We’re Using AI to Fix the Vulnerabilities That AI Creates. That’s a Problem.

You’ve probably noticed the creeping dread when you push code these days. Your team’s velocity is up 300% thanks to Copilot, Cursor, and whatever LLM is hot this week. But every time you merge, you wonder: what did the AI sneak in? A logic bomb? A backdoor? An insecure dependency that no human would have written?

Here’s the uncomfortable truth: we are now generating vulnerabilities faster than we can find them, and the only thing fast enough to catch them is another AI.

This week, a new model called Dfs-Large1 dropped. It’s a specialized cybersecurity AI designed to scan code for vulnerabilities. The catch? That code was almost certainly generated by another AI. We’ve entered the autonomous arms race: one AI builds the bridges, another AI checks for cracks. And humans are standing on the sidelines, watching.

“Given the insane volume of AI generated code these days, I guess we need specialized AI models to scan it for vulnerabilities,” one developer wrote on a forum. That’s the resigned acceptance of a profession that knows it’s been outrun. Manual code reviews aren’t just slow—they’re impossible. You can’t eyeball 100,000 lines of AI-generated boilerplate and expect to spot the subtle injection point.

But here’s the twist: the same model that generates the vulnerabilities can be used to generate the fixes, creating a closed loop where the disease and the cure are both automated. We’re not just using AI to help us—we’re using AI to protect us from itself. It’s like giving a self-driving car a built-in mechanic that fixes the bugs the car creates while driving.

This isn’t a hypothetical. Companies are already deploying Dfs-Large1 to scan their CI/CD pipelines. The model is trained on millions of known vulnerabilities and patterns of insecure code. It’s fast, it’s cheap, and it never gets tired. But it also never asks: should we be writing this code at all?

Take a position: this is dangerous. Not because the AI will fail—it will probably succeed at finding most vulnerabilities. But because we are removing human oversight from the entire software supply chain, leaving our digital infrastructure at the mercy of algorithmic self-regulation. When both the builder and the auditor are AI, who audits the auditor? And who takes responsibility when the house of cards collapses?

Security professionals need to accept that traditional manual code reviews are dead. But they also need to understand that relying solely on AI-vs-AI creates a fragile system. The real answer is hybrid: let AI handle the volume, but keep humans in the loop for the critical, high-stakes decisions. The problem is, most organizations won’t do that. They’ll just set the AI to scan and forget, because it’s cheaper.

So here’s the golden quote you’ll screenshot and send to your team: “If you think AI-generated code is a productivity boost, wait until you see the vulnerabilities it creates—and the new AI you’ll need to buy to find them.”

The cybersecurity industry is now an autonomous arms race. The winners won’t be the humans who write the best code or the best security audits. The winners will be the ones who figure out how to make the two AIs talk to each other honestly. But honest AI is an oxymoron. So we’re left with a question: do we trust the machine to police itself?

I don’t have a clean answer. But I know this: the day you stop pretending you’re in control is the day you start building systems that don’t need you. That day is here.