AI Code Auditor: What an AI Code Audit Covers and Delivers

An AI code auditor reviews an application built with AI tools (Lovable, Bolt, Replit, Cursor, v0, Claude Code and others) and tells you what’s dangerous, what’s fragile and what’s fine, ranked by risk and with a concrete fix for each finding. The point of an AI code audit is to decide what to fix first, and whether to clean up or rebuild, before you put real users, data or payments on the app.

What an AI code audit covers

Area	What the auditor checks
Data access	Database rules (e.g., Supabase Row Level Security), who can read and write each table, storage bucket permissions
Authentication	Sign-up, login, password reset, session handling, email verification
Authorization	Admin routes and actions enforced on the server, not just hidden in the UI
Secrets	API keys in frontend code or Git history; which keys need rotating
Payments	Webhook verification, server-side price checks, idempotency
Dependencies	Known vulnerable or abandoned packages
Reliability	Error handling, retries, timeouts, race conditions
Data model	Schema, constraints, indexes, migrations under version control
Deployment	Build reproducibility, environment separation, rollback
Backups	Whether backups exist and a restore has been tested
Maintainability	Duplication, dead code, structure that the AI keeps tripping over

Data access is usually the top finding. In May 2025, CVE-2025-48757 showed that 170 of 1,645 apps in Lovable’s showcase let anyone read or write database tables with the public anon key.

How the audit works

1. Scope call: what the app does, who uses it, what data and money flow through it.
2. Access: read-only access to your repository, database settings and hosting, through individual accounts you control.
3. Automated checks: dependency audit, secret scanning, build check.
4. Manual review: access rules, auth flows, payment logic, and the critical user journeys.
5. Verification: checking findings against your environment (never against someone else’s app).
6. Report and walkthrough: findings explained in plain language, with a prioritized fix plan.

What the report should contain

• Executive summary: can you launch or not, and what must change first.
• Findings by severity (critical / high / medium / low), each with evidence, impact and a recommended fix.
• Cleanup vs. rebuild recommendation, with reasons.
• A fix plan you can hand to any engineer or use with your AI tool.

If a report is just a scanner’s output pasted into a PDF, it’s not an audit.

Automated tools vs. a human auditor

Automated tools are useful and fast:

npm audit                 # known vulnerable dependencies
gitleaks detect           # secrets in the repo and its history

But they can’t tell whether a user should be able to see another user’s invoices, or whether your admin check happens only in the browser. That requires someone who understands what the app is supposed to allow. The best audits combine both.

Findings that show up again and again

AI tools make the same kinds of mistakes across very different apps. These are the patterns an auditor looks for first:

• Checks that live only in the browser. The “Admin” button is hidden for normal users, but the database call behind it works for anyone who sends it.
• Prices calculated on the client. The checkout sends the amount to charge, so a modified request can pay less.
• Webhooks that trust whatever arrives. A payment-confirmation endpoint that marks orders as paid without verifying the provider’s signature.
• One giant table with a role column the user can edit. Self-service promotion to admin.
• Keys with too much power. A server-side key that bypasses all database rules, shipped in the frontend bundle or committed to the repo.
• Error messages that leak internals. Stack traces, SQL or third-party responses shown to users.
• No migrations. The schema was changed by hand in a dashboard, so nobody can recreate the database or know what changed.

None of these show up as a red error. The app works, which is exactly why they survive until someone looks.

What you can check yourself first

Before paying for an audit, a founder can do a quick pass and make the audit faster:

1. Make a list of every service the app uses and who has admin access to each.
2. Search the repository for words like secret, service_role and sk_live and note where they appear.
3. Log in as two different test users and try to open the other user’s records by changing an ID in the URL.
4. Confirm you can find the last backup, and when it was made.

Anything you find goes to the auditor as a starting point.

When an audit isn’t the first step

If the app doesn’t build, doesn’t deploy, or the core flow is still unfinished, an audit will mostly report “incomplete.” In that case, get it working first, then audit before launch.

Choosing an AI code auditor

• Senior production experience: they’ve run systems with real users and incidents.
• Familiarity with the platforms AI tools use: Supabase, Firebase, Vercel, Netlify, Stripe.
• Defensive scope: they test your app, with your permission, in your accounts.
• A sample report or outline before you commit.
• No conflict of interest: an audit should be useful even if you hire someone else for the fixes.

After the audit

1. Fix critical findings immediately (open data, leaked keys).
2. Add tests around auth, payments and data access.
3. Re-check the critical findings after the fixes.
4. Schedule a lighter review after major new features.

Details in how to fix AI-generated code and vibe coding cleanup services.

When to get help

Black Engine Technologies is a software engineering studio in Monterrey, Mexico (UTC-6). We have 12+ years shipping production software, including licensed fintech payment systems where audits and compliance are routine, and we work daily with AI coding agents. Send us a WhatsApp message to scope an audit of your app.