Row Level Security

When you need granular authorization rules, nothing beats Postgres's Row Level Security (RLS).

Row Level Security in Supabase

Supabase allows convenient and secure data access from the browser, as long as you enable RLS.

RLS must always be enabled on any tables stored in an exposed schema. By default, this is the public schema.

RLS is enabled by default on tables created with the Table Editor in the dashboard. If you create one in raw SQL or with the SQL editor, remember to enable RLS yourself:

alter table <schema_name>.<table_name>
enable row level security;

RLS is incredibly powerful and flexible, allowing you to write complex SQL rules that fit your unique business needs. RLS can be combined with Supabase Auth for end-to-end user security from the browser to the database.

RLS is a Postgres primitive and can provide "defense in depth" to protect your data from malicious actors even when accessed through third-party tooling.

Policies

Policies are Postgres's rule engine. Policies are easy to understand once you get the hang of them. Each policy is attached to a table, and the policy is executed every time a table is accessed.

You can just think of them as adding a WHERE clause to every query. For example a policy like this ...

create policy "Individuals can view their own todos."
on todos for select
using ( (select auth.uid()) = user_id );

.. would translate to this whenever a user tries to select from the todos table:

select *
from todos
where auth.uid() = todos.user_id;
-- Policy is implicitly added.

Enabling Row Level Security

You can enable RLS for any table using the enable row level security clause:

alter table "table_name" enable row level security;

Once you have enabled RLS, no data will be accessible via the API when using the public anon key, until you create policies.

Auto-enable RLS for new tables

If you want RLS enabled automatically for new tables, you can create an event trigger that runs after table creation. This uses a Postgres event trigger to call ALTER TABLE ... ENABLE ROW LEVEL SECURITY on each newly created table.

CREATE OR REPLACE FUNCTION rls_auto_enable()
RETURNS EVENT_TRIGGER
LANGUAGE plpgsql
SECURITY DEFINER
SET search_path = pg_catalog
AS $$
DECLARE
  cmd record;
BEGIN
  FOR cmd IN
    SELECT *
    FROM pg_event_trigger_ddl_commands()
    WHERE command_tag IN ('CREATE TABLE', 'CREATE TABLE AS', 'SELECT INTO')
      AND object_type IN ('table','partitioned table')
  LOOP
     IF cmd.schema_name IS NOT NULL AND cmd.schema_name IN ('public') AND cmd.schema_name NOT IN ('pg_catalog','information_schema') AND cmd.schema_name NOT LIKE 'pg_toast%' AND cmd.schema_name NOT LIKE 'pg_temp%' THEN
      BEGIN
        EXECUTE format('alter table if exists %s enable row level security', cmd.object_identity);
        RAISE LOG 'rls_auto_enable: enabled RLS on %', cmd.object_identity;
      EXCEPTION
        WHEN OTHERS THEN
          RAISE LOG 'rls_auto_enable: failed to enable RLS on %', cmd.object_identity;
      END;
     ELSE
        RAISE LOG 'rls_auto_enable: skip % (either system schema or not in enforced list: %.)', cmd.object_identity, cmd.schema_name;
     END IF;
  END LOOP;
END;
$$;

DROP EVENT TRIGGER IF EXISTS ensure_rls;
CREATE EVENT TRIGGER ensure_rls
ON ddl_command_end
WHEN TAG IN ('CREATE TABLE', 'CREATE TABLE AS', 'SELECT INTO')
EXECUTE FUNCTION rls_auto_enable();

Note that this applies to tables created after the trigger is installed. Existing tables still need RLS enabled manually.

When a request is made without an authenticated user (e.g., no access token is provided or the session has expired), auth.uid() returns null.

This means that a policy like:

USING (auth.uid() = user_id)

will silently fail for unauthenticated users, because:

null = user_id

is always false in SQL.

