AI coding assistants are brilliant at producing working code — but "working" and "secure" aren't the same thing. The same mistakes come up over and over in AI-generated projects. Here are seven of the most common, and how to fix each one.
1. Hard-coded secrets
The mistake: API keys, passwords, or tokens written directly into the code. The fix: move them to environment variables, add your .env file to .gitignore, and rotate any key that was ever committed.
2. SQL injection
The mistake: building database queries by gluing user input into a string. An attacker can inject their own SQL and read or delete your data. The fix: always use parameterized queries or an ORM that escapes inputs for you.
3. Cross-site scripting (XSS)
The mistake: rendering user-provided content onto a page without escaping it, letting attackers run scripts in other users' browsers. The fix: escape output by default and avoid dangerous "render raw HTML" APIs unless you've sanitized the content.
4. Broken access control
The mistake: checking that a user is logged in, but not that they're allowed to access the specific record they asked for — so anyone can read anyone else's data by changing an ID. The fix: verify ownership on the server for every protected resource, on every request.
5. Outdated, vulnerable dependencies
The mistake: pulling in packages with publicly known vulnerabilities and never updating them. The fix: run dependency audits regularly and keep packages current.
6. Missing input validation
The mistake: trusting that input is well-formed — the right type, length, and range — which leads to crashes and exploits. The fix: validate every input against an explicit schema and reject anything that doesn't match.
7. Leaky error handling
The mistake: shipping verbose stack traces and debug output to production, handing attackers a map of your internals. The fix: log details privately, show users a generic error, and disable debug mode in production.
How to catch these automatically
You could review every line by hand — but if you're vibe coding, you probably didn't write every line, and these bugs are exactly the kind that hide in plain sight. Automated scanning catches them for you.
VibeGuard AI scans your repository for all seven of these issues and more, using the same trusted scanners professional security teams rely on, then explains each finding in plain English and offers a fix you can open as a pull request. It's the fastest way to make sure the code your AI wrote is safe to ship.