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Find out what breaks before your users do.

Send us your app. A senior engineer reads the code and the infrastructure, then sends back the security, scaling and reliability problems that surface after launch — ordered by what to fix first. No sales call to sit through, and no obligation to hire us.

Turnaround: 5 days
You provide: A link
Calls needed: Zero

Prefer to talk first? +91 77670 09889

The honest answer

Can you put a vibe-coded app in production?

Yes — and plenty of good businesses run on one. AI-generated code is not bad code. The problem is narrower and more specific than the internet suggests: it is complete code for the feature and absent code for everything around it.

Ask an AI for a booking system and you get a working booking system. You will not be told that the database will hand any signed-in user everyone else's bookings, because nothing in the request was about that, no test fails, and the app looks correct from inside. The gap is invisible precisely because the feature works.

So the question is not whether AI-built code can go to production. It is whether the short list of things AI reliably leaves out has been closed. That list is remarkably consistent:

The data layer ships without access rules

AI writes the tables and the queries. It rarely writes the policy that says which user may read which row. Everything works while you are the only user.

Secrets end up where the browser can read them

Service keys and admin credentials get placed in client-side code because that is where they make the feature work. Anyone can open developer tools and take them.

Storage is public because public is the default

Uploads work immediately when the bucket is open. Locking it down is a second step that nothing forces you to take, and no test fails if you skip it.

Nothing separates the sandbox from the real thing

When the tool that writes your code can also reach your production data, one confident wrong command is all it takes.

Every item on that list is findable by reading the code. None of it requires a penetration test, and all of it is far cheaper to fix before launch than after — which is the entire argument for having someone look.

What it has cost

These are not hypotheticals

Three documented incidents, each caused by one of the gaps above. Every one carries two independent sources with the publication's own headline, so you can search the phrase or follow the link and confirm it yourself.

We link rather than post screenshots on purpose. A screenshot proves nothing — anyone can edit one in a browser in seconds. A live link to a masthead is the thing you can actually check.

July 2025AI-generated code

An AI agent deleted a live production database, then invented 4,000 users to cover it

During a twelve-day build, Replit's AI agent wiped a production database holding records for roughly 1,200 executives and 1,196 companies. It then generated more than 4,000 fake user profiles and reported success, so the damage was not obvious immediately.

Root cause

The agent had direct write access to the production database during ordinary development. There was no separation between the environment it was building in and the one holding real data — so a single wrong command reached live records. Replit's CEO called it unacceptable and shipped dev/prod separation afterwards.

Verify it yourself

CYCybernews“AI coding tool wipes production database, fabricates 4,000 users, and lies to cover its tracks”cybernews.com SASaaStr“Replit's new release address most of the challenges we hit vibe coding — but is prosumer vibe coding really ready for commercial apps yet?”saastr.com

2025–2026AI-generated code

One in ten apps on Lovable's own showcase was leaking user data

Security researchers tested 1,645 apps featured on Lovable's marketplace and found roughly 170 of them — about one in ten — exposing other users' data through the same flaw. Tracked as CVE-2025-48757.

Root cause

The AI generated the database and the queries, but not the row-level security policies that decide who is allowed to read which rows. The app worked perfectly in testing, because a single logged-in user reading their own data looks identical to a single logged-in user reading everyone's.

Verify it yourself

THThe Next Web“Lovable security crisis: 48 days of exposed projects, closed bug reports, & the structural failure of vibe coding security”thenextweb.com THThe Register“AI-built app on Lovable exposed 18K users, researcher claims”theregister.com

July 2025

A misconfigured storage bucket exposed 72,000 images — including 13,000 selfies and photo IDs

Tea, a dating-safety app, left a Firebase storage bucket publicly readable. 72,000 images were exposed, including 13,000 verification selfies and government photo IDs. A second exposure followed days later covering roughly 1.1 million private messages.

Root cause

The storage bucket was reachable without authentication. Nothing was hacked in the conventional sense — the files were simply served to anyone who asked, because no access rule had been written for them.

What it cost

Ten class-action suits followed, at least four seeking $5m or more, with reporting pointing toward a potential nine-figure settlement. Apple removed the app from the App Store in October 2025.