To avoid confusion and make your intention clear, we recommend explicitly checking for authentication:

USING (auth.uid() IS NOT NULL AND auth.uid() = user_id)

Authenticated and unauthenticated roles

Supabase maps every request to one of the roles:

• anon: an unauthenticated request (the user is not logged in)
• authenticated: an authenticated request (the user is logged in)

These are actually Postgres Roles. You can use these roles within your Policies using the TO clause:

create policy "Profiles are viewable by everyone"
on profiles for select
to authenticated, anon
using ( true );

-- OR

create policy "Public profiles are viewable only by authenticated users"
on profiles for select
to authenticated
using ( true );

Using the anon Postgres role is different from an anonymous user in Supabase Auth. An anonymous user assumes the authenticated role to access the database and can be differentiated from a permanent user by checking the is_anonymous claim in the JWT.

Creating policies

Policies are SQL logic that you attach to a Postgres table. You can attach as many policies as you want to each table.

Supabase provides some helpers that simplify RLS if you're using Supabase Auth. We'll use these helpers to illustrate some basic policies:

SELECT policies

You can specify select policies with the using clause.

Let's say you have a table called profiles in the public schema and you want to enable read access to everyone.

-- 1. Create table
create table profiles (
  id uuid primary key,
  user_id uuid references auth.users,
  avatar_url text
);

-- 2. Enable RLS
alter table profiles enable row level security;

-- 3. Create Policy
create policy "Public profiles are visible to everyone."
on profiles for select
to anon         -- the Postgres Role (recommended)
using ( true ); -- the actual Policy

Alternatively, if you only wanted users to be able to see their own profiles:

create policy "User can see their own profile only."
on profiles
for select using ( (select auth.uid()) = user_id );

INSERT policies

You can specify insert policies with the with check clause. The with check expression ensures that any new row data adheres to the policy constraints.

Let's say you have a table called profiles in the public schema and you only want users to be able to create a profile for themselves. In that case, we want to check their User ID matches the value that they are trying to insert:

-- 1. Create table
create table profiles (
  id uuid primary key,
  user_id uuid references auth.users,
  avatar_url text
);

-- 2. Enable RLS
alter table profiles enable row level security;

-- 3. Create Policy
create policy "Users can create a profile."
on profiles for insert
to authenticated                          -- the Postgres Role (recommended)
with check ( (select auth.uid()) = user_id );      -- the actual Policy

UPDATE policies

You can specify update policies by combining both the using and with check expressions.

The using clause represents the condition that must be true for the update to be allowed, and with check clause ensures that the updates made adhere to the policy constraints.

Let's say you have a table called profiles in the public schema and you only want users to be able to update their own profile.

You can create a policy where the using clause checks if the user owns the profile being updated. And the with check clause ensures that, in the resultant row, users do not change the user_id to a value that is not equal to their User ID, maintaining that the modified profile still meets the ownership condition.

-- 1. Create table
create table profiles (
  id uuid primary key,
  user_id uuid references auth.users,
  avatar_url text
);

-- 2. Enable RLS
alter table profiles enable row level security;

-- 3. Create Policy
create policy "Users can update their own profile."
on profiles for update
to authenticated                    -- the Postgres Role (recommended)
using ( (select auth.uid()) = user_id )       -- checks if the existing row complies with the policy expression
with check ( (select auth.uid()) = user_id ); -- checks if the new row complies with the policy expression

If no with check expression is defined, then the using expression will be used both to determine which rows are visible (normal USING case) and which new rows will be allowed to be added (WITH CHECK case).

To perform an UPDATE operation, a corresponding SELECT policy is required. Without a SELECT policy, the UPDATE operation will not work as expected.

DELETE policies

You can specify delete policies with the using clause.

Let's say you have a table called profiles in the public schema and you only want users to be able to delete their own profile:

-- 1. Create table
create table profiles (
  id uuid primary key,
  user_id uuid references auth.users,
  avatar_url text
);

-- 2. Enable RLS
alter table profiles enable row level security;

-- 3. Create Policy
create policy "Users can delete a profile."
on profiles for delete
to authenticated                     -- the Postgres Role (recommended)
using ( (select auth.uid()) = user_id );      -- the actual Policy

Views

Views bypass RLS by default because they are usually created with the postgres user. This is a feature of Postgres, which automatically creates views with security definer.

