Fallouts

The first wave of vibe-coding AI coding copilots felt magical: type an idea, ship an app. But we’re now dealing with the second order fallouts.

Recently, researchers showed that Lovable, a popular no-code tool, left user emails, API keys and even payment data wide open. A scan of AI-generated code snipets also shows that many copilot suggestions carry a known vulnerability.

Similar patterns are repeating.

Zero filters: Copilots grab the first library or package that seems to fit a user’s prompt. They don’t check how old it is, whether it’s been flagged for security problems or if its license is legal for your project. Bad or outdated code can slip straight into production without anyone noticing

Policy drift: Your company might have policies in place for things like using some open sourced code or making network calls that aren’t encrypted. When Copilots autocomplete code, those rules can get ignored because the model just wants to satisfy the prompt. The violations usually pop up weeks later during a security or compliance audit

Backdoors: Attackers can hide malicious instructions inside innocent-looking config or “rule” files. Copilots read those files and happily turn the hidden instructions into real, runnable code, giving hackers a secret way into your system without raising any immediate red flags

Companies can install some lightweight checks that work as fast as the AI copilots itself. These can stop bad, new libraries suggested in real-time. Keeping an unchangeable history of how the code got in can also work for auditors to later trace where exactly every code line came from.

The productivity dividend with these tools is real but the security debt is compounding at a faster rate.