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supabase-rls-check

supabase-rls-check

supabase-rls-check

Consent-gated, read-only configuration and anonymous-exposure checks for Supabase projects (PostgreSQL 15+, Python 3.11+). No database password or service_role key required.

Why this exists

Supabase applications intentionally ship a public anon JWT or sb_publishable_ key inside browser JavaScript bundles so client code can talk directly to PostgREST (/rest/v1/) and Storage. Because every visitor can read that key from DevTools, access control depends directly on PostgreSQL Row Level Security (RLS), policy expressions, table/function grants, view options, and Storage bucket visibility—along with never bundling a service_role token in frontend code.

During rapid prototyping, AI-assisted schema generation, or manual migrations, it is easy to leave a table in public without RLS, keep a temporary USING (true) policy, or forget that PostgreSQL grants EXECUTE to PUBLIC on new SECURITY DEFINER functions exposed at /rest/v1/rpc/*. supabase-rls-check helps project owners review those patterns safely without modifying data or saving table rows.

Related MCP server: database-sentinel

Installation

Requires Python 3.11 or newer.

Not on PyPI yet. Install from the GitHub release tag:

pipx install "git+https://github.com/Tthanh2k6/supabase-rls-check@v0.2.0"

Optional Model Context Protocol (MCP) server support (mcp>=1.2):

pipx install "supabase-rls-check[mcp] @ git+https://github.com/Tthanh2k6/supabase-rls-check@v0.2.0"

From source for development:

pip install -e ".[dev]"

Three-step usage

1. Export catalog metadata with check.sql

Print the bundled read-only SQL query (or open src/supabase_rls_check/sql/check.sql):

supabase-rls-check sql --print-sql > check.sql

Paste check.sql into the Supabase SQL Editor, adjust the sensitive-table name pattern if needed, run the SELECT, and export the single result set as CSV or JSON (rule,schema_name,object_name,policy_name). Only catalog metadata is queried—never application rows.

Validate and normalize the export:

supabase-rls-check sql --from export.csv --out sql_results.json --i-own-this-project

2. Optionally verify anonymous read exposure (probe)

Check what an unauthenticated caller can actually read with your public anon JWT or sb_publishable_ key, and optionally inspect your frontend origin for leaked service_role JWTs:

supabase-rls-check probe \
  --url https://YOUR_PROJECT.supabase.co \
  --anon-key YOUR_ANON_OR_PUBLISHABLE_KEY \
  --site https://your-app.example \
  --out probe_results.json \
  --i-own-this-project
  • Pass the project HTTPS origin to --url (not /rest/v1/).

  • Only anon JWTs and sb_publishable_ keys are accepted; service_role and authenticated tokens are rejected before any request is sent.

  • If OpenAPI schema discovery is disabled on PostgREST, pass known table/view names explicitly with --tables todos profiles.

3. Generate the standalone report and dry-run SQL template

supabase-rls-check report \
  --from sql_results.json probe_results.json \
  --out-dir review_out \
  --project "My Supabase App" \
  --i-own-this-project

This writes three files to review_out/:

  • report.html — standalone, offline-ready HTML report (A4 landscape print stylesheet for Print -> Save as PDF)

  • report.md — Markdown summary suitable for issues or pull requests

  • fix.sql — commented review template wrapped in BEGIN; ... ROLLBACK; (never executed by this tool; replace user_id with your real ownership column and test on staging first)

Add --lang vi to generate Vietnamese report text.

Explaining a rule and optional MCP mode

Inspect the rationale, caveats, and remediation example for any rule without touching a target project:

supabase-rls-check explain rls_disabled
supabase-rls-check explain definer_anon_exec

If installed with supabase-rls-check[mcp], start the local stdio MCP server:

supabase-rls-check mcp

The MCP server exposes only two local, read-only tools: analyze_sql_export(path) and explain_rule(rule). Network probing is intentionally excluded from MCP so AI clients never handle API keys or send HTTP requests through this tool.

Sample report and offline demo

Sample HTML report preview

Try the offline fictional dataset (demo/todo_ai.csv -> demo/report.md and demo/fix.sql):

supabase-rls-check report --from demo/todo_ai.csv --out-dir demo --project "todo-ai (fictional demo)" --i-own-this-project
supabase-rls-check report --from demo/todo_ai.csv --out-dir demo/vi --project "todo-ai (dữ liệu giả)" --lang vi --i-own-this-project

Eleven checks: SQL catalog rules

check.sql inspects PostgreSQL 15+ system catalogs and storage.buckets in a single read-only CTE query:

Rule ID

Severity

Meaning

rls_disabled

High

Table or partitioned table in public has Row Level Security disabled (relrowsecurity = false).

policy_true

High

Policy in public has an unconditional USING (true) or WITH CHECK (true) expression.

sensitive_anon_policy

High

Policy granted to anon or public on a table matching common sensitive names (user, profile, order, payment, invoice, customer, patient, secret, token, document).

definer_anon_exec

High

Non-trigger SECURITY DEFINER function in public is executable by anon (callable via /rest/v1/rpc/*).

definer_search_path

High

SECURITY DEFINER function in public has no fixed search_path set on the function definition.

view_not_invoker

High

View in public does not enable security_invoker, so base-table queries run with view-owner privileges.

public_bucket

Medium

Supabase Storage bucket in storage.buckets is marked public = true.

public_extension

Low

PostgreSQL extension is installed directly in public rather than a dedicated extensions schema.

policy_user_metadata

High

Policy in public reads user-editable user_metadata from auth.jwt() or request.jwt.claims. Use server-managed app_metadata or a protected authorization table.

policy_auth_per_row

Low

Policy on an RLS-enabled table in public calls auth.uid(), auth.jwt(), auth.role(), or current_setting() without a sub-select. Performance finding: use (select auth.uid()) for row-independent calls.

auth_users_exposed

Critical

Public view directly depends on auth.users, allows anon or authenticated to SELECT, and is materialized or lacks security_invoker. Drop it or harden a regular view and revoke access; publish only needed profile columns from a separate table.

The three new rules add only commented guidance to fix.sql; they never generate automatic DDL. Policy expression checks are heuristics over PostgreSQL's deparsed SQL. The authentication performance check recognizes directly SELECT-wrapped calls; complex subqueries need manual review. The Auth exposure check follows direct dependencies and returns no findings when auth.users is absent.

Additional checks from probe

When probe is run, two runtime checks complement the SQL catalog export:

  • anon_readable (High) — PostgREST returned visible rows for an unauthenticated GET/HEAD request using the anon or publishable key (records column names and visible row count only).

  • service_role_exposed (Critical) — A JWT whose payload decodes to role = "service_role" was found in frontend HTML or a directly linked same-origin script.

Tip: pass the anon key through the SUPABASE_ANON_KEY environment variable instead of --anon-key to keep it out of shell history.

How it is tested

  • 66 unit tests (pytest): consent flag enforcement, GET/HEAD-only probing with no redirects, response size limits, row values and keys never written to disk, service_role JWT detection, HTML/SQL escaping in reports, identifier quoting in fix.sql, English and Vietnamese output, all 11 catalog rules in reports/explanations, comment-only fixes for the new rules, and the MCP tool surface (no probe exposed).

  • Catalog query on a real PostgreSQL 15 (GitHub Actions): tests/fixtures/seed_pg.sql plants issues for all 11 rules next to safe controls. CI compares all 19 expected findings exactly, including overlapping view findings, and rejects extra or missing rows. Cases cover both JWT sources, all four authentication helpers, mixed wrapped/unwrapped calls, materialized views, role grants, and safe app_metadata/sub-select/invoker controls. The query runs with read-only transactions, including separate cases without an auth schema or auth.users table.

  • Abuse cases checked by hand: running any command without --i-own-this-project exits before touching the network; passing a service_role key to probe is refused; the MCP server cannot reach the network.

Ethical boundaries

  • Authorization required: Only scan projects you own or have explicit written authorization to test, including any --site frontend domain. Commands that process project exports or targets require --i-own-this-project or --have-written-consent.

  • Strictly read-only: probe sends GET and HEAD requests only—never POST, PUT, PATCH, DELETE, or RPC invocations. HTTP redirects are not followed, trust_env is disabled, responses are capped at 5 MB, and timeouts are fixed at 20 seconds.

  • No customer row storage: probe requests at most limit=1 row per relation in memory, extracts column names, discards row values immediately, and strips JWT patterns ([REDACTED]) from metadata and error messages.

Limitations (not a penetration test)

This tool is a configuration and anonymous-read review aid, not a penetration test and not a security certification:

  • Configuration findings are review signals, not proof of exploitability; public buckets or anonymous SELECT policies are sometimes intentional.

  • An empty finding list, HTTP 200 [], or 401/403 on a single request does not prove overall safety.

  • Does not test authenticated cross-user / multi-tenant isolation, INSERT/UPDATE/DELETE permissions, SQL function bodies, Edge Functions, custom Auth hooks, or complex storage.objects policy combinations.

  • --site inspects only the entry HTML document and up to 20 directly linked same-origin HTTPS <script src> files. Dynamically imported chunks, source maps, split/obfuscated tokens, and non-JWT secret formats (sb_secret_*) are outside its coverage, and JWT signatures are not verified.

  • fix.sql is a starting template ending in ROLLBACK;. Adding a new permissive policy does not override existing broad permissive policies—always export existing policies/grants and test two-user isolation on staging before committing changes.

Need a human review?

Need a manual review of RLS policies, SECURITY DEFINER function bodies, and two-user staging isolation? Book a fixed-scope review here: https://contra.com/s/sFUXWqOZ-supabase-security-check-for-lovable-bolt-and-v0-apps

License

Code is released under the MIT License (Copyright (c) 2026 Trung Thanh Tran). Bundled Bricolage Grotesque report fonts in assets/ are licensed under the SIL Open Font License 1.1 (assets/BricolageGrotesque-OFL.txt).

Available Tools

2 tools
analyze_sql_exportA

Validate and summarize a local CSV or JSON metadata export from check.sql.

NameRequiredDescriptionDefault
pathYes

Output Schema

NameRequiredDescription

No output parameters

TDQS

A3.7/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of disclosing behavior. It states the supported formats and the validation/summarization purpose, which strongly implies a read-only analysis operation, but it does not explicitly confirm non-destructiveness, describe validation behavior, or mention error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is a single front-loaded sentence with no unnecessary wording. It states the action and accepted input formats immediately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with an output schema, the description is largely complete: it identifies the operation, accepted file formats, and source context. The main remaining gap is that it does not explicitly state read-only behavior or clarify path resolution expectations, though these are minor given the tool's low complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the bare 'path' parameter. It does so by explaining that the path refers to a local CSV or JSON metadata export from check.sql, giving the agent clear meaning beyond the schema's generic string type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb pair, 'Validate and summarize,' with a clear resource: a local CSV or JSON metadata export from check.sql. It is distinguishable from a generic file reader, but it does not explicitly contrast itself with the sibling tool explain_rule.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the mention of local CSV/JSON exports from check.sql, so an agent can infer when the tool applies. However, there is no explicit when-to-use guidance, no exclusions, and no named alternative for related tasks such as explaining rules.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

explain_ruleB

Explain what a supabase-rls-check rule detects, caveats, and how to remediate it.

NameRequiredDescriptionDefault
ruleYes

Output Schema

NameRequiredDescription
resultYes

TDQS

B3.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry behavioral disclosure. It indicates the explanation covers detection logic, caveats, and remediation, which is useful output context, but it does not state that the operation is read-only, whether it requires a valid rule identifier, or what happens for an unknown rule.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no redundant wording. Every phrase contributes to the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter explain tool with an output schema, the description covers the conceptual return content. However, it leaves the required rule identifier's format and usage context undocumented, which is a meaningful gap given zero schema coverage and no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single 'rule' parameter has 0% schema description coverage, and the description only calls it a 'supabase-rls-check rule' without specifying format, source, or valid values. It does not compensate for the missing schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb, 'Explain', and a specific resource, 'supabase-rls-check rule', and lists the content covered: detects, caveats, and remediation. It does not explicitly differentiate from the sibling analyze_sql_export, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides no when-to-use or when-not-to-use guidance, and does not mention alternatives such as analyze_sql_export. The sentence describes output content rather than invocation context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv0.1.0
    • First observedanalyze_sql_export
    • First observedexplain_rule

TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation5/5

The two tools operate on completely different inputs: one validates/summarizes a local metadata export file, the other returns static documentation for a rule. There is no plausible way for an agent to confuse them.

Naming Consistency5/5

Both names use the same snake_case verb_noun pattern (analyze_sql_export, explain_rule) with clear, specific nouns. No stylistic drift between the two.

Tool Count3/5

Two tools is thin for a domain that includes running the check, enumerating rules, and interpreting results. It is defensible given a narrow scan-and-explain scope, but the surface feels minimal rather than well-scoped.

Completeness2/5

There is no way to list available rules (so explain_rule requires guessing rule names) and no tool to actually run or re-run check.sql, only to analyze a pre-existing export. Remediation is described in prose via explain_rule but never produced as SQL, leaving notable dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo recent community issues

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