## The Perfect Crime

It looks right. It works right. A user searches for "john," the database returns john's records, and everyone moves on. But lurking in thousands of codebases is a ticking time bomb: AI-generated SQL queries built with string interpolation instead of parameterized statements.

## How the Trap Closes

When developers ask their AI assistant to "add a search endpoint" or "filter users by name," Cursor and Claude default to the deadly pattern: SELECT * FROM users WHERE name = '${req.query.name}'. The assistant confidently commits a textbook SQL injection vulnerability. An attacker passing ' OR '1'='1' as the name parameter doesn't bypass authentication—they dump the entire users table. The code compiles. Tests pass. No alarms sound.

## The Real Horror

This isn't a rare edge case. It's a pattern. The AI tools generate this code reliably, and the vulnerabilities hide in plain sight because they work in normal testing scenarios. Developers without security training—exactly the audience most likely to trust AI-generated database code—ship these flaws into production. A single Cursor session can infect dozens of files.

## Defensive Measures

One developer has started grepping their codebase after every AI session, hunting for backtick usage in database query files. The red flag: template literals anywhere near query or execute calls. The fix is unglamorous but critical: parameterized queries like db.query('SELECT * FROM users WHERE name = $1', [req.query.name]). But this fix only works if someone knows to look for the vulnerability in the first place.

The nightmare isn't that AI generates buggy code—it's that it generates convincingly correct code that fails in the worst possible way, silently, after deployment.