'Vibe coding' let anyone build an app. Now the security bill is coming due

One of the defining shifts in software in 2026 is that you no longer need to know how to code to build and ship an app. You describe what you want to an AI, it writes the code, and a few clicks later it is live on the internet. The practice picked up a fittingly casual name, "vibe coding," and it has genuinely opened software creation to people who could never have done it before. It has also quietly created a security problem at scale, and in 2026 that bill started arriving. Here is the honest accounting.

The problem: AI writes code that works, not code that is safe

The core issue is a mismatch between what AI is good at and what security requires. An AI coding tool is superb at producing an app that runs and looks finished: the buttons work, the data saves, the demo is impressive. What it does not reliably do is build in the invisible protections that separate a working app from a safe one, checking that a logged-in user can only see their own data, validating input, keeping secret keys off the public-facing side of the app.

Those protections are invisible precisely because, when they are missing, the app still works perfectly in a demo. The flaw only shows when someone goes looking. And the person who vibe-coded the app, by definition, often lacks the security knowledge to know what to look for. The result is software that passes the "does it work?" test and fails the "is it safe?" test, shipped by someone who cannot tell the two apart.

The receipts

This is not hypothetical hand-wringing; it is showing up in scans and incidents. Security researchers who scanned thousands of deployed AI-built apps in 2026 reported that a large fraction expose sensitive data in some way, on the order of 40% in one analysis of 5,000-plus apps by the security firm Red Access. A separate scan of roughly a thousand apps reportedly found hundreds leaking database keys directly in the code that runs in users' browsers, and many that let anyone delete records without so much as logging in.

Named cases make it concrete. The AI app-building platform Lovable was reported to have exposed large numbers of user projects for weeks through a broken-access-control flaw, the kind where the app simply fails to check whether you are allowed to see a given piece of data. The viral Tea app reportedly leaked private user information through similar access-control failures. In each case the common thread is the same: an app that worked flawlessly for its users while quietly leaving the front door unlocked. (These figures and incident details come from security-vendor research and reporting rather than a single peer-reviewed study, so treat the specifics as reported rather than independently confirmed, the broad pattern, though, is consistent across multiple reports.)

Why AI-generated code fails this way

It is worth being precise about the cause, because "AI is bad at security" is too crude. AI models are trained to produce code that satisfies the request, and a request like "build me an app where users can post reviews" contains no mention of authorisation, rate-limiting or secret management, so the model, reasonably, focuses on the reviews. Security is the stuff you have to know to ask for, and the entire appeal of vibe coding is that you do not have to know anything. The very thing that makes it accessible, you can build without understanding what is under the hood, is the thing that removes the person who would have caught the missing lock.

Experienced developers using the same AI tools tend to fare better, not because their AI is different, but because they know to prompt for the protections, and to review what comes out. The danger zone is the gap between "AI can write the code" and "someone understands whether the code is safe," a gap vibe coding is designed to let you skip.

What it does, and does not, mean

The honest reading is not "AI coding is a mistake." Letting far more people turn ideas into working software is a real and mostly good expansion of who gets to build. The lesson is narrower and more useful: AI-generated code is a first draft, not a finished product, and the review step, checking access controls, secrets and input handling, is not optional just because the app appears to work. For anyone shipping a vibe-coded app that touches real user data, that means either learning the basics of what to check, using tools that scan for these flaws, or having someone who knows review it before it goes live. The tools that will win are the ones that build those guardrails in by default, so that "it works" and "it is safe" stop coming apart. Until then, the security bill for a wave of unreviewed AI apps is real, and it is being paid by their users. For more, see the Software section and our look at how the software supply chain got safer.

The vibe-coding security problem, at a glance

What "vibe coding" is	Describing an app to an AI and shipping the code it generates, no coding needed
The problem	AI produces apps that work but often lack basic security (access control, secret handling)
What scans found	A large share of AI-built apps expose sensitive data (~40% in one 5,000+ app analysis)
Named cases	Platforms and apps (e.g. Lovable, Tea) leaked data via broken access controls
Why	AI optimises for "works," not "secure"; the person shipping often can't tell the difference
The fix	Treat AI code as a first draft; review access control, secrets and input before shipping