How I Would Fix database rules leaking customer data in a Lovable plus Supabase mobile app Using Launch Ready.

How I Would Fix database rules leaking customer data in a Lovable plus Supabase mobile app Using Launch Ready

The symptom is usually ugly and easy to miss at first: one user opens the app and sees another users records, private profile fields, or admin-only rows. In a Lovable plus Supabase mobile app, the most likely root cause is broken row-level security, a bad policy on one table, or client code querying too much data from a public endpoint.

The first thing I would inspect is the Supabase table policy setup, then the exact query path from the mobile app. I want to know whether the leak is coming from an over-permissive RLS rule, a service role key exposed in the client, or a join that bypasses the intended access model.

Triage in the First Hour

1. Check which tables contain customer data.

• List every table with PII, account data, billing data, messages, or support notes.
• Mark which ones have Row Level Security enabled and which ones do not.

2. Open Supabase Dashboard and review policies.

• Look at `SELECT`, `INSERT`, `UPDATE`, and `DELETE` policies per table.
• Flag any policy using `true`, missing `auth.uid()`, or broad role checks.

3. Inspect the mobile app data layer.

• Find every query generated by Lovable or custom code.
• Look for direct selects against sensitive tables without filters.

4. Check for leaked secrets in the build.

• Verify no service role key is bundled into the app.
• Confirm only anon/public keys are used client-side.

5. Review recent deploys and schema changes.

• Identify if the leak started after a new table, view, trigger, or policy change.
• Compare current branch to last known good release.

6. Audit auth state handling on mobile screens.

• Confirm users cannot access cached screens after sign-out.
• Check if stale session state is showing another users data.

7. Inspect logs and monitoring.

• Review Supabase logs for suspicious broad reads.
• Check whether unauthenticated requests are returning more rows than expected.

8. Freeze risky changes until containment is clear.

• Stop new releases for 24 hours if customer data exposure is active.
• If needed, disable affected endpoints or remove public access temporarily.

-- Quick policy audit starting point
select schemaname, tablename, policyname
from pg_policies
order by schemaname, tablename, policyname;

Root Causes

| Likely cause | What it looks like | How I confirm it | |---|---|---| | RLS disabled on sensitive tables | Any authenticated or anonymous user can read all rows | Check table settings in Supabase and test with two different user accounts | | Over-broad SELECT policy | Policy uses `true`, `auth.role() = 'authenticated'`, or misses ownership checks | Read the exact SQL policy and compare it to expected tenant logic | | Service role key exposed in client | App can read everything because it is using admin credentials | Search Lovable-generated code, env vars, and built assets for service role usage | | View or RPC bypassing RLS | A view returns more rows than the base table should allow | Inspect views, functions, and security definer settings | | Broken foreign key join logic | User queries their own record but joins into another users private rows | Trace the full query and returned payload shape in mobile logs | | Cache or offline storage leak | A previous users data remains visible after logout or account switch | Test sign-in/sign-out on a real device and inspect local storage/cache |

My default assumption is that this is not one bug. It is usually a stack of small trust mistakes: weak policies, too much client access, and no verification that each query stays inside its tenant boundary.

The Fix Plan

1. Contain first, then repair.

• If customer data is actively exposed, I would disable public reads on affected tables immediately.
• If needed, I would ship a temporary deny-all read posture for sensitive tables while preserving login and write paths for core flows.

2. Turn on RLS everywhere sensitive data lives.

• Every table with customer records should have RLS enabled before any other work.
• I would treat this as non-negotiable for profiles, subscriptions, orders, messages, attachments, and support tickets.

3. Rewrite policies around ownership or tenant membership.

• For single-user apps: allow access only when `user_id = auth.uid()`.
• For team apps: allow access only when the user belongs to that tenant through an approved membership table.

4. Remove any client-side admin access.

• Service role keys must never ship inside Lovable-generated mobile code.
• If admin operations are needed, move them behind server-side functions or protected edge logic.

5. Replace broad queries with scoped queries.

• I would update each screen to request only the columns it needs.
• I would avoid wildcard selects on tables containing private fields.

6. Lock down views and functions.

• Any function that reads protected data should be reviewed for security definer behavior.
• If a function needs elevated privileges, it should be tightly scoped and never callable from an untrusted client without checks.

