The developer of the alexeykrol.com project successfully remediated a serious security breach caused by a misconfiguration of Row Level Security (RLS) in Supabase. The use of advanced AI tools allowed for closing access to users' personal data and updating the entire infrastructure in just one hour, clearly demonstrating the high economic efficiency of implementing AI agents in development and incident response processes.

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What Happened

A critical vulnerability was discovered in the alexeykrol.com project: a public Supabase anon key allowed unauthorized access to PII (16,000 email addresses and correspondences) and also provided data write capabilities. Using Claude Code tools and the Opus model, the developer conducted an audit, fixed the RLS policies, and updated the infrastructure within 1 hour. This avoided hours of manual fixes and expensive external security audits.

Context

The vulnerability arose from a common error when using BaaS (Backend-as-a-Service) solutions, where developers mistakenly assume that public keys (anon-key) are secure by default without configuring appropriate Row Level Security (RLS) policies to restrict access to specific database rows.

Why It Matters for the Industry

This case confirms the transition from simple AI assistance to full-scale Incident Response using AI agents. Utilizing tools like Claude Code allows for a radical reduction in Mean Time to Remediation (MTTR) and demonstrates the practical Return on Investment (ROI) of AI: subscription costs are completely offset by savings in operational expenses and the prevention of data leaks.

Why It Matters for Users

For developers and product owners, this is a vital lesson: even a small configuration error in cloud services like Supabase or Firebase can lead to a mass leak of personal data. It is essential to always verify RLS settings, even when using public access keys.

What Is Not Yet Known / Limitations

There is a difference in how the situation is evaluated: technical specialists focus on the operational efficiency of the tools, while legal departments view such incidents through the lens of violating "privacy by design" principles and regulatory risks (e.g., GDPR).

Sources

Author

Look at AI, Editorial Team