You built it with AI, and it works. Maybe you used Cursor or Claude or Replit, described what you wanted, and watched a working product appear in days instead of months. That is genuinely great, and it is cheaper than hiring a development team to build it from scratch. But before you put it in front of paying customers or an investor, there is a question worth sitting with: should you actually ship it as is?
The uncomfortable truth is that AI is not there yet when it comes to getting the hard parts right. It writes code that looks correct and runs, which is exactly what makes it dangerous. It is very good at the happy path and quietly bad at the edges, the security, and the assumptions.
Where AI-built code tends to break
Security holes. AI routinely produces code with missing authorization checks, injectable queries, leaked secrets, and access-control mistakes. It is not being careless, it just optimizes for something that works, and insecure code very often works until someone attacks it.
Logic flaws. The subtle business-logic bugs, the wrong assumption about who can do what, the edge case nobody described in the prompt, these are the ones AI misses and the ones that surface in front of your first real users.
Overlooked edge cases. AI builds what you asked for. It does not push back on what you forgot to ask for. The empty state, the concurrent request, the malformed input, the thing that only happens at scale.
This is not an argument against vibe coding
To be clear, building with AI is a good idea. Shipping something unchecked because it was built with AI is not. The right mental model is the same one a sensible team uses for any code: fast building is great, but you still review it, test it, and try to break it before customers do. The only difference with AI-generated code is that the reviewer needs to be a little more suspicious, because the code carries none of the hesitation a human writes into it when they are unsure.
How to check a vibe-coded product before you ship
Four things catch the vast majority of problems. A quality-assurance pass to find the broken flows and wrong assumptions. A security review of the code and architecture for the auth, injection, and secret-handling mistakes. Fuzzing, which throws malformed and unexpected input at the running app to see what falls over. And a penetration test that treats the live product the way an attacker would. Together they tell you what is broken, what is exploitable, and what to fix, before it is a customer or an investor finding out for you.
Where we come in
This is exactly what our vibe-coding QA and security review is for. We QA, security-review, fuzz, and penetration test AI-built products, led by a published security researcher with six CVEs, and it still costs a fraction of what a full development team would. If you have shipped or are about to ship something built with AI and you want to know it is actually good, get it reviewed or read more on our security page.