Your AI-generated code probably has security holes. Here is how to find them.
ChatGPT and Cursor write code fast. They also write SQL injections, exposed API keys, and authentication bypasses. Learn what to check before you ship.
If you build with AI, you build fast. ChatGPT, Cursor, Copilot — they generate hundreds of lines in minutes. The problem is that AI models optimize for code that runs, not code that is safe. And there is a big difference between the two.
This is not a criticism of AI coding tools. It is a description of how they work. Language models predict the next token. They do not reason about whether a database query can be manipulated by a malicious user.
What AI-generated code gets wrong most often
Missing authorization checks. The code authenticates users correctly — you need to log in. But once logged in, you can access other users' data by changing an ID in the URL. The authentication is there. The "is this your data?" check is not.
SQL queries built from user input. Instead of using parameterized queries, AI tools sometimes generate code that builds SQL strings by concatenating user input. Any user can manipulate these to read or delete your entire database.
API keys in the code. Not in environment variables where they belong. In the code itself. Committed to GitHub. Readable by anyone with repo access — or public if your repo is public.
No rate limiting on sensitive endpoints. Login forms, password reset requests, API endpoints — without rate limiting, anyone can hammer these with thousands of requests per second.
Why this matters even if you are not a security expert
You do not need to understand the technical details of every vulnerability. You need to understand that your users trust you with their data. If your app gets breached, that trust is gone — and depending on what data you store, you may be legally liable.
What the fix looks like
Automated code review. Every time you open a pull request on GitHub, an automated reviewer checks it for the patterns above and flags anything suspicious with an inline comment — the file, the line, and what is wrong.
CodeSentinel connects to your GitHub repositories and reviews every pull request automatically. It posts findings as inline comments: "this endpoint has no authorization check at line 47" or "this looks like a hardcoded API key at line 12." You see it before it ships. You fix it. Done.
