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Researchers at the University of Toronto, in collaboration with the Vector Institute and the University of Cambridge, have published a proof‑of‑concept showing an AI-driven computer worm that adapts its tactics as it spreads.
The prototype used an open‑weight large language model running on compromised machines to reason about each target, read public vulnerability advisories in real time and generate tailored exploits.
Tests were conducted in an isolated 33‑host network (Linux, Windows and IoT devices) over 15 seven‑day runs; on average the agent identified about 31 vulnerabilities, achieved elevated access on roughly 23 hosts and propagated to about 20 hosts.
The authors redacted operational details and disclosed findings to Canadian authorities before release.
The worm hijacks infected devices’ compute (including GPUs) to run the model and can repurpose credentials and workarounds it finds, making single‑patch fixes insufficient.
The team and outside experts warn the threat could grow as devices gain local inference