Your AI wrote the code. We verify what it ships.
AI tools get you to production in days. What they also ship — quietly — are the same classic flaws humans have been shipping forever: exposed environment files, weak session defaults, debug endpoints left on, vulnerable framework versions. Notra audits the running product and gates every finding on proof.
The same flaws, shipped faster
Generated apps get deployed fast, and config rides along: a .env, a backup archive, a secrets file served from the web root. The agent probes for these read-only and proves the exposure with the exact request.
Scaffolded auth is famously default-flavored: cookies without Secure or SameSite, tokens that outlive logout, debug endpoints that never got turned off. The session-chain and JWT proofs catch what the scaffold shipped with.
AI picks a framework and pins whatever version was in its training window. The audit fingerprints what is actually serving traffic and checks it against known CVEs — with the vulnerable version as evidence.
Why an evidence-gated audit fits AI-built code
Proof beats reassurance
A model can argue anything; a finding with the exact request and response cannot be argued with. Every Notra finding ships its own exhibit, so 'the code looks fine' meets 'here is the proof it isn't'.
The gate is the point
Vibe-coded output is high-variance. The ≥0.8 verification gate means the variance lands in Notra's reasoning — where you can watch it — instead of in your report as noise.
You can watch it think
The agent streams its reasoning while it works. Open the live trace on the watch page and see exactly how it went from crawl to conclusion — no black box.
What it doesn't do — stated plainly
Not a code review
It never reads your repo. If you want static review of the generated source, that's a different (and complementary) tool.
Not model testing
It tests the web application over HTTP, not prompt-injection logic inside the model. If your product's risk lives in the prompt layer, treat this as one layer of the answer.
Not a guarantee
A clean audit means nothing could be verified in the budget — a real result, not a guarantee. Novel business-logic chains still need creative humans.
The audit is the same product either way — AI-written or hand-written, the attacker doesn't care. Start with the free scorecard, or see the live agent run before spending anything.
Questions founders ask
The AI said the code is secure. Isn't that enough?
A model reviewing its own code has the same blind spot any author has. The failure modes that actually bite AI-built apps are rarely logic errors — they are the classics: a config file copied into the deploy, a session cookie missing its flags, a debug route left enabled. Those are proven or disproven with live requests, which is exactly what the audit does.
Which proof classes does the agent carry?
Verified proof classes include exposed-file checks (environment and config files reachable in production), JWT and session-chain weaknesses, and injection-class flaws — each re-proven with a harmless PoC before it ships. It fingerprints scaffold and framework versions and checks them against known CVEs.
Does Notra read my source code or my prompts?
No. Notra attacks the running product the way an attacker would — over HTTP, from the outside. It does not review source, models, prompts, or infrastructure. That outside-in view is also why it works regardless of which AI or framework wrote the code.
Run the free scorecard on your AI-built app — about a minute, no signup gymnastics.
Related: the sample verified report shows the evidence you'd get, the watch page shows the reasoning live, and early access is where AI-built-product teams are shaping the roadmap.