Thousands of applications built with AI code assistants are leaking corporate and personal data onto the open web, according to a new investigation from Wired. The problem isn't the AI itself — it's that people who don't understand security are now able to deploy code that looks functional but is fundamentally broken.
The Democratization Paradox
AI coding tools like Claude Code and GitHub Copilot have made it easier than ever to build applications. You can describe what you want in plain English, and the AI will generate working code. For many people, it feels like magic.
But here's what we're learning: the ability to write code and the ability to write secure code are not the same thing.
The Wired investigation found applications with databases exposed to the public internet, API keys hardcoded into source files, authentication systems that could be bypassed with basic techniques, and encryption implementations that don't actually encrypt.
These aren't sophisticated zero-day exploits. These are Security 101 mistakes — the kind that any computer science curriculum would cover in the first semester. But AI coding assistants don't come with that curriculum built in.
The New Class of Vulnerabilities
What's emerging is a new category of security risk: code that works perfectly for its intended purpose but fails catastrophically under adversarial conditions.
Traditional security vulnerabilities often stem from edge cases or complex interactions between systems. But AI-generated vulnerabilities are different. They're often the result of the AI not understanding why certain patterns exist in its training data.
For example, an AI might generate a login system that checks usernames and passwords correctly — but fails to implement rate limiting, allowing brute-force attacks. Or it might create an API that returns exactly the data you ask for — without checking if you're authorized to see that data.
The code works. It just doesn't work securely.
The Technical Debt Tsunami
Here's the scary part: most of these vulnerabilities won't be discovered until after they're exploited. The people deploying this code often don't have the expertise to audit it for security issues. And the rapid deployment cycles enabled by AI tools mean vulnerable code is reaching production faster than ever.
