What is AI code security?
AI tools write code fast — and skip the security defaults a careful engineer applies without thinking. This is what that costs, and how to catch it before your app is live.
Why AI-generated code needs its own security lens
AI coding tools turn a sentence into working software. What they optimize for is 'does it run' — not 'is it safe to be reachable by strangers.' The security defaults a careful engineer applies out of habit (lock the database down, keep secrets out of the bundle, expire sessions) are exactly the invisible steps a language model tends to skip, because nobody asked for them in the prompt.
The result is an app that demos perfectly and is quietly wide open. 'It works' and 'it's safe' are different claims, and the gap between them is where incidents live.
The failure classes we see most
Missing row-level security. The database is reachable and every row is readable, because RLS was never switched on. This single unchecked setting is behind some of the largest AI-app exposures on record.
Exposed secrets. API keys, database URLs, and payment tokens left in the client bundle or committed to the repo. Committed credentials are the most common initial-access vector in cloud breaches.
Weak or missing auth configuration. Tokens that never expire, sessions that don't rotate, a CORS policy that trusts everyone — the combination that turns one leaked token into standing access.
Vulnerable dependencies. Known CVEs arriving through a dependency of a dependency, never surfaced because nobody ran an audit.
Missing transport and header hygiene. No HSTS, no CSP, cookies without HttpOnly, an expiring certificate — small gaps that turn an ordinary bug into a breach.
Where a security scan fits
The cheapest place to catch all of this is before the app is reachable — a scan that runs on the deploy itself, blocks the release when it finds something critical, and tells you exactly which line or setting to change. After launch is more expensive: by then the exposure is live and the fix is a scramble.
How ZipLoom approaches it
ZipLoom runs its security checks as a gate on every deploy — row-level security, secrets, CVEs, licenses, auth configuration, headers, TLS, and DNS — then ships the app to your platform of choice and keeps watching it. Findings come back ranked, with the fix formatted for the same AI tool that wrote the code. The scan isn't a separate product you have to remember to run; it's the step between your repo and your live URL.
Keep reading
See the checks ZipLoom runs on every deploy, or compare approaches: vs Snyk, vs OWASP ZAP, vs Vibe App Scanner.
- Add cited statistics with sources (e.g. Veracode AI-code study, CVE-2025-48757, Georgia Tech SSLab) — currently described in general terms only
- Optional: security-SME review of the failure-class descriptions
- Add 2-3 more internal links once the vulnerability-glossary pages exist