I Let an AI Agent Hack All My Gadgets—and I’d Do It Again
After I removed the safety guardrails from a powerful open-source model, it found vulnerabilities in my household devices and hacked into a PC. But it also told me how to make everything a lot more secure.
You’re probably aware that frontier AI models have attained advanced cybersecurity capabilities in recent months. They can find zero-day bugs in large codebases and scan computers for vulnerabilities at lightning speed. To make things even more exciting, cybersecurity agents sometimes go rogue , colluding with one another and hacking into outside systems to gain an edge.
To get a closer look, I decided to unleash one in my own home network. Over the course of a few days, I watched as my own rogue agent found vulnerabilities in various household devices, hacked into a PC, and showed me that several vibe-coded projects were—unsurprisingly—riddled with bugs. (My wife knew what I was up to, and rolled her eyes each time I proudly announced the discovery of a new vulnerability.)
But Will , you might be thinking, giving an impish, all-powerful cybersecurity agent access to your home network is batshit . And you would be correct! Nevertheless, I believe that a good way to understand the cybersecurity hellscape in front of us is to pay it a visit.
In the end, my experiment was revealing, but oddly reassuring, too. My little network gremlin showed me how vulnerable my home life would be to AI hacking, but it also told me how to make everything a lot more secure. In the end, I discovered that the best way to deal with AI hacking may well be having your own AI hacker.
I got the idea for the experiment after discovering Abliteration AI , a startup that offers access to powerful AI models with the usual guardrails removed.
Most mainstream AI models will refuse to respond to certain queries, and they will certainly refuse to find and exploit vulnerabilities in computer systems. But it’s possible to remove these restrictions by finding and modifying certain patterns within an open-weight model’s internal parameters. You can tweak the patterns that lead to refusals through a process known as abliteration.
