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AI auditor for leaky Supabase backends

Build an AI-powered security auditor for Supabase and other no-code/BaaS backends that scans a project for missing RLS policies, public storage buckets, and leaked service keys, then auto-generates fixes or PRs. Target vibe-coders and solo founders who ship fast without a security background and don't know their database is exposed until it's too late.

via Reddit
Original post

What to build

A one-click security scanner for Supabase projects, built for solo founders and vibe-coders: it connects via the Supabase management API, flags tables with RLS disabled or misconfigured, public storage buckets, and leaked service-role keys, then auto-generates a fix (SQL migration or PR) the builder can apply instantly.

Thousands of fast-shipped Supabase apps are leaking data because their builders never knew RLS existed; a scanner that finds and auto-fixes the exposure turns a silent liability into a five-minute fix.

Demand

Solo founders and AI-assisted builders who ship Supabase backends without security review want this now because they're discovering exposure only after public call-outs or breaches, not before launch.

  • r/nocodeCommentary

    Reddit thread 'Systemic Data Exposure in Supabase Apps' - commenters with security backgrounds say this is unsurprising, pointing to a recurring, known pattern rather than a one-off incident

  • Independent security researchersNews

    Multiple writeups documenting live Supabase projects with RLS disabled or misconfigured, exposing user tables and PII to unauthenticated requests

  • Developer community forums (Reddit/Hacker News/Twitter-X)Commentary

    Indie developers and solo founders posting about discovering exposed Supabase databases or storage buckets post-launch, often via third-party scanning or responsible disclosure

Stack

  • Supabase Management API
  • Supabase CLI
  • Node.js/TypeScript
  • GitHub API (for auto-PR fixes)
  • Postgres RLS policy templates
  • Vercel/Cloudflare Workers for hosting the scan service

Solo + AI difficulty

Easy to MVP: reading RLS status and bucket ACLs via Supabase's API is straightforward, and a CLI script can ship in days. Harder part is generating safe, non-breaking RLS policy fixes across varied schemas; start with detection-only and manual-fix suggestions, add auto-PR generation later.

Entry threshold
Low capital barrier, buildable solo in days to a few weeks using Supabase's management API plus an LLM to interpret schema and policy gaps, but it requires real domain knowledge of RLS semantics and enough trust-building to get users to grant read access to their backend.
Window
12-24 months

Where to find first users

  • r/nocode
  • r/Supabase
  • Indie Hackers
  • Product Hunt launch
  • Supabase Discord

Competitors

  • Supabase's own dashboard security linter

Counter-signals & risks

  • Supabase itself provides built-in RLS tooling, dashboard warnings, and documentation nudging developers toward secure defaults, which may reduce the addressable severity of the problem over time

  • Generic cloud security posture management vendors could expand into BaaS-specific checks, commoditizing this niche quickly

  • Target customers (solo founders, vibe-coders) are typically price-sensitive and may not prioritize paying for security tooling until after a breach occurs, limiting willingness-to-pay and go-to-market efficiency

Original title: Systemic Data Exposure in Supabase Apps

AI auditor for leaky Supabase backends — Nichr