Security March 2026

The most common security mistakes in AI‑generated MVPs

Most security holes in vibe‑coded apps are not exotic. They are the same five or six patterns, reproduced at speed by tools that have no stake in what happens after you ship.

If you are a non‑technical founder, this is both good news and bad news. The bad news is that AI makes it easier than ever to ship these mistakes. The good news is that most of them are well‑understood, repeatable patterns. You do not need to become a security engineer to spot the danger signs. You do need to know when "it works" is no longer a good enough bar.

This piece is written for founders, not for engineers. The goal is to give you language for what can go wrong, how it tends to show up from your side of the dashboard, and what questions to ask the people helping you build.

1. Access control that only works in the UI

From the founder's perspective, this looks like: "I logged in as a test user, clicked around, and everything looked fine." The navigation only shows the right things. The wrong buttons are hidden. It feels safe.

Underneath, the app may be doing something very different. The interface is hiding actions or data, but the backend is still handing out more than it should. Broken access control has been sitting at the top of critical web‑app risk lists for years for exactly this reason. See the OWASP Top 10: 2025 and OWASP's entry on Broken Access Control.

What it feels like when this bites you

  • A customer emails you a screenshot of another customer's data.
  • Someone changes a number in the URL and suddenly sees records that do not belong to them.
  • An "admin‑only" action ends up being callable by a regular user through some creative clicking.

You do not need the implementation details. You need a clear answer and, ideally, a human who is slightly paranoid on your behalf.

2. Secrets and credentials treated like configuration, not like keys to the building

Most AI‑generated MVPs lean on third‑party services: auth providers, payment processors, email tools, AI models. Each one comes with keys and credentials that are effectively keys to your building.

In a rush, it is common to see those keys end up in the wrong place: checked into a repo, baked into the frontend, shared across environments, or reused long after a test phase. Security agencies have been blunt about this: default or hard‑coded credentials are still one of the easiest ways attackers get in. See CISA‑related guidance on secure by design and eliminating default passwords.

What it feels like when this bites you

  • A contractor or ex‑team member still has access to live systems months after they leave.
  • A staging or demo environment accidentally talks to production data.
  • You discover an API key you use everywhere has leaked, and you have no idea where to rotate it safely.

You do not have to manage the vault yourself. You just need to know that someone is treating these values as more than just config.

3. Input that only gets checked when the user behaves

Generated apps are very good at handling the happy path: the user types what you expect, in the shape you expect, and follows the invisible script in your head. Real users do not do that.

Security guidance repeats the same point over and over: validate what comes into your system, and do not assume that only well‑behaved input will arrive. See the OWASP Top 10: 2025 and the NCSC's guidance on input validation for HTTP‑based APIs.

What it feels like when this bites you

  • The app crashes or hangs when someone pastes a long or strange value into a field.
  • Search, filters, or uploads behave unpredictably when pushed outside "normal" usage.
  • Small test scripts cause big performance spikes.

You are not expected to design validation rules. Your job is to insist that someone has.

4. Logging that creates risk instead of reducing it

Logs are how you reconstruct what happened when something goes wrong. In many AI‑generated MVPs, logging is either an afterthought or a liability.

Two bad patterns show up often:

  • There is no useful logging at all, so you cannot tell what happened during an incident.
  • Logs are full of sensitive data — tokens, personal details, or full payloads — which should never have been written in the first place.

For background, see this summary of the NCSC's API security guidance and this CISA secure‑by‑design pledge summary.

What it feels like when this bites you

  • A customer complains about "something weird," and you have no trail to follow.
  • You discover that logs contain information you would be uncomfortable emailing in plain text.
  • A small issue takes days to untangle because every investigation starts from zero.

Good logging makes problems fixable. Bad logging quietly multiplies them.

5. Assuming normal use, forgetting abuse and scale

Most MVPs are built with a mental model of a "good user" in mind. That is natural. It is also where many security issues hide.

Guidance on API security explicitly calls out rate limiting and abuse handling as core to safety, not optional hardening. See the NCSC's guidance on DoS attack mitigation and the broader API security guidance summary.

What it feels like when this bites you

  • A login or signup endpoint gets hammered and your whole app slows down.
  • A "free" AI feature starts to rack up a real bill overnight.
  • A feature meant for occasional use gets scripted against, and nothing in the system pushes back.

You are not designing the limits. You are making sure the idea of limits exists.

6. Trusting generated code and dependencies more than you trust your own judgment

AI tools are very good at producing plausible‑looking code quickly. They are less good at telling you when a solution adds more risk than it is worth.

Projects that lean heavily on generated code often accumulate a long tail of dependencies and patterns that nobody has really evaluated. Modern risk lists explicitly call out software and data integrity failures — cases where code, updates, or components are accepted without enough verification. See OWASP's entry on Software or Data Integrity Failures and this overview of changes in OWASP Top 10: 2025.

What it feels like when this bites you

  • You are afraid to update anything because you do not know what might break.
  • A small change turns into a week‑long debugging session because the system is full of unfamiliar patterns.
  • Different parts of the app feel like they were written by different people with different assumptions — because, in practice, they were.

These questions are not about blaming AI. They are about making sure someone is taking responsibility for the system you are putting in front of users.

What you are really buying when you buy a security review

For a non‑technical founder, the value of a review is not a list of bug IDs or a pile of patch instructions. It is a clear, human answer to questions like:

  • "Where am I taking on more risk than I realize?"
  • "What could realistically go wrong with real users and real data?"
  • "What should I fix now, what can wait, and what is actually fine?"

The patterns above show up again and again in AI‑generated MVPs. They are fixable. The hard part is seeing them early enough, in the context of your product, before they turn into incidents.

You do not need to do that work yourself. But you do need to know it is work that someone is doing — with judgment, not just with more generation.

Further reading