7. Clean up session handling on mobile.

• Clear cached sensitive state on logout and account switch.
• Force refetch after auth changes so one users old data does not remain visible offline.

8. Add monitoring before redeploying.

• Track denied reads, unexpected row counts, auth failures, and policy errors.
• Set alerts for sudden spikes in returned rows from sensitive endpoints.

9. Deploy in stages.

• First validate in staging with seeded test accounts.
• Then deploy to production with feature flags or limited rollout if possible.

My preference here is to fix access control at the database layer first. UI masking alone does not protect customer data if Supabase still returns too much information.

Regression Tests Before Redeploy

I would not ship this fix until these checks pass:

1. Two-account isolation test

• Create User A and User B with different records.
• Confirm User A cannot read User B records through any screen or API call.

2. Anonymous access test

• Log out completely and try loading protected screens again.
• Confirm no private rows are returned without authentication.

3. Role-based policy test

• Verify authenticated users can only see their own rows or their tenants rows as designed.
• Verify admin-only paths are blocked unless explicitly intended.

4. Column exposure test

• Check response payloads for hidden fields like internal notes, tokens, billing metadata, or staff flags.
• Confirm only required columns are sent to the app.

5. Logout cache test

• Sign in as one user, open private screens, sign out, then sign in as another user on the same device.
• Confirm there is no stale cross-account content.

6. Join and view test

• Test any views or RPCs used by key screens against both allowed and disallowed accounts.
• Confirm they respect the same authorization rules as base tables.

7. Mobile smoke test

• Run through onboarding, profile view, list pages, detail pages, logout/login flow on iOS and Android builds if available.
• Watch for empty states turning into leaked fallback content.

Acceptance criteria I would use:

• Zero unauthorized row reads across 20+ manual checks across two accounts.
• No sensitive table accessible without matching ownership or tenant membership rules.
• No service role keys present in client builds or public repo files.
• No regressions in login success rate above 99 percent during smoke testing.

Prevention

I would put three guardrails in place so this does not come back next month:

1. Security review on every schema change

• Any new table gets RLS by default before launch approval.
• Any new policy must name who can read what and why in plain English.

2. Query review for Lovable-generated changes

• I would review generated screens for broad selects before merge.
• The goal is to catch accidental over-fetching before it hits production users.

3. Monitoring on access anomalies

• Alert when row counts jump unexpectedly for sensitive tables.
• Alert when denied reads spike after deploys because that often means a broken policy release rather than normal traffic noise.

I also recommend basic API security hygiene:

• Least privilege everywhere
• Tight CORS where applicable
• Secret scanning in CI
• Dependency updates reviewed monthly
• Audit logs kept long enough to investigate incidents

From a UX angle, do not hide auth failures behind vague errors. If a user loses access because of a valid security rule change after logout or token expiry, show a clear message instead of broken blank screens that look like app bugs.

When to Use Launch Ready

Launch Ready fits when you already have an app that works enough to demo but you need it safe enough to ship publicly without leaking customer data.

This sprint includes:

• DNS setup
• Redirects
• Subdomains
• Cloudflare protection
• SSL
• Caching setup
• DDoS protection
• SPF/DKIM/DMARC
• Production deployment
• Environment variables
• Secrets handling
• Uptime monitoring
• Handover checklist

What I need from you before starting: 1. Supabase project access with admin rights limited to what is necessary for remediation. 2. The Lovable project export or repository access if custom code exists outside Lovable syncs. 3. A list of sensitive tables and expected access rules per screen or user role. 4. Any recent screenshots showing where leaked data appears in the app. 5. Production domain registrar access if deployment work is part of the sprint.

If your issue includes live leakage of customer records plus launch blockers like domain misconfigurations or broken deployment settings, Launch Ready gives me enough room to fix both without stretching into a messy multi-week rebuild.

Delivery Map

References

1. Roadmap.sh API Security Best Practices: https://roadmap.sh/api-security-best-practices 2. Roadmap.sh Cyber Security: https://roadmap.sh/cyber-security 3. Supabase Row Level Security docs: https://supabase.com/docs/guides/database/postgres/row-level-security 4. Supabase Auth docs: https://supabase.com/docs/guides/auth 5. Cloudflare security docs: https://developers.cloudflare.com/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.*