I was drinking my coffee this morning, scrolling through X, when I realized we are completely ignoring a massive shift in cybersecurity.

Sundar Pichai has been warning us about something recently.

He talked about how AI is moving beyond just finding bugs in software. He warned that it is moving into the territory of actually proving that software is exploitable.

There is a massive difference between those two things.

If you run a static analysis tool on a codebase today, it will hand you a list of potential vulnerabilities. Most of them are false positives. It's just a machine guessing that a piece of code looks a little suspicious.

But an exploit? An exploit is proof. It is a working, weaponized script that actually breaks the system.

And a few days ago, a team at Alibaba published a paper that asks a question with very hard consequences.

Can large language models confirm software vulnerabilities by actually building working exploits for them?

The researchers' answer is yes.

But it only works when the AI stops acting like a single genius and starts acting like a team.

That sounds like a minor detail. But when you look at how they actually pulled this off.. it changes how we have to think about the entire software supply chain.

Automated exploit generation is not a new concept. The cybersecurity industry has been trying to automate this for years.

But historically, it usually fails for very familiar reasons.