In Postgres 15 and above, you can make a view obey the RLS policies of the underlying tables when invoked by anon and authenticated roles by setting security_invoker = true.

create view <VIEW_NAME>
with(security_invoker = true)
as select <QUERY>

In older versions of Postgres, protect your views by revoking access from the anon and authenticated roles, or by putting them in an unexposed schema.

Helper functions

Supabase provides some helper functions that make it easier to write Policies.

auth.uid()

Returns the ID of the user making the request.

auth.jwt()

Not all information present in the JWT should be used in RLS policies. For instance, creating an RLS policy that relies on the user_metadata claim can create security issues in your application as this information can be modified by authenticated end users.

Returns the JWT of the user making the request. Anything that you store in the user's raw_app_meta_data column or the raw_user_meta_data column will be accessible using this function. It's important to know the distinction between these two:

• raw_user_meta_data - can be updated by the authenticated user using the supabase.auth.update() function. It is not a good place to store authorization data.
• raw_app_meta_data - cannot be updated by the user, so it's a good place to store authorization data.

The auth.jwt() function is extremely versatile. For example, if you store some team data inside app_metadata, you can use it to determine whether a particular user belongs to a team. For example, if this was an array of IDs:

create policy "User is in team"
on my_table
to authenticated
using ( team_id in (select auth.jwt() -> 'app_metadata' -> 'teams'));

Keep in mind that a JWT is not always "fresh". In the example above, even if you remove a user from a team and update the app_metadata field, that will not be reflected using auth.jwt() until the user's JWT is refreshed.

Also, if you are using Cookies for Auth, then you must be mindful of the JWT size. Some browsers are limited to 4096 bytes for each cookie, and so the total size of your JWT should be small enough to fit inside this limitation.

MFA

The auth.jwt() function can be used to check for Multi-Factor Authentication. For example, you could restrict a user from updating their profile unless they have at least 2 levels of authentication (Assurance Level 2):

create policy "Restrict updates."
on profiles
as restrictive
for update
to authenticated using (
  (select auth.jwt()->>'aal') = 'aal2'
);

Bypassing Row Level Security

Supabase provides special "Service" keys, which can be used to bypass RLS. These should never be used in the browser or exposed to customers, but they are useful for administrative tasks.

Supabase will adhere to the RLS policy of the signed-in user, even if the client library is initialized with a Service Key.

You can also create new Postgres Roles which can bypass Row Level Security using the "bypass RLS" privilege:

alter role "role_name" with bypassrls;

This can be useful for system-level access. You should never share login credentials for any Postgres Role with this privilege.

RLS performance recommendations

Every authorization system has an impact on performance. While row level security is powerful, the performance impact is important to keep in mind. This is especially true for queries that scan every row in a table - like many select operations, including those using limit, offset, and ordering.

Based on a series of tests, we have a few recommendations for RLS:

Add indexes

Make sure you've added indexes on any columns used within the Policies which are not already indexed (or primary keys). For a Policy like this:

create policy "rls_test_select" on test_table
to authenticated
using ( (select auth.uid()) = user_id );

You can add an index like:

create index userid
on test_table
using btree (user_id);

Benchmarks

After: user_id indexed</details> |

Call functions with select

You can use select statement to improve policies that use functions. For example, instead of this:

create policy "rls_test_select" on test_table
to authenticated
using ( auth.uid() = user_id );

You can do:

create policy "rls_test_select" on test_table
to authenticated
using ( (select auth.uid()) = user_id );

This method works well for JWT functions like auth.uid() and auth.jwt() as well as security definer Functions. Wrapping the function causes an initPlan to be run by the Postgres optimizer, which allows it to "cache" the results per-statement, rather than calling the function on each row.

