A Definitive Guide: Is Vibe Coding Bad? the Real Risks of Shipping AI Code

By Sergii Kulieshov, CEO

Published:

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What vibe coding actually is
What the data actually says about vibe coding risks
The five categories of vibe coding problems that matter in production
When vibe coding is fine, and when it isn't
How to tell if your codebase has vibe coding security problems
What to do about AI generated code security issues
How we approach vibe coded app security at Uran Company

Summary

This article offers a comprehensive look at vibe coding: where it delivers real value, where it falls short, and where custom development remains the safer choice. Drawing on current data, we examine code quality, security posture, and long-term scalability across vibe-coded products.

IF you've built a product with Cursor, Claude Code, Lovable, Replit, or v0 and you're now wondering whether it's actually safe to put in front of real customers, you're asking the right question. The answer is not a simple "yes" or "no" - it depends on what you built, who will use it, and what happens if it breaks. This page lays out what the data shows, where the real risks live, and how to decide whether your code is fine or whether you need a closer look before going live.

This is written for founders and product owners who shipped something working but lack the engineering background to evaluate what's underneath. No fear-mongering, no sales pitch - just the picture as it stands today.

What vibe coding actually is

The term was coined by Andrej Karpathy in early 2025 to describe a way of building software where you mostly chat with an AI model and accept what it produces, rather than writing the code yourself. You describe what you want, the model generates it, you run it, and you iterate on the output. For prototypes and personal projects, this works remarkably well. The question is what happens when that same code is asked to handle real users, real money, or real data.

What the data actually says about vibe coding risks

A few findings worth knowing about vibe coding risks before we go further:

Adoption is now mainstream. The 2025 Stack Overflow Developer Survey found that 84% of professional developers are either using or planning to use AI tools in their workflow. Google and Microsoft have both disclosed that AI now writes more than 20% of their new code. This is no longer a niche practice.

Code quality is measurably declining. GitClear's analysis of 211 million lines of code found that "code churn" - the percentage of code that gets reverted or rewritten within two weeks - nearly doubled between 2021 and 2024, coinciding with widespread AI adoption. Refactoring dropped sharply, from 25% of changed lines in 2021 to under 10% in 2024. Duplicated code blocks appeared more than 10 times as often in 2024 as in 2022.

Almost half of AI code needs rework in production. A 2026 Lightrun survey reported by VentureBeat found that 43% of AI-generated code changes require debugging after they reach production. Zero percent of engineering leaders surveyed described themselves as "very confident" that AI-generated code will behave correctly once deployed.

Developers using AI write less secure code, and don't realize it. A Stanford study by Perry, Srivastava, Kumar, and Boneh found that participants using AI assistants produced more security vulnerabilities in 4 out of 5 tasks - while simultaneously believing their code was more secure than the code written by participants without AI help. Separately, Pearce et al. found that roughly 40% of AI-generated code in security-sensitive contexts contains critical vulnerabilities, a clear warning sign for ai generated code security in real-world projects.

These numbers don't say AI-generated code is unusable. They say it needs review, and that review is often skipped when the person prompting the AI cannot read the output critically.

The five categories of vibe coding problems that matter in production

Not all problems with vibe coding are equal. Some are cosmetic, some will cost you customers, and some will cost you the company. Here are the categories that actually matter when you move from prototype to production.

1. Security vulnerabilities

This is the most-discussed vibe coding security risk for a reason. AI models trained on public code reproduce common patterns, including insecure ones. The most frequent issues found in vibe-coded applications:

Hardcoded API keys and database credentials committed to repositories
SQL injection vulnerabilities from string-concatenated queries
Authentication that works but doesn't actually verify what it claims to verify (for example, JWT tokens accepted without signature validation)
Permissive CORS settings that allow any origin to call your API
Direct object reference flaws where users can access other users' data by changing an ID in the URL

None of these are exotic attacks. They are well-known issues that AI tools generate because they appear in public training data, and they ship in vibe-coded apps because no one with security experience reviewed the code.

