AI-Powers Worm Exploits Stolen Compute to Infect Mixed Devices Per GBHackers, researchers published a proof-of-concept AI-driven worm that uses an embedded open-weight LLM running locally on compromised GPUs to autonomously scan, exploit, and propagate across mixed networks of Linux, Windows, and IoT devices. The GBHackers report says the worm exposes hosted models as remote "reasoning nodes" so low-power devices forward decision queries upstream, effectively turning victim networks into distributed inference clusters. According to GBHackers, the malware performs reconnaissance, fingerprints hosts, identifies likely weaknesses (the report cites CVEs such as EternalBlue and PrintNightmare), generates tailored exploits at runtime, and writes payloads on the fly. GBHackers also attributes an arXiv source for technical detail on the worm's architecture and its parasitic use of stolen GPU compute. What happened Per GBHackers, researchers developed a proof-of-concept AI-driven worm that embeds an open-weight LLM and runs it locally on compromised GPU hosts. The GBHackers article reports the worm autonomously scans local networks, fingerprints reachable hosts, enumerates services and operating-system details across Linux, Windows, and IoT devices, and generates exploitation plans in real time. GBHackers states the worm can ingest fresh advisory text at runtime, create and adapt exploit payloads on the fly, and share credentials and stolen compute across infected machines. The GBHackers piece cites an arXiv writeup for the underlying architecture and experiments. Technical details Editorial analysis - technical context: Local, open-weight models that fit on a single GPU lower the operational friction for autonomous malware because they remove dependency on external APIs and allow decision loops to run on-host. Models that are quantized or optimized for inference can be packaged into compact runtimes and exposed as networked services, which defenders may not detect via traditional EDR focused on process signatures. The worm pattern described combines automated reconnaissance, dynamic exploit generation, and lateral movement,