The Difference Between a Bug and a Vulnerability (and Why It Matters to You)

Your MVP passed the demo — every button clicked, every screen loaded, every investor nodded. So the remaining problems feel like bugs: annoying, visible, fixable next sprint. One of them may not be a bug at all. One of them may already be an open door, and the AI tool that patched it last Tuesday may have been the one that left it ajar. The question worth sitting with before your next deploy: do you know which is which?

My MVP works in demos — so what exactly is a bug?

A bug is a deviation from what your system is supposed to do — visible to a user, bounded to a specific behavior, and fixable without changing who controls the system. Your payment button throws an error on mobile Safari. Your CSV export skips the last row. The damage stays inside the feature, and the fix stays inside the feature.

That definition matters because it sets a boundary. When your prototype was built through vibe coding — prompting an AI tool to generate, extend, and patch code in rapid cycles — bugs are the category you learned to expect. You saw them. You reported them. You asked the AI to fix them. Adam Palicz, Founder of SpiniX, described the texture of that loop precisely: "you build something. there's a bug. you ask AI to fix it. still broken. you ask again. still broken. you repeat. you burn credits. you test. you try again. still broken."

That loop is exhausting. It is also, on its own, manageable — because the worst outcome of an unfixed bug is a frustrated user who sees a broken feature. The system still belongs to you. No one outside gains anything.

Bugs disappoint. That distinction is the entire point of this article.

So what for you? If every problem in your codebase looks like a bug, you are probably applying the wrong mental model to at least some of them — and that mismatch will cost you more than a sprint.

What makes something a vulnerability — and not just a broken feature?

A vulnerability is a flaw that lets an outside party do something your system was never meant to allow — read data that isn't theirs, escalate their permissions, exfiltrate credentials, or take actions as another user. The feature may appear to work perfectly in your demo. The damage is invisible until an attacker finds the flaw and uses it.

That is the sharpest line between the two categories. A bug disappoints a user — a vulnerability empowers an attacker. A broken export is a bug. An export endpoint that returns any user's data when you swap one parameter is a vulnerability. The UI looks identical. The consequence is not.

Vulnerabilities concentrate around a small set of code surfaces: authentication and session handling, data storage and retrieval, and external API integrations. These are also, not coincidentally, the surfaces that AI-generated MVPs touch most often and most carelessly — because they are the plumbing beneath the visible interface, and the interface is what gets praised. As Adam Palicz put it: "visually? usually quite impressive. it seems like the AI is either very good at front-end... or just really prioritizes it. functionally though? you'll likely have to fight a bit to make it actually work..."

The front end impresses. The plumbing leaks.

So what for you? Knowing that vulnerabilities live in auth, data, and API layers — not in the UI — tells you exactly where to look first. That is a short list, not an overwhelming one.

How does the AI fix-break loop turn a bug into a vulnerability without me noticing?

The fix-break loop converts bugs into vulnerabilities through a specific mechanism: each patch generates new, unreviewed code paths — and some of those paths touch auth, credential handling, or data access in ways that were never explicitly tested or intended. The transition happens silently, because the feature starts working, and "working" reads as "fixed."

One anonymized account from a founder building on Cursor captures the cascade directly. A broken user-authentication flow — a bug, visible, reproducible — got patched through a sequence of prompts. Each prompt generated a fix. Each fix introduced a new breakage elsewhere. By the third or fourth iteration, the auth check had been restructured twice, a credential had been hardcoded into the logic to bypass a configuration problem, and a new code path existed that the original intent had never anticipated. The feature worked in the demo. The hardcoded credential and the unvalidated path remained. Neither was flagged. Neither was visible from the interface.

This is the mechanism: the AI is optimizing for "the test passes," not for "no outside party gains access." Those are different objectives, and in a fast fix-break-fix cycle, the second one gets dropped without anyone deciding to drop it.

Kevin Tran described the same loop from a different tool, different stack, same structure: "That endless loop of fixing bugs is the worst. I face the same problem with writing formulas on airtable, tons of hallucinations, functions that don't exist etc." Hallucinations that produce nonexistent functions are bugs. Hallucinations that produce nonexistent — but executable — auth logic are vulnerabilities. The loop does not distinguish between them.

There is a compounding problem with tooling. Adam Palicz noted that AI-native build tools sometimes fail silently on configuration: "sometimes, even though Bolt has access to Supabase or Netlify, it can't complete the required config. the worst part? it doesn't tell you this. so you waste hours, burn credits, only to realize - you had to fix it manually elsewhere." A silent configuration failure around a database or deployment platform is not a cosmetic problem. It is a surface where defaults — not your intentions — govern access.

The fix-break loop is fast. That speed is the value. It is also the exact reason the loop can produce a vulnerability in the same session that it produces a working demo — and leave you holding both without knowing.

So what for you? The problem is not that you used AI to build. The problem is that the loop's success signal — "it works now" — cannot detect what the loop left open. You need a different signal for that.

What do I actually do differently now that I know the difference?

Treat every AI-patched code path that touches authentication, data storage, or external APIs as a candidate vulnerability — until a structured audit says otherwise. That is the single change that separates informed risk from invisible risk.

This is not a call to stop shipping or to throw out your prototype. Your AI-generated MVP contains real value — validated assumptions, working flows, paying pilots. The goal is to keep the speed and add the rigor, not to rebuild from zero.

A structured audit — one that ingests your codebase, maps intent against actual behavior, and pen-tests the surfaces that matter — is the instrument that separates bugs from vulnerabilities with evidence rather than guesswork. It looks specifically at auth flows, data access patterns, and API integrations: the three surfaces where the fix-break loop does its quietest damage. An audit of this kind differs from a code scanner, which finds issues but does not understand what your application is trying to do. Functional app understanding — knowing that this endpoint is supposed to return only the authenticated user's records — is what makes the difference between a finding and a fix.

The window for doing this is not after your Series A. It is before you hand credentials to a paying customer, before you store data you are responsible for, before a compliance question surfaces in a sales call and you do not have an answer.

Your MVP passed the demo. That is worth something — it means the idea is real. Now find out what else it is.