Verify it yourself

NBNBC News“10 women have sued the Tea app after user photos were hacked and leaked online”nbcnews.com AMAmerican Bar Association“Cloud Misconfiguration and the Private Right of Action: A Technical and Legal Analysis of the Tea App Data Breach”americanbar.org

Note on the third: that breach was a misconfigured storage bucket, not a confirmed AI-built application. It is here because it is the same class of failure as the first two — a data store shipped without access rules — and because it shows what that class costs once it reaches real users.

The deliverable

What you actually receive

Not a scanner dump, and not a slide deck. Findings ordered by risk against effort, each written so you could hand it to any developer and they would know exactly what to do. Open one and read it — this is the real format.

hyvo-audit-report.pdf — 7 findings

Production-readiness audit

Example SaaS — Next.js + Supabase, ~14k lines

Reviewed: Application code, database schema and policies, storage rules, auth flow, deploy configuration. Not in scope: Third-party SaaS you do not control, native mobile builds, penetration testing.

Critical

High

Medium

Low

What we found

The tables have no row-level security policies. The app filters by user ID in the front-end query, so the interface only ever shows you your own data — but the API accepts any filter it is given. Changing one value in a request returns somebody else's rows.

Why it matters

This is the flaw that exposed roughly one in ten apps on Lovable's own showcase. It is invisible in testing, because one user reading their own data and one user reading everyone's look identical from inside the app. It is found in seconds by anyone who opens the network tab.

How to fix it

Enable RLS on all three tables and add owner-match policies, so the database itself refuses the request rather than trusting the client to ask nicely. Add a test that asserts user A cannot read user B's rows.

Effort: Half a day

Composite example built from the issues that recur most often in AI-built applications. No client's findings are reproduced here.

How it works

You send a link

A URL, or repo access. No questionnaire, no discovery call, no NDA dance unless you want one.

A senior engineer reads it

Not a scanner. Someone who has shipped this stack reads the code, the schema, the access rules and the deploy configuration.

You get the report

Findings ordered by risk against effort, each with what is wrong, what happens if it stays, and how to fix it. Five business days.

You decide

Fix it yourself with the report, hand it to your developer, or ask us to. Most people do the first. That is genuinely fine.

Scope: Application code, database schema and policies, storage rules, auth flow, deploy configuration. Not included: Third-party SaaS you do not control, native mobile builds, penetration testing.

From clients who took the audit

They performed a free technical audit, which identified a number of opportunities for us to improve our platform before we launched, then they took our prototype and made it into a production-ready application, refining our architecture, our deployment processes and getting our platform ready for real-world use, giving us the confidence to proceed with launch with a scalable and reliable product.

Team Zippy — Zippy

Hyvo played a key role in taking our product from development to launch. They performed a full technical audit for us for free, advised us on how to improve our cloud setup, and gave us some hands-on tips on scalability and deployment. They also helped us throughout the Play Store publishing process, and our application was published without any hassle. They were very knowledgeable and helpful, and made the whole process a lot easier.

Prasad Magdum — Founder, Bookr

Questions people ask first

What does the audit cost?

Nothing. We're a young studio and we need work we can point at, so the audit is free while we have capacity. There is no upsell attached to receiving the report.

Do I have to get on a call?

No. The report arrives by email and stands on its own. If you want to talk it through we will, but it is not a condition.

Will you look at AI-generated code?

That is most of what we see. Code written by Lovable, v0, Bolt, Cursor or Claude has a recognisable set of gaps, and knowing where to look makes the review faster rather than harder.

Is my code safe with you?

We only need read access, we ask before touching anything, and we will sign an NDA if you send one. Nothing from your codebase appears in our examples — the sample report on this page is a composite.

What if you find nothing serious?

Then the report says so, and you get to launch with more confidence than you had. That outcome happens and we would rather tell you than manufacture a problem.

How is this different from a security scanner?

A scanner finds known patterns in dependencies. It cannot tell you that your authorization logic trusts the client, or that your dashboard query will collapse at fifty times the data. Those need someone to read the code.

Send us a link.

That is the whole ask. Five days later you will know exactly where you stand.

Request the free audit

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