I Let an AI Agent Hack All My Gadgets—and I’d Do It Again
| Source: Wired AI
Tags: AI security, abliteration, GLM-5.3, cybersecurity, autonomous agents, home network security, Wired
A Wired reporter let an abliterated GLM-5.3 agent (Abliteration AI) loose on their home network; it found vulnerabilities in household devices, hacked into a PC, exposed bugs in vibe-coded projects, and generated a prioritized hardening plan—suggesting defensive AI value may exceed offensive risk for most users.
Details
The reporter worked with Abliteration AI, a startup that sells access to censorship-removed open-weight models. Using an abliterated GLM-5.3 agent, they scanned and attacked their own home network over the course of an experiment. The agent identified real vulnerabilities in household IoT devices, successfully accessed a home PC, and flagged security flaws in personal software projects built with AI coding tools ('vibe-coded' apps). No attempt was made on financial or sensitive credential stores, keeping this within personal-experiment scope. The more striking finding: the same agent that found vulnerabilities also produced a clear, prioritized hardening checklist—the defensive playbook was arguably more useful than the attack surface it revealed. The reporter frames this as a net positive for security-conscious users willing to run such tests. Abliteration AI is the startup behind the accessible abliterated model distribution. Abliteration (removing the refusal vector from open-weight models) is a documented technique; the models are available on HuggingFace. The practical risk is that commodity hardware and freely available models are now sufficient to run autonomous offensive-security sweeps of home infrastructure.