Is Vibe Coding Bad? Check Your MVP Before Release

Is vibe coding bad? Not automatically. AI-generated code can be useful, but a working demo does not establish that an application is ready for real users. The release decision should depend on who can access it, what data and money it handles, how failures affect customers, and whether someone can maintain and recover it.

I would judge an AI-built MVP by evidence, not its origin. The goal is to decide what you can keep, what needs repair, and whether any component genuinely needs replacement before the next release.

Vibe coding is not the same as supervised AI-assisted development

People use “vibe coding” loosely. Here, it means prompting an AI tool and accepting generated changes with limited understanding or review of the implementation.

Supervised AI-assisted development has a different accountability model: a developer understands the changes, reviews their consequences, tests important behavior, and owns ongoing maintenance. OWASP’s guidance on secure coding with AI recommends human ownership of generated changes and does not treat AI review as a substitute for human review.

That distinction matters more than how much code the AI wrote. A demo may show that a customer can create an account and buy a subscription. It does not establish that another customer cannot read their records, that a failed payment leaves the correct access level, or that a deployment will preserve saved data.

Vibe coding security concerns become useful release questions when you turn them into specific checks.

Define what the next release will expose

Start with the intended release, not the label “MVP.” Write down its users, data, permissions, payment behavior, and consequences of failure.

Scope can reduce risk, but only when the restriction is real. Calling a publicly accessible application a “private beta” does not restrict access.

Where indispensable evidence is missing, keep the application in a restricted environment with synthetic data. You can continue testing the product without exposing customer information or live payments.

Run founder-accessible checks without treating them as certification

You do not need to read the entire codebase to collect useful behavioral evidence. Use an authorized test environment and accounts you control. Use synthetic records rather than real customer data.

Record the expected result and actual result for each check:

• Account lifecycle: Sign up, sign out, sign back in, and complete the password-reset flow if applicable. Check whether protected areas remain accessible after logout.
• Persistence: Create and edit a record. Refresh, sign out, and return. Confirm that the intended changes remain saved.
• Account boundaries: Create two accounts intended to be isolated. Add a private record under account A. Copy its URL and try opening it as account B, then while signed out. Neither should expose that private record.
• Payments: In the payment provider’s test mode, exercise applicable purchase, failed-payment, and cancellation paths. Check the resulting subscription state and feature access against your intended rules.

The private-record check illustrates an important principle: permission to view one record does not imply permission to view every record of that type. OWASP’s authorization guidance explains this distinction.

A failed check gives you a concrete defect to investigate. A passed check establishes only that the tested behavior worked under those conditions. It is not a security certification or a substitute for implementation review.

Ask a developer to review what the interface cannot prove

Authentication establishes identity: “Who are you?” Authorization establishes permission: “May you view or change this particular record?” A working login screen proves neither that customer data is isolated nor that administrative actions are protected.

Ask a developer to explain the critical protections and point to supporting code, configuration, and tests:

• Authorization and tenant boundaries: Where does the system enforce access to each customer’s records and actions? OWASP recommends denying access by default and checking permissions on every request at a trusted enforcement point. Hiding a button in the browser is insufficient.
• Sessions and input validation: How are sessions ended or invalidated? Where are incoming values checked before the application acts on them?
• Secrets and dependencies: Where are privileged credentials stored, who can access them, and which dependencies need attention? Distinguish actual secrets from intentionally public identifiers. OWASP recommends keeping secrets out of AI context and restricting agent and deployment credentials.
• Data flows: Where does customer information go, including external services and logs? Which components can read or change it?
• Billing and entitlements: What trusted mechanism changes paid access? Where applicable, how are payment events authenticated, and how does processing handle duplicates or retries without applying an action twice?
• Failure and deployment paths: What happens when a service times out, a write fails, or a deployment changes the database?

Request evidence rather than “the AI checked it.” Scans and passing tests are useful inputs, not release approval. OWASP specifically cautions that passing tests alone does not establish security, especially when an agent generated both the implementation and its tests. Its guidance recommends human-written security-critical and adversarial tests.

Prove someone can operate and recover the application

An application is difficult to own if the business cannot access the repository, hosting, database, domain, or payment accounts. Document business-controlled access and name the people responsible for deployments, incidents, and maintenance.

Before exposing important data or transactions, request:

• A reproducible deployment: Document how an authorized person can deploy the intended version without relying on an unexplained local setup.
• Environment separation: Keep testing distinct from production, with appropriately restricted credentials and access.
• Useful alerts: Identify which failures need attention, who receives the alert, and what they should do next.
• A rollback plan: Explain how to return to a working application version. Code rollback may not undo database migrations or other data changes.
• Restoration evidence: Restore a backup into an isolated environment and check that the necessary data is usable. The existence of a backup is weaker evidence than a demonstrated restore.

Agree on acceptable downtime and data loss in business terms. How long can customers be unable to work? How much recent work could the business afford to lose? Those answers should guide recovery arrangements.

If a separate business website supports signups or inquiries, include a maintenance check for the business website alongside application ownership. The acquisition path needs maintenance too.

Separate release blockers from repair priorities

I would use the following as practical release gates, not as a formal certification standard. Block the affected exposure when evidence shows:

• Unauthorized access to private customer data.
• Exposed privileged credentials.
• Bypassable paid entitlements or harmful billing errors.
• Destructive data defects.
• No viable recovery path or insufficient control of critical business accounts.

An unknown critical protection also needs evidence before exposure. “We have not checked account isolation” is not equivalent to “account isolation works.”

The response can be narrower than stopping all development. Disable live payments while billing is repaired, for example, or keep a pilot on synthetic data while access controls are reviewed. The restriction must actually prevent the affected harm.

Separate these issues from usability improvements such as awkward wording or inconsistent spacing. Those still matter, but they should not displace work needed to protect customers, money, and recoverability.

Give every blocker an owner, a remediation action, and a retest that demonstrates the intended result. Rank the remaining repairs by customer impact and the needs of the next release, with cosmetic improvements below work that affects essential customer tasks.

Decide what to keep, fix, or replace

Choose the smallest defensible change that meets the next release’s requirements.

Messy-looking code alone is not a sufficient rebuild case. Neither is the fact that AI generated it. Replacement needs a specific explanation of what the current component cannot reasonably support and why repair is less defensible.

Hypothetical example: A pilot application saves records correctly, but account B can open account A’s private-record URL. Block real customer data immediately. If a developer identifies a bounded authorization defect, repairs it, and verifies the relevant access paths, targeted repair may be appropriate. If the underlying data model cannot represent the required customer boundaries without substantial change, assess replacement of that component—not automatically the entire application.

Use a release decision record for each material finding. Label its priority as a release blocker, next-release repair, or later improvement. Record missing evidence explicitly, including who will obtain it before the affected exposure is allowed.

If critical checks need implementation help, Kohl Digital’s MVP to Production service covers authentication, data handling, error handling, environment configuration, deployment, and handover for prototypes and AI-built applications.

Your next action is to assemble that decision record for the planned release. Close critical evidence gaps before expanding exposure, and keep the parts that already meet the requirements.