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Inherited code and AI. A staged guide to cleaning up your codebase and surfacing the critical bugs

Seven stages for auditing inherited codebases with AI. The statistic is brutal - 100% of inherited systems have at least one critical vulnerability the owner had no idea existed.

Marius Silo
CEO & Co-founder
9 min read
Marius at a MacBook in a dark server room with red error screens in the background
#legacy code#vibe-coding#security#Claude Code#Codex#dead code#code audit

Frequently asked questions

How long does an audit like this take?
It depends on the size of the codebase. A small vibe-coded product can be read in a couple of days, a twelve-year-old monolith demands weeks. What matters is the order, not the speed - fast at the start means slow at the finish.
Why start with the map, not with security?
Without context, a security scan is theatre. The AI finds generic things that often aren't even real problems in this particular system. We give the model a mermaid map first, and only then send it hunting for SQL injection, auth bypasses, and other real holes.
Does this process also work for vibe-coders who shipped a product in a month?
Yes, even more so. In vibe-coded systems the AI loves dropping in the newest package just because it's newest, leaves helper functions uncalled, and never asked about .env files or hardcoded API keys. All seven stages apply, the road is just shorter.
Is 100% of inherited systems really leaky?
By our experience over the past year - yes, every single one had at least one critical security issue the owner had no idea existed. The models keep getting stronger, the internet is full of automated scanners, and legacy code stays in place. The only question is who finds the gap first.