You can only use this technique if the results of the query or function do not change based on the row data.

Benchmarks

After: (select auth.uid()) = user_id</details> | | test2b-wrappedSQL-isadmin | 11,000 | 7 | 99.94% | <details className="cursor-pointer">Before: is_admin() table join

After: (select is_admin()) table join</details> | | test2c-wrappedSQL-two-functions | 11,000 | 10 | 99.91% | <details className="cursor-pointer">Before: is_admin() OR auth.uid() = user_id

After: (select is_admin()) OR (select auth.uid() = user_id)</details> | | test2d-wrappedSQL-sd-fun | 178,000 | 12 | 99.993% | <details className="cursor-pointer">Before: has_role() = role

After: (select has_role()) = role</details> | | test2e-wrappedSQL-sd-fun-array | 173000 | 16 | 99.991% | <details className="cursor-pointer">Before: team_id=any(user_teams())

After: team_id=any(array(select user_teams()))</details> |

Add filters to every query

Policies are "implicit where clauses," so it's common to run select statements without any filters. This is a bad pattern for performance. Instead of doing this (JS client example):

const { data } = supabase
  .from('table')
  .select()

You should always add a filter:

const { data } = supabase
  .from('table')
  .select()
  .eq('user_id', userId)

Even though this duplicates the contents of the Policy, Postgres can use the filter to construct a better query plan.

Benchmarks

After: add .eq or where on user_id</details> |

Use security definer functions

A "security definer" function runs using the same role that created the function. This means that if you create a role with a superuser (like postgres), then that function will have bypassrls privileges. For example, if you had a policy like this:

create policy "rls_test_select" on test_table
to authenticated
using (
  exists (
    select 1 from roles_table
    where (select auth.uid()) = user_id and role = 'good_role'
  )
);

We can instead create a security definer function which can scan roles_table without any RLS penalties:

create function private.has_good_role()
returns boolean
language plpgsql
security definer -- will run as the creator
as $$
begin
  return exists (
    select 1 from roles_table
    where (select auth.uid()) = user_id and role = 'good_role'
  );
end;
$$;

-- Update our policy to use this function:
create policy "rls_test_select"
on test_table
to authenticated
using ( (select private.has_good_role()) );

Security-definer functions should never be created in a schema in the "Exposed schemas" inside your API settings`.

Minimize joins

You can often rewrite your Policies to avoid joins between the source and the target table. Instead, try to organize your policy to fetch all the relevant data from the target table into an array or set, then you can use an IN or ANY operation in your filter.

For example, this is an example of a slow policy which joins the source test_table to the target team_user:

create policy "rls_test_select" on test_table
to authenticated
using (
  (select auth.uid()) in (
    select user_id
    from team_user
    where team_user.team_id = team_id -- joins to the source "test_table.team_id"
  )
);

We can rewrite this to avoid this join, and instead select the filter criteria into a set:

create policy "rls_test_select" on test_table
to authenticated
using (
  team_id in (
    select team_id
    from team_user
    where user_id = (select auth.uid()) -- no join
  )
);

In this case you can also consider using a security definer function to bypass RLS on the join table:

If the list exceeds 1000 items, a different approach may be needed or you may need to analyze the approach to ensure that the performance is acceptable.

Benchmarks

After: col in table join on auth.uid()</details> |

Specify roles in your policies

Always use the Role of inside your policies, specified by the TO operator. For example, instead of this query:

create policy "rls_test_select" on rls_test
using ( auth.uid() = user_id );

Use:

create policy "rls_test_select" on rls_test
to authenticated
using ( (select auth.uid()) = user_id );

This prevents the policy ( (select auth.uid()) = user_id ) from running for any anon users, since the execution stops at the to authenticated step.

Benchmarks

After: TO authenticated (anon accessing)</details> |

More resources

• Testing your database
• RLS Guide and Best Practices
• Community repo on testing RLS using pgTAP and dbdev