examples

Row Level Security (RLS) examples

These examples show how Row Level Security (RLS) problems ship in real apps — and what fixes actually work when tested via direct API access.

Why Row Level Security (RLS) examples matter

Without properly enforced RLS, any credentials that reach the table (often the anon/authenticated keys in your bundle) can read or modify every row, turning private data into a public endpoint.

Examples about Row Level Security (RLS)

Example	Summary	URL
RLS enabled but not forced	RLS was enabled but not forced, letting privileged paths bypass the filters.	/examples/row-level-security/rls-enabled-but-not-forced
RLS missing exposes user profiles	Disabled RLS let the public profiles table leak every profile even though the UI hid it.	/examples/row-level-security/rls-missing-exposes-profiles

Root cause → fix pattern analysis for Row Level Security (RLS)

Examples are most useful when you can translate them into a repeatable fix pattern. This table highlights the “why” behind each fix:

Example	Root cause	Fix pattern	URL
RLS enabled but not forced	Unforced RLS let owner and superuser sessions read data without hitting the policies, so some environments behaved as if the table was open.	Force RLS on every sensitive table, revoke the extra grants, and route backend actions through secure service endpoints.	/examples/row-level-security/rls-enabled-but-not-forced
RLS missing exposes user profiles	RLS was never enabled, so the anon key could call the API and enumerate every row.	Enable and force RLS, drop the broad grants, and shift the profile fetch to a backend endpoint that uses `service_role`.	/examples/row-level-security/rls-missing-exposes-profiles

How Row Level Security (RLS) failures typically happen

Believing a table is protected because a policy exists even though RLS is disabled or not forced.
Leaving broad SELECT/UPDATE grants on anon or authenticated while assuming policies will override them.
Trusting client-side filtering instead of letting the database enforce row-level restrictions.

Fix patterns that tend to work for Row Level Security (RLS)

Across these examples, the highest-leverage fixes share a theme: remove direct client access and make verification repeatable.

Backend-only access for sensitive operations (server endpoints enforce authorization).
Least-privilege grants: revoke broad privileges from anon/authenticated.
Small, testable policies if you intentionally keep client access — avoid complex conditions.
A verification step that proves direct access fails (not just that the UI hides data).

How to spot Row Level Security (RLS) in your own project (signals)

A direct API call returns rows/files even when the UI is supposed to restrict them.
RLS/policies exist, but access still succeeds (often because RLS is disabled or policies are too broad).
Permissions depend on the client behaving “nicely” (UI checks) rather than the database enforcing access.
After a migration, access behavior changes unexpectedly (drift).

How to use these examples to fix your own app

Match the scenario to your table/bucket/function setup.
Identify the root cause (not just the symptom).
Apply the relevant template or conversion guide.
Verify direct access fails for client credentials.
Document the rule so it doesn’t regress.

Verification checklist for Row Level Security (RLS) fixes

Reproduce the issue once using direct API access (anon/authenticated) so you know it’s real.
Apply the fix pattern (backend-only access + least privilege) using a template.
Repeat the same direct access call and confirm it now fails.
Confirm the app still works via backend endpoints for authorized users.
Re-scan after the fix and add a drift guard for the next migration.

Preventing Row Level Security (RLS) regressions (drift guard)

Re-run the same direct access test after every migration that touches auth, policies, grants, Storage, or functions.
Keep a short inventory of sensitive resources and treat them as server-only by default.
Review new tables/buckets/functions in code review with an access-control checklist.
If you intentionally allow client access, document the policy rationale and add tests for it.

Optional SQL checks for Row Level Security (RLS) (extra confidence)

If you like having a repeatable “proof”, add a small set of SQL checks to your process.

Confirm RLS status for tables involved (enabled/forced where appropriate).
List policies and read them as plain language: who can do what under what condition?
Audit grants to anon/authenticated and PUBLIC for tables, views, and functions tied to this topic.
If Storage/RPC is involved, explicitly audit bucket settings and EXECUTE grants.

These checks complement (not replace) the direct access tests shown in the examples.

Decision guide for Row Level Security (RLS): template vs conversion vs integration

If you’re here because you found this topic in a scan, the fastest path depends on whether the fix is a small config change or a workflow change.

Choose a template when you need a copy/paste change plus verification (tighten a policy/grant/bucket setting).
Choose a conversion when you need to change an access model end-to-end (unsafe → backend-only) with example transformations.
Choose an integration when the fix is a workflow pattern you’ll repeat (signed URLs, server-only RPC, backend endpoints).

If you’re unsure, start with the smallest template that removes direct client access, then add integrations for durability.

Evidence to keep after fixing Row Level Security (RLS) (makes reviews faster)

Teams often “fix” a topic but can’t prove it later. Save a few small artifacts so you can re-verify after migrations:

The direct access request you used before the fix (and the expected denial after).
A short boundary statement (who can access what, through which server endpoint).
The change you applied (policy/grant/bucket setting/EXECUTE revoke) and why.
The drift guard you’ll run after migrations (scan, checklist query, or release checklist item).

Glossary: Row Level Security (RLS) → /glossary/row-level-security

What to do after you fix one example (so it stays fixed)

One fixed example is great — but the real win is preventing drift.

Write a one-sentence boundary statement (who can access what, through which server path).
Keep the one direct access test you used before the fix (and expect it to fail after).
Re-run the same test after migrations that touch policies, grants, buckets, or functions.

If you can re-run the test and it still fails, you’ve turned a one-time fix into a durable control.