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5 Real Bugs I Found in AI-Generated Code (That Looked Fine)

5 Real Bugs I Found in AI-Generated Code (That Looked Fine)

AI makes building easy. It also makes it easy to ship broken systems.


I built a working app in few hours.

No planning. No architecture. No real thinking.

Just prompts.

It worked.

Or at least… it looked like it worked.

That’s the part that worries me.

Because “working” is doing a lot of heavy lifting here.


1. The "Everything Is Admin" Bug

AI wrote this:

if (user.role = "admin") {
  allowAccess();
}
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At a glance, nothing feels off.

But that’s assignment, not comparison.

Which means every user becomes admin.

No crash. No error. No warning.

Just a silent failure sitting in your code.

This is exactly the kind of bug that slips into production.


2. The API That Never Existed

AI assumed this:

const user = await getUser();
console.log(user.name);
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Looks reasonable.

But the real response was:

{ data: { user: { name: "John" } } }
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It worked during testing because the mock matched the assumption.

It broke instantly with real data.

And the worst part?

Nobody thought to question it.


3. The “Works on My Machine” Performance Killer

const users = await db.getAllUsers();
const active = users.filter(u => u.active);
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This works perfectly.

Until it doesn’t.

With 50 users, it’s fast.

With 200,000 users, it’s a problem.

Now you’re dealing with slow APIs, memory spikes, and random timeouts.

AI gave you something that was technically correct.

It just wasn’t built for reality.


4. The SQL Injection Gift

app.get("/user", async (req, res) => {
  const query = `SELECT * FROM users WHERE id = ${req.query.id}`;
  const result = await db.query(query);
  res.send(result);
});
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This is where things get serious.

No sanitization. No validation.

Now someone sends:

?id=1 OR 1=1
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And suddenly, your entire database is exposed.

No alarms. No obvious signs.

Just data… gone.


5. The XSS Time Bomb

res.send(`<div>${userInput}</div>`);
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Looks harmless.

But nothing is escaped.

Now a user sends:

<script>alert("hacked")</script>
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And your app happily runs it.

Because you trusted the input.


What’s Actually Happening

AI isn’t dumb.

It’s just confidently incomplete.

Most of the time, the code it generates looks correct.

It runs. It passes basic checks. It feels right.

And that’s exactly why people trust it.

But the missing 10% — the edge cases, the assumptions, the things you didn’t verify — that’s where things break.

And they don’t break immediately.

They break later. Under load. With real users.


The Real Skill Shift

A few years ago, writing code was the hard part.

You had to think through everything. Debug carefully. Read documentation.

That effort forced understanding.

Now, you can generate working code in minutes.

Which means you can also skip understanding in minutes.

That’s the shift.

And most people haven’t adjusted to it yet.

The engineers who do well now aren’t just fast.

They’re careful.

They question things.

They look at working code and ask, “what’s wrong with this?”


What Actually Works (Simple Rule)

Before using AI, take a minute.

Write what you’re building.

Not in your head. Actually write it down.

What problem are you solving?
Who is it for?
What does success look like?

Then use AI.

And after it gives you code, assume something is wrong.

Check everything:

  • inputs
  • outputs
  • edge cases
  • performance
  • security

Because something usually is.


When AI Coding Still Works

It’s not all bad.

AI is great for:

  • quick prototypes
  • side projects
  • learning new tools
  • exploring ideas

But for anything real — anything that touches users, data, or scale — you need more than “it works.”

You need understanding.


The Bottom Line

AI didn’t remove thinking.

It removed the cost of building.

Which means you can now build bad things faster.

Or good things faster.

That choice is still yours.


Final Thought

Before your next prompt, pause.

What are you actually building?

Why does it matter?

Because the code will come easily.

Understanding won’t.


We write about real-world AI and engineering problems (no fluff) at https://www.nandann.com.

Most of this comes from things that looked fine… until they weren’t.

Follow if you’re building with AI — this is just the start.

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