Vibe Coding Security: What to Audit Before Deploying an AI-Built Application

The scale of the problem, according to the first real research on it

Until recently, "vibe-coded apps are probably less secure" was mostly an assumption. That changed with a study published in June 2026 that audited real-world vibe-coded applications at scale, collecting over 9,000 open-source applications built with Claude Code and Lovable and directly auditing 200 publicly deployed applications built the same way. The findings were stark: insecurity turned out to be the norm rather than the exception, with 91 percent of audited applications containing at least one vulnerability, and nearly two-thirds of all identified vulnerabilities rated Critical or High severity, concentrated specifically in broken access control, injection flaws, and authentication failures .

The same research traced these failures back to three systematic limitations baked into how AI coding agents actually work, not random bad luck. Memory defects, where an agent loses track of security decisions or constraints established earlier in a long build session and quietly reintroduces a problem it had already addressed. Objective defects, where the agent is optimizing for "does this feature work" rather than "is this feature secure," so it satisfies the literal request while treating security as an unstated requirement it was never actually asked to solve. And knowledge defects, where the agent reproduces the patterns most common in its training data, patterns that are frequently insecure by default because that's what most publicly available code actually looks like . Critically, the same study found that better agent harnesses and more careful prompting reduce how often these problems occur, but don't eliminate the underlying risk . That last finding is exactly why an independent, verified vulnerability assessment matters regardless of which tool or which version of that tool built your application. A better prompt reduces risk. It doesn't remove the need to check.

This isn't a hypothetical concern for platforms with small user bases either. Lovable alone reports more than eight million users and over 100,000 new applications created daily, with more than 10 percent of those deployed as live, publicly accessible systems . A separate, earlier disclosure found that roughly one in ten Lovable-created applications had a specific issue that let anyone access personal information stored in the app , which lines up closely with what the June 2026 study found at a broader scale.

Why this differs from a normal software security problem

If you're using Cursor or Claude Code inside an existing codebase, you're still the one reviewing and merging what gets generated, at least in theory. Research specifically comparing security-aware prompting against default prompting on identical applications found that being explicit about security requirements meaningfully reduces the number of confirmed findings and eliminates the most severe issues entirely , which tells you something important: the agent is capable of writing more secure code when asked, it just doesn't default to it.

That review step is exactly what tends to erode under deadline pressure, and it's measurable. An analysis of hundreds of real pull requests found that AI-co-authored code contained roughly 1.7 times more major issues than human-written code, with misconfigurations appearing 75 percent more often and security vulnerabilities appearing at nearly three times the rate . On platforms like Lovable and Replit, where the tool generates and deploys a complete, working application from a natural-language prompt with far less opportunity for line-by-line review, that gap widens further, which is exactly the profile the June 2026 study focused its deployed-application audit on. Whichever of the four tools you're building with, the audit scope Ceron runs against your application doesn't change: authentication, database permissions, exposed secrets, deployment configuration, and adversarial testing boundaries, tested the same way regardless of what generated the code underneath.

What to actually audit before deploying

Generated authentication. This is the single most common failure category in the research, and it shows up in predictable ways. Look specifically for hardcoded test credentials or authentication bypass logic left over from early prototyping, placeholder logic is one of the specific recurring patterns researchers identified, code stubbed out during development and never actually replaced before deployment . Check whether password reset flows genuinely verify identity or just accept a token without proper validation, whether sessions actually expire and get invalidated on logout, and whether every protected route actually enforces the authentication it's supposed to rather than relying on the frontend to simply hide a button. This is the first thing tested in a Ceron core audit, precisely because it's the category most likely to produce a Critical finding.

Database permissions. This is where the most damaging vibe-coding vulnerabilities concentrate, and it's specifically a broken access control problem, the exact category the June 2026 study found dominating its findings. Modern backend-as-a-service platforms commonly used by these tools, Supabase and Firebase among the most popular, ship with database access rules that default to wide open unless explicitly locked down, and an AI agent focused on making a feature functional has no inherent reason to configure row-level security correctly unless specifically instructed to. Check whether database rules actually restrict each user to their own data, whether a service-role or admin key, which bypasses those rules entirely, has been exposed anywhere in client-side code, and whether an authenticated user can access another user's records simply by changing an ID in a request. Ceron's assessment methodology treats this as a standard check on any application connected to a cloud account, not an optional add-on.

Exposed secrets. API keys, database credentials, and third-party service tokens hardcoded directly into client-side code or committed into a connected repository are a recurring finding across AI-assisted development broadly, and the trend is accelerating specifically around AI-related services: one industry report tracking secret exposure found AI-service credential leaks surged 81 percent year over year, with 29 million secrets found exposed on public GitHub . An agent building fast, working code has no built-in incentive to route credentials through proper environment variable handling unless the founder explicitly directs it to. A Ceron audit checks both the deployed application and any connected repository access within scope for exactly this pattern.

Deployment configurations. Defaults left unchanged are a recurring theme across every category here, and deployment settings are no exception. Check whether CORS is scoped to your actual domain rather than left wide open, whether debug mode and verbose error messages, which can leak stack traces, file paths, and internal architecture details, are disabled in production, and whether staging and production environments are actually separated rather than sharing the same database and credentials because that was the faster way to get the demo working.

Testing boundaries. This is the category founders most consistently miss, because it requires understanding what the AI agent's own testing actually covers, and what it doesn't. An agent's iterative testing loop is almost always functional: does the feature work when used the way it's supposed to be used. It's essentially never adversarial: what happens when someone deliberately tries to break it, bypass it, or extract something it wasn't meant to expose. Those are fundamentally different testing objectives, and no amount of "make sure it works" prompting substitutes for someone actually trying to break it on purpose. If you're using agentic tools like Cursor or Claude Code that read external context, READMEs, issue trackers, linked documentation, be aware this also introduces a separate risk: research on indirect prompt injection against these exact tools found attack success rates exceeding 85 percent under adaptive attack conditions, with most current defenses blocking less than half of attempts , meaning the agent itself can be manipulated through content it reads, not just through what you directly prompt it to do. This adversarial layer is exactly what a frontier-AI-driven assessment from Ceron is built to run, reasoning through exploit paths the same way an attacker would, rather than checking for functionality the way the original build agent did.

A representative test: what an audit actually finds

To make this concrete, here's a synthetic example modeled directly on the failure patterns above, representative of what a Ceron audit typically finds in this class of application, not a claim about any specific named product.

The application: a simple subscription-based invoicing tool, built over a weekend on a popular AI app-building platform, with user accounts, saved client records, and Stripe-based billing. Functionally, it works exactly as intended.

A verified audit of an application matching this profile typically surfaces findings like these. Critical: an insecure direct object reference on the invoice endpoint allows any logged-in user to view and download another customer's invoices, including client names and billing details, simply by changing a numeric ID in the URL, a direct instance of the broken access control pattern that dominates the research findings. High: the Stripe secret key is present in a client-side JavaScript bundle rather than restricted to server-side use, exposing it to anyone who opens browser developer tools. High: the database's row-level security policy, generated during initial setup, was never actually enabled on the clients table, meaning the database-level protection the developer assumed was in place was silently inactive the entire time. Medium: password reset tokens don't expire, remaining valid indefinitely once issued. Low: verbose error messages on failed API calls return internal file paths and framework version information.

None of these findings required exotic techniques to surface. They required someone to actually look, with the specific knowledge of where vibe-coded applications tend to fail, exactly the evidence-backed, severity-ranked format a Ceron report delivers rather than trusting that a functional demo meant a secure one.

Why this specifically matters if you don't have a security team

If you're a founder shipping an AI-built application without in-house security expertise, and increasingly, that's simply what building a startup looks like in 2026, you don't have the internal review layer that used to catch at least some of this before launch. That's not a criticism of the tools or of building this way. It's a description of exactly where the gap sits, and why closing it requires an outside, independent check rather than assuming the platform or the agent handled it.

A vulnerability assessment scoped specifically to an AI-built application covers precisely the categories above: authenticated testing of access controls and database permissions, verification that secrets aren't exposed anywhere in the deployed application, review of deployment configuration against production security standards, and adversarial testing of exactly the boundaries the agent's own functional testing never touched. Ceron's core audit is built to run against this kind of application fast, a fixed $1,500 engagement covering your web app or API and connected cloud account, starting the same day scope is agreed, which matters when the whole point of building this way was speed in the first place. And because it runs on a no findings, no fee model, there's no downside to checking before you ship real user data through something that was built in a weekend.

For applications scaling past an early prototype, handling payment data, or preparing for enterprise customers who'll ask for evidence of testing before they sign, Ceron's extended audit adds deeper, authenticated penetration testing across identity and access controls, quoted around the specific scope of the application rather than sold as a flat product. And because vibe-coded applications tend to keep shipping fast well past launch, new features prompted into existence just as quickly as the original build, a single pre-launch check has a short shelf life. Rolling into a recurring quarterly Ceron assessment matches the actual pace these applications keep changing at, catching what the next round of fast iteration introduces instead of assuming the first clean report still holds six sprints later.

Before you deploy: the practical checklist

Confirm every protected route actually enforces authentication server-side, not just in the frontend UI. Locate and remove any hardcoded test credentials or bypass logic left over from prototyping. Verify row-level security or equivalent database access rules are actually enabled and correctly scoped, not just configured and forgotten. Search your entire codebase and deployed bundle for hardcoded API keys, and confirm sensitive service-role keys never reach client-side code. Disable debug mode and verbose error output in production. Confirm CORS is scoped to your actual domain. And before real users and real payment data start flowing through what you built, scope an independent Ceron assessment that specifically tests the boundaries your AI agent's own testing never went near.

Vibe coding didn't create insecure software. It just removed the step where someone used to catch it before it shipped. Ceron is that step, put back in, deliberately, before launch, so the gap between moving fast and moving fast into a problem doesn't get closed by a customer, or an attacker, finding it for you first.