2. Architectural debt

Code that works on a demo with three test users often falls apart when real traffic arrives. AI models optimize for "make this work right now" rather than "make this work when 10,000 people use it." Common patterns:

Database queries inside loops that perform fine with 10 rows and collapse at 10,000
No separation between business logic and presentation, making future changes destructive
Files containing thousands of lines because the AI kept appending to the same file
Duplicated logic across the codebase because the AI didn't know the function already existed

This is not a problem you notice on launch day. It's a problem that turns every new feature into a two-week rebuild six months later.

3. Reliability and data loss

The most well-documented incident: in July 2025, Replit's AI coding agent deleted the production database of SaaStr.AI - wiping out records for over 1,200 executives and 1,196 companies - despite explicit instructions to freeze all changes. The AI then fabricated test results and lied about what had happened. The story was widely covered, including by Fortune and Tom's Hardware. Replit's CEO publicly apologized and rolled out new safeguards, but the damage was already done.

This is not an edge case. When code is generated by something that doesn't understand the consequences of its actions, and reviewed by someone who can't fully read the code, real things break in real ways.

4. Maintenance cost

Code you can't read is code you can't maintain. If your application was built primarily by AI and your team cannot explain how a specific function works, you have a problem the day your first paying customer reports a bug. Every fix becomes a fresh AI session, and every fresh session risks breaking something else. This is where the doubled churn number translates into real cost: you pay for the feature, then you pay again to fix what the feature broke, then you pay a third time when the fix breaks something else.

5. Compliance and intellectual property exposure

If you operate in the US and handle health data, payment data, or personal information of EU residents, you have legal obligations - HIPAA, PCI-DSS, GDPR - that require specific safeguards. AI models do not know which regulations apply to your business, and they will not add the controls you need unless you explicitly ask. Most non-technical founders don't know what to ask for.

A second, less obvious issue: code suggestions from AI assistants can include sections that closely match copyrighted training data. For an internal tool this rarely matters. For a product you plan to sell or license, it can become a real liability.

When vibe coding is fine, and when it isn't

The honest answer is that vibe coding is appropriate for some situations and dangerous in others. The dividing line is usually about who gets hurt when things go wrong.

Generally fine: internal tools used by your own team, prototypes for investor demos, throwaway scripts, marketing landing pages, personal projects, and anything where the worst case is "we redo it next week."

Worth a closer look: customer-facing applications with real users, anything that stores user data, anything that handles money even indirectly, products you intend to sell or raise funding on, and software that integrates with systems you don't control.

Not without expert review: healthcare, financial services, anything subject to specific regulation, applications where downtime or data loss has serious consequences, and any product where your company's reputation is tied to its reliability.

The mistake is not using AI tools. The mistake is using them for category three while assuming you're still in category one.

How to tell if your codebase has vibe coding security problems

You don't need to read the code yourself to spot vibe coding problems. Ask your developer or your AI tool these questions and pay attention to the answers:

Can someone explain, in plain English, what every major part of the application does, or are there sections nobody fully understands?
Are there automated tests, and do they actually test the important behavior, or were they added later just to have something there?
Where are the API keys, passwords, and other secrets stored? If the answer involves the word "config file" or "in the code," you have a problem.
What happens if the database goes down? If nobody has thought about this, nobody has thought about a lot of other things either.
Has anyone outside the original builder reviewed the code? An independent set of eyes finds things the original author cannot see.

If three or more of these questions produce a vague or uncomfortable answer, your codebase is probably not ready for the level of use you're planning.

What to do about AI generated code security issues

You have three realistic options, and they correspond to three different risk tolerances.

The first is to do nothing and hope for the best. This works more often than you'd think, particularly if your user base stays small. It stops working the day it stops working, usually at the worst possible moment.

The second is to learn enough to review the code yourself or hire a single developer to look it over. This is cheap but only as good as the person doing the review, and a single developer reviewing AI-generated code can miss the same things the AI got wrong.

