delete only own rows

3. Standardize ownership fields.

• Pick one canonical field such as `user_id` or `account_id`.
• Backfill inconsistent records before tightening policies so legitimate users do not get locked out.

4. Replace broad policies with precise ones.

• Use `auth.uid()` checks tied to ownership columns.
• For team-based SaaS apps, use organization membership through a join table rather than guessing from email domains or roles in the frontend.

5. Lock down views and RPCs.

• Audit every view that returns customer data.
• Make sure functions run with least privilege and do not expose columns you would not show in a support screenshot.

6. Separate public metadata from private records.

• Keep marketing content and product catalog data apart from customer-specific data.
• This reduces accidental joins that leak information across accounts.

7. Fix storage access too if files exist.

• Make private buckets private by default.
• Add signed URL flows where customers need controlled access to documents or uploads.

8. Rebuild deployment safely in Launch Ready terms after security is stable: | Item | What I would set | | --- | --- | | Domain | Correct apex and www DNS records | | Email | SPF, DKIM, DMARC configured | | SSL | Valid certs on all active hosts | | CDN | Cloudflare caching for static assets | | Protection | DDoS protection enabled | | Secrets | Env vars moved out of client code | | Monitoring | Uptime checks + alerting live |

9. Re-deploy only after policy tests pass in staging first. 1. Clone production schema into staging if possible without copying sensitive data rawly. 2. Test with at least two separate user accounts plus one admin account. 3. Confirm no account can read another account's rows through UI or direct API calls.

10. Document what changed for handover. 1. List affected tables and buckets 2. Record new policy logic 3. Note any temporary workarounds removed 4. Save rollback steps if an edge case appears later

Regression Tests Before Redeploy

I would treat this as an API security release with QA gates, not a cosmetic fix.

Acceptance criteria:

• User A cannot read User B's records through UI requests or direct API calls
• User A cannot update or delete User B's records
• Anonymous users cannot read private customer data
• Storage objects marked private stay private
• Admin/support access works only through approved paths
• No production secrets appear in browser bundles or logs

Test plan: 1. Positive path test

• Log in as a valid user and confirm they can see only their own records.

2. Negative path test

• Log in as another valid user and confirm zero cross-account visibility.

3. Anonymous test

• Visit protected pages logged out and confirm no customer rows load silently in background calls.

4. Direct request test

• Inspect network requests and verify returned payloads contain only permitted rows.

5. Edge case test - Try empty accounts, newly created accounts, deleted accounts, and users without profile completion states.

6. Role test Ensure admin-only screens do not leak ordinary customer data unless explicitly intended.

7. Storage test Attempt file access using expired links, copied URLs, and unauthenticated sessions.

8. CI gate Require tests for policy-critical queries before merge, especially anything touching billing, messages, uploads, or account settings.

I would aim for at least 90 percent coverage on access-control-related query paths before shipping this back into production traffic.

Prevention

This kind of issue comes back when teams treat database rules as invisible plumbing instead of product behavior visible to customers when it fails badly enough.

Guardrails I would put in place:

• Code review checklist item for every new table: RLS enabled yes or no
• Policy review required before merge for any schema change touching customer data
• Least privilege by default for service role usage
• Separate staging credentials from production credentials
• Logging of denied access attempts without storing sensitive payloads
• Alerting on unusual row counts returned by protected endpoints
• Monthly permission audits for tables, storage buckets, views, and functions

For UX safety, I also like clear empty states instead of silent partial loads when permissions fail:

• "No records available" should not mask an authorization bug during testing
• Error states should say something failed without exposing internal IDs or SQL details

From a performance angle, keep authorization checks cheap:

• Index ownership columns like `user_id` or `account_id`
• Avoid expensive joins inside hot-path policies if you can model membership cleanly
• Watch p95 latency after adding stricter filters so login does not feel broken under load

When to Use Launch Ready

Launch Ready fits when you already have an app built in Lovable plus Supabase but need it made production-safe fast without turning your team into infrastructure firefighters for two weeks straight.

I would use this sprint when you need:

• Domain setup done correctly across apex, www, subdomains, redirects
• Email authentication fixed with SPF/DKIM/DMARC so transactional mail does not land in spam
• Cloudflare configured with SSL, caching, and DDoS protection
• Production deployment cleaned up with proper environment variables and secrets handling
• Uptime monitoring turned on before launch traffic starts hitting you hard

Price-wise it is straightforward:

• 48 hour delivery

What I need from you before I start: 1. Access to Supabase project settings with admin permissions where appropriate 2., Repository or Lovable project export plus deployment details 3., List of all environments currently live: local, staging, production

4., Domain registrar access if DNS changes are part of the sprint 5., Email provider access if sending mail is involved 6., A short list of which customer data must stay private versus what can be public

If your real problem is "the app works but we cannot trust it," Launch Ready gets the launch surface under control quickly while I fix the things that break trust first: secrets leakage, bad DNS, broken SSL, missing monitoring, and sloppy deployment hygiene.

Delivery Map

References

• https://roadmap.sh/api-security-best-practices
• https://roadmap.sh/cyber-security
• https://roadmap.sh/code-review-best-practices
• https://supabase.com/docs/guides/database/postgres/row-level-security
• https://supabase.com/docs/guides/storage/security

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Take the next step

If this is a problem in your product right now, here is what to do next:

• [Use the free Cyprian tools](/tools) - estimate cost, score app risk, check launch readiness, or pick the right service sprint.

• [Book a discovery call](/contact) - I will tell you honestly whether you need a sprint or if you can DIY the next step.

*Written by Cyprian Tinashe Aarons - senior full-stack and AI engineer helping founders rescue, launch, automate, and scale AI-built products.*