Your AI wrote the code. We verify what it ships.
Vibe-coding got you to production in a weekend. The trade nobody prints on the landing page: AI-written apps inherit the classic flaws at AI speed — exposed .env files, weak session defaults, debug endpoints, vulnerable scaffold versions. Notra verifies what actually shipped.
What AI-built apps ship with
Fast deploys carry config along: environment files, backups, secrets in the web root. The exposed-file verifier checks this read-only and proves any exposure with the exact request.
Generated session handling arrives with defaults attached — cookies missing Secure or SameSite, tokens that survive logout. The JWT and session-chain proofs find what the scaffold shipped with.
The codegen pins whatever version it learned on. The audit fingerprints what's actually serving traffic and checks it against known CVEs — and every flaw is re-proven with a harmless PoC before it counts.
Why verification fits your workflow
Argue with evidence, not opinions
Your AI says the code is fine; the audit says otherwise and attaches the request that proves it. Paste the exhibit back into your coding tool and the fix is a prompt away.
Watch it think
The agent streams its reasoning live — open the watch page during the run and see how it went from crawl to conclusion. No black box between you and the verdict.
Gate it going forward
Wire the CI/CD gate into your pipeline so the next AI-generated deploy gets checked before it ships, not after your users find it.
The honest boundaries
Outside-in only
Notra attacks the running product over HTTP. It doesn't read your repo, your prompts, or your model — if the risk lives in the prompt layer, this is one layer of the answer.
Known classes
It mechanically verifies the exploit classes it has proofs for — injection, exposed files, auth/session chains, CVE-exposed components. Novel chains still need creative humans.
Empty is honest
A clean report means nothing could be verified within the run's budget — a real result, not a guarantee, and the report says exactly what was covered.
The product is the same engine as the AI-app audit — this page is just the founder's-eye view of it.
Questions from builders
My AI coding tool already writes 'secure' code. Why audit?
Because the model optimizes for features, and security posture is what it never gets graded on. The result is usually not broken logic — it's the classics: a secrets file served from the web root, session cookies without their flags, a debug route left enabled. Those are proven or disproven with live requests, which no codegen does.
Is this just code review with extra steps?
No — and the difference is the product. Code review reads source and argues; Notra attacks your running product over HTTP and only reports what it proved, with the exact request and response attached. It also doesn't care which model or framework wrote the code.
What does it cost to try?
The scorecard is free: about 16 passive checks in roughly a minute. The Deep Audit is a flat $149 when you want the verified findings, and you can watch the agent reason live while it runs.
Create an account and run the free scorecard on what your AI built — about a minute, no card.
Related: watch the agent think live, read the sample verified report, or compare a manual pentest against the $149 audit. Keeping it covered after launch: Monitor.