AI writes code that looks right, and that's exactly the problem. A quiet security bug tucked inside clean, confident-looking code is far harder to notice than a clumsy one waving its hands at you.
The upside is that generated code tends to break in the same few ways. Learn the patterns and they jump out at you. Here are the ones you'll run into most, why they happen, and how to catch them before someone else does.
1. Missing input validation
Far and away the most common one. The AI tends to assume incoming data will be the right size and shape. Real people aren't that considerate, and attackers make a hobby of not being.
Ask for a function that handles a form and it'll nail the happy path. Then feed it an empty field, a 10,000-character string, or a lump of malicious script, and it often either falls apart or waves the bad data straight through to somewhere it really shouldn't go.
How to catch it: send it bad input on purpose. Empty, enormous, full of odd characters, laced with injection strings. If the code doesn't push back or clean it up, it's not done.
2. SQL injection
The training data is full of examples that build queries by mashing strings together, which happens to be the textbook setup for SQL injection — one of the oldest and nastiest bugs we've got, and somehow still everywhere.
When a query gets built by dropping user input straight into a string, the right input stops being input and starts being instructions. Instead of pulling one user's record, someone reads the whole table. Or drops it.
How to catch it: hunt for any query stitched together with concatenation or string formatting that includes user input. The fix is nearly always parameterized queries or prepared statements, where the database treats what the user typed as data and never as commands.
3. Hardcoded secrets
The AI will happily plant API keys, passwords, and tokens right in the source. Not out of carelessness, really — it's just that heaps of example code online do exactly this to keep things short.
Trouble is, a hardcoded secret ends up in your git history, your logs, and every place that code ever travels. The moment it's committed it's basically public, and rotating it is a genuine pain.
How to catch it: grep for anything that smells like a credential sitting in the open. Secrets belong in environment variables or a real secrets manager, never baked into the code.
4. Weak or missing authentication
Ask for a feature and the AI zeroes in on making the feature work, not on whether the person using it is allowed to. So you end up with endpoints and functions that check nothing, or check something you can slip past without trying hard.
That's how you get the classic: change an ID in the request and suddenly you're reading someone else's data. Common as dirt, and about as damaging as it gets.
How to catch it: for every action, ask who's actually allowed to do this and where that gets checked. No clear answer in the code means the check isn't there.
5. Outdated and vulnerable dependencies
The model suggests libraries and versions based on training data with a cutoff, so it'll sometimes point you at a package with a known hole that's since been patched, or drag in dependencies you didn't need in the first place.
Every extra library is more surface for someone to attack. An outdated one with a public CVE against it is more or less an open door.
How to catch it: run a dependency scanner over anything the AI recommends. These check what you're pulling in against known-vulnerability databases and tell you flat out what needs bumping.
6. Overly revealing error messages
Trying to be helpful while you build, the AI often writes error handling that blurts out internals — file paths, database layout, full stack traces, sometimes even bits of sensitive data. Wonderful for debugging. Awful the moment it's live.
An attacker reads those messages like a map you drew for them. Every detail you spill is one less thing they have to figure out.
How to catch it: break things on purpose and read what comes back. The user should see something plain and generic. All the juicy detail goes to your private logs, not to their screen.
7. Insecure defaults
Left to pick for itself, the AI reaches for whatever's easiest, which is rarely whatever's safest. Wide-open access rules, security features switched off, weak crypto settings, cookies missing their protection flags. Every one of them behaves in a demo and quietly leaves you exposed in production.
How to catch it: actually read the config and settings it generated. For each default, ask whether it's leaning toward easy or toward safe, and tighten the ones pointing the wrong way.
The pattern behind the patterns
See the thread running through all of these? Nearly every one comes back to the same root: the AI is optimizing for code that works in the ordinary case, with zero sense of someone deliberately trying to wreck it.
Reading carefully catches some of it. But that's also why reading alone won't save you — the reliable move is to attack your own code somewhere safe before it ships. Throw hostile input at it, try to walk past its checks, see what gives.
Which is exactly what a secure code review lab is for. Putting AI-generated code through realistic attack simulations on Simulation Labs turns all of this from abstract worry into something you watch happen and fix, instead of a surprise someone else hands you later.
Generated code isn't the enemy. Unchecked generated code is. Learn the patterns, test for them every single time, and you keep all of AI's speed without shipping its blind spots along with it.
