Your app shipped in a weekend. Did anyone check it for holes?
AI coding tools let founders and small teams ship production apps in days instead of months. They also ship the same handful of dangerous mistakes, over and over, at the same speed. We find them before an attacker does — with senior security engineers, not another scanner.
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45%
of AI-generated code samples introduced an OWASP Top 10 vulnerability — Veracode
72%
failure rate in Java specifically, the worst language tested — Veracode
+172%
year-over-year rise in Broken Access Control, now the top bug class in public repos — GitHub Octoverse 2025
50%+
of organisations have already hit a security issue from AI-generated code — Snyk
Vibe coding shipped the feature. It didn't ship the security review.
"Vibe coding" — describing what you want in plain English to an AI pair programmer and shipping whatever comes back — has collapsed the time between idea and production app. What it hasn't done is add a security review step, because for most solo founders and small teams there was never a formal one to begin with.
That gap shows up in the data. Veracode found that 45% of AI-generated code samples introduced at least one OWASP Top 10 vulnerability, a figure that climbed to 72% for Java. A peer-reviewed Stanford study presented at ACM CCS'23 went further: developers using AI coding assistants wrote measurably less secure code, while being more confident it was safe — the exact combination that lets a bug ship unquestioned.
Meanwhile the shape of the risk is shifting. GitHub's Octoverse 2025 report found Broken Access Control has overtaken Injection as the single most common vulnerability class in public repositories, up 172% year-over-year — precisely the bug that lets one user quietly read or edit another user's data. Snyk puts a number on the outcome: over half of organisations have already been hit by a security issue traced to AI-generated code — and plenty of that code still ships without a dedicated security review.
None of this means AI coding tools are unsafe to use. It means the code they produce needs the same scrutiny any production system needs — from someone who already knows what a coding assistant tends to get wrong.
Five places vibe-coded apps break, every time
We've reviewed enough AI-generated codebases to know where the bodies are buried. Every audit covers these five categories in depth, plus anything specific to your stack.
Missing row-level security
Supabase and Firebase make it easy to query a database straight from the browser — and just as easy to forget the policy that stops User A from reading User B's data. We check every table against every role, not just the ones your prompts happened to mention.
Hardcoded API keys & secrets
Service-role keys and third-party tokens pasted straight into client-side code, or committed to a public repo. An AI assistant will wire up a working integration with whatever key is easiest to reach, including the one that should never leave your server.
Broken access control
Now the single most common vulnerability class in public code (GitHub, 2025): checking that a user is logged in, but not whether they're allowed to touch this record — so any authenticated user can edit or view data that isn't theirs.
Injection risks
Unescaped input reaching a database query, shell command, or template — the oldest bug in the book, still routinely reintroduced by code generated a few lines at a time with no consistent pattern across the codebase.
Runaway-cost exposure
An unpaginated query, a missing rate limit, or a loop calling a paid API once per row instead of once per batch. Not a breach — but the kind of bug that turns a normal Tuesday into a four-figure invoice.
Plus your stack
Auth flows, webhook checks, and file-upload handling — scoped to your framework once we've seen the codebase.
Why a person, not another AI
There's no shortage of automated tools now that scan a repository and flag common AI-coding mistakes. They're a reasonable first pass, and if you're already running one, keep running it. But a scanner matches patterns — it doesn't understand your product.
It won't notice that your row-level security policy is syntactically fine but logically wrong for how your app assigns roles. It won't trace a multi-step authorisation bypass that only appears when two features interact, or flag that the "temporary" admin endpoint left in three commits ago is still reachable in production.
Finding that takes a person who has spent a career locating exactly these kinds of holes — reading the code the way an attacker would, not the way a linter would.
Our team brings three decades of security engineering experience to this work, now applied to the patterns that show up in AI-assisted code. AI introduces the risk at a speed no human team could match. A human being is still what it takes to find it first.
Built it fast with an AI coding tool? This is for you.
This audit is built for exactly the kind of app that's becoming normal in 2026: something built solo or by a tiny team, largely by describing what was wanted to Cursor, Claude Code, Replit, Lovable, Bolt, v0, or a similar tool, and shipping whatever came back.
It's especially built for non-technical founders — people who can now build a working product without writing most of the code themselves, but who have no real way to judge whether that code is safe. If you can't answer "does every table have row-level security?" that's the exact gap this audit closes.
It's just as useful for technical teams who moved fast on purpose and want a second, adversarial set of eyes before a launch, a funding round, or a compliance conversation.
How the audit works
We don't publish a price list, because a five-page app and a fifty-table SaaS product cost different amounts of engineering time to review properly. What we do publish is exactly how the engagement runs.
Audit
We get read access to your codebase, and where relevant your Supabase or Firebase project, and go through it the way an attacker would: authentication, authorisation, data rules, secrets, and cost exposure.
Prioritised findings report
A plain-English report ranking every issue by real-world severity and exploitability, not forty pages of everything a scanner could theoretically flag. Each finding explains what's wrong and exactly what to change.
Fix support
Most founders want the holes closed, not just described. We can implement the fixes directly, or work alongside your developer or AI tool of choice to get each one resolved, then re-verify it before we call it closed.
We scope every engagement on a short call first, so you get a firm timeline before anything starts — typically days from kickoff to your findings report, not weeks.
Frequently asked questions
What exactly is a vibe code security audit?
It's a human-led security review built specifically for apps that were largely generated by an AI coding assistant, such as Cursor, Claude Code, Replit, Lovable, Bolt, or v0. We check for the mistakes these tools make repeatedly: missing Supabase or Firebase row-level security, hardcoded secrets, broken access control, injection risks, and runaway-cost bugs, then hand you a prioritised, plain-English report.
Do I need to give you my AI prompts or chat history?
No. We audit the code that exists, not the prompts that produced it. Read access to your repository, and your database project where relevant, is all we need to start.
How is this different from an automated AI code scanner?
A scanner pattern-matches against known bad code shapes. It doesn't understand what your app is supposed to do, so it misses logic-level authorisation bugs, row-level security policies that are syntactically valid but wrong for your actual roles, and multi-step exploits that only appear when two features interact. That's the layer our engineers cover.
I'm not technical. Will I understand the report?
Yes. This service was built for non-technical founders as much as engineering teams. Every finding is written in plain English covering what's wrong, what it would let someone do, and what needs to change, with fix support available if you'd rather we, or your developer, close the gaps directly.
How long does an audit take?
It depends on the size and complexity of the codebase, which we scope on a short call before quoting a timeline. Most engagements move from kickoff to a delivered findings report in a matter of days, not weeks.
Need the fuller three-phase treatment — secure code review, penetration test, and retest, assessed against OWASP ASVS? That is our AppSec Assessments engagement. Running a security audit alongside other work? See our SEO services and technical SEO audits, browse our case studies, or read more about the team behind all of it.
Find out what's actually in your codebase.
One audit, one prioritised report, no guessing.