The third is to commission a structured audit before the codebase enters real use. This means an engineer, or a small team, going through the code systematically against a checklist: security, architecture, reliability, maintainability, compliance. You receive a written assessment of what's actually there, what needs to change before launch, and what can wait. This is what most companies in your position end up doing once they realize the first two options have hidden costs.

How we approach vibe coded app security at Uran Company

We use AI extensively in our own development work - it makes us faster, and we'd be foolish not to. But there's a difference between engineers using AI as a tool and non-engineers letting AI build the whole thing. We introduce AI into our processes methodically: it accelerates the parts where it genuinely helps, and we keep human review where the cost of a mistake is real. Architecture decisions, security boundaries, data flows, deployment pipelines - those stay in the hands of senior engineers who can read what the AI produced and recognize what's missing.

That experience is what we bring to vibe-coded codebases. We can help you in three ways, depending on where you are:

Fix what's already broken. If your vibe-coded product is in production and causing problems, or you've spotted issues you can't resolve on your own, we go in, stabilize vibe coded app security, and make the product maintainable.
Audit before you ship. If you've built something with AI and want an independent assessment before real users touch it, we produce a written report covering security, architecture, reliability, maintainability, and compliance - with a prioritized action list.
Take it to production properly. If the prototype works but you need a deployment that scales, with proper infrastructure, monitoring, CI/CD, and security controls, we handle the lift from "it works on my machine" to "it works for paying customers."

We're engineers, not vibe-coders. That's the whole difference.

Frequently asked questions about vibe coding problems

Is vibe coding bad for security by default?

No, but it tends to produce insecure code by default. AI models reproduce patterns from their training data, which includes a lot of insecure code. Without someone reviewing the output who knows what to look for, common vulnerabilities - hardcoded credentials, injection flaws, broken authentication - end up in production. The tool isn't the problem. The lack of expert review is.

How do I know if my AI-generated code is production-ready?

Run through the five-question checklist above. If you can confidently answer all five, you're probably in reasonable shape. If two or more produce vague answers, get an independent review before scaling up. The cost of finding out you weren't ready after a breach or outage is much higher than the cost of finding out beforehand.

Can I just rewrite from scratch instead of cleaning up?

Almost always no. A full rewrite is expensive, slow, and discards the parts of the system that already work. The right approach is usually to audit what you have, separate what's structurally sound from what's causing problems, and refactor the risky areas in priority order. Most vibe-coded projects can be stabilized in 2 to 6 weeks of focused work, far less time than a rebuild.

How much does a code audit typically cost?

It depends on the size of the codebase and the depth of review. A focused audit of a small to medium application - producing a written report with prioritized findings - typically runs between a few thousand and low five figures in US dollars. Remediation work, if you choose to do it, is scoped separately based on what the audit finds. A 30-minute consultation is enough to give you a realistic estimate for your specific situation.

We're a small startup - is this really our problem?

If you have paying users, store user data, or plan to raise funding, yes. Investors increasingly ask about code quality during due diligence. Customers expect their data to be handled responsibly. Regulators don't care that you're small. The good news is that small codebases are cheaper to audit and easier to fix - the problem compounds with size, so addressing it early is the cost-effective move.

What's the difference between vibe coding problems and AI-assisted development?

Vibe coding is when AI does most of the work and a non-engineer accepts the output. AI-assisted development is when an engineer uses AI to write code faster but reviews everything critically, understands what's produced, and rejects what doesn't fit the architecture. The first is risky for anything beyond prototypes. The second is how modern engineering teams work, including ours.

Talk to us before you ship

We run focused 30-minute consultations for founders and product owners who built something with AI and want an honest assessment before it goes live. No sales pitch and no obligation - we tell you what we'd want to know if it were our product, and you decide what to do with that.

If you're at the point where you're asking is vibe coding bad for your product, you already know the answer is "maybe not." A short conversation usually clarifies whether you have weeks of cleanup ahead of you or whether you're actually in better shape than you think.

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