University of Toronto

Adaptive LLM Worm Uses Local Models to Craft Per-Target Exploits in Heterogeneous Networks

Researchers from the University of Toronto have published a preprint describing an adaptive computer worm driven by LLM agents that spreads across corporate networks by generating individualized attack strategies for each compromised system. Unlike traditional worms such as WannaCry that rely on fixed exploits, this worm maintains its own infrastructure by running local LLMs on infected GPU-equipped machines to analyze vulnerabilities and synthesize new attack vectors in real time. The system was tested in an isolated FakeCorp environment containing Linux, Windows, and IoT devices, successfully leveraging known real-world vulnerabilities to propagate over 48 hours and seven-day autonomous runs. Two core components power the worm: a GPU-hosted LLM component and a hierarchical agent framework with memory, reasoning graph, and tool modules that manage reconnaissance, exploitation, and payload deployment. The authors note that the approach creates an economic asymmetry favoring attackers because the worm parasitizes victim compute resources, eliminating the need for external C2 or commercial LLM services. They warn that adding adaptive reasoning to historical worms such as SQL Slammer, Conficker, or Stuxnet would significantly increase their resilience while remaining slower and noisier than classic self-propagating malware.