How to Test Supabase Row-Level Security Using an Open-Source Scanner 

If you are building on Supabase, you already know how powerful Row-Level Security can be. It gives you fine-grained control over who can read, update, or delete specific rows in your database. The problem is not the feature itself. The problem is assuming it is configured correctly just because it works in development. 

Supabase RLS testing is often overlooked until something breaks or, worse, until sensitive data is exposed. In real-world audits, the most common issues are not complex exploits. They are small logic mistakes inside policies: missing conditions, incorrect role assumptions, or policies that look restrictive but aren’t. 

This article walks through how to test Supabase Row-Level Security using an open-source scanner, what to look for, and how it fits into broader Supabase security testing and AI penetration testing workflows. 

Why Supabase RLS Needs Dedicated Testing 

The system uses Supabase Row-Level Security to control database access. The system remains unprotected because improperly configured RLS policies allow users to access information via direct database queries, even when the frontend and API systems include security measures.   

The common risks that teams tend to underestimate are:  

• Rather than restricting access to specific users, the policy grants all authenticated users system access. 

• The conditions are based on user-provided information rather than verified claims from a trusted source. 

• Because of their overlapping nature, both policies create access rights that go beyond their original purpose. 

• The system lacks the required USING and WITH CHECK clauses. 

SQL RLS policies introduce security vulnerabilities because even minor logical flaws can lead to serious security issues. The rapid development of products requires more than just manual review as a primary solution.  

Automated testing meets the requirement for Supabase RLS testing. 

What an Open-Source Supabase RLS Scanner Does 

The open-source RLS scanner tests database security by executing simulated access attempts, which reveal unintentional access permissions. The scanner evaluates system operations through its testing process instead of reading policy documents. The system performs the following functions:

• Lists all tables that track RLS security measures. 

• Verifies the existence of security policies that protect against SELECT, INSERT, UPDATE, and DELETE operations. 

• Tests various access methods through its simulated access system. 

• Identifies which security rules provide excessive user permissions. 

This method approaches security testing using offensive security techniques. The system evaluates user access rights using a simple question: What can a user actually access? 

Instead of assessing configuration appearances, modern Supabase and AI penetration testing frameworks use behavior assessment as their security testing method. 

Step-by-Step: Testing Supabase RLS with an Open-Source Scanner 

1. Prepare a Safe Testing Environment  

Many tools focus on reading data, but testing should always occur in environments that do not affect current production work. A staging database, free of production data, is required for the testing process.  

Make sure:  

• RLS is enabled on all relevant tables. 

• The actual production environment should be used to test the deployment of policies. 

• You have a service key or test credentials. 

Testing against incomplete configurations can lead to misleading results. 

2. Install the Scanner 

Most open-source Supabase RLS testing tools can be installed as: 

• A browser extension 

• A CLI-based scanner 

• A local Node or Python utility 

Follow the project documentation for setup. Installation typically involves: 

• Providing your Supabase project URL 

• Supplying an anon key or service key 

• Configuring role-based test parameters 

Always verify that you are using official repository sources. 

3. Run Role-Based Access Checks 

The primary advantage of Supabase RLS testing system is its ability to validate role-based access control system access rights. 

The testing process needs to create simulations for all of the following user types:  

• Anonymous user  

• Authenticated user  

• Admin user  

• Custom roles testing (if applicable)  

The scanner utilizes SELECT queries to check all tables and present its findings about: 

• Tables accessible without authentication 

• Tables accessible by incorrect roles 

• Policies missing for specific operations 

The use of anonymous users to access user profiles and financial records constitutes a significant security breach because it allows unauthorised access to sensitive information. 

4. Review Policy Logic Carefully 

Automated scanning flags symptoms. You still need to inspect root causes. 

Common misconfigurations include: 

Using only auth.role() = ‘authenticated’ without ownership checks 

This allows any logged-in user to read all rows. 

Relying on unverified JWT claims 

If policies trust claims that are not strictly controlled, users may gain unintended access. 

Missing WITH CHECK conditions 

Even if SELECT is restricted, INSERT or UPDATE may allow users to modify records improperly. 

After each flagged issue, review the SQL policy directly in Supabase. 

5. Retest After Fixes 

Security testing follows an iterative process. The implementation of policy changes requires you to: 

• Redeploy the adjusted policies 

• Rerun the scanner 

• The team needs to verify access which security systems had identified as dangerous now remains restricted 

The loop serves as an essential component. Supabase security testing should not be a one-time activity before launch. It should be part of release cycles. 

Where AI Penetration Testing Fits In 

Traditional scanners are rule-based. They check known patterns. But modern AI penetration testing goes a step further. 

AI-driven systems can: 

• Generate edge-case queries 

• Identify unusual access paths 

• Detect logical contradictions between policies 

• Simulate multi-step exploitation chains 

For example, a policy might correctly restrict SELECT access but allow INSERT in a way that indirectly exposes data through relationships. 

AI penetration testing tools analyze those cross-table interactions more effectively than static checks alone. 

For teams scaling quickly or handling sensitive data, combining open-source Supabase RLS testing with AI penetration testing provides stronger coverage. 

Building a Sustainable Supabase Security Testing Workflow 

To reduce long-term risk, integrate Supabase RLS testing into your development lifecycle: 

• Run RLS scans before production deployments 

• Include RLS validation in CI pipelines 

• Conduct quarterly Supabase security testing reviews 

• Combine manual review with automated scanning 

• Use AI penetration testing for deeper analysis 

Security failures in RLS rarely happen because the feature is flawed. They happen because assumptions go untested. 

Final Thoughts 

Supabase Row-Level Security is powerful. It allows teams to centralize access logic at the database level. But power without validation is risky. 

Testing Supabase RLS with an open-source scanner provides a practical first line of defense. It identifies common misconfigurations, highlights unintended access paths, and forces teams to validate real-world behavior instead of relying on theoretical policy logic. 

When combined with structured Supabase security testing and AI penetration testing, it becomes part of a mature security posture. 

If your application stores user data, financial records, or internal business logic, treat RLS policies as production codes. Test them. Break them. Fix them. And test again. 

Security is not about assuming policies are correct. It is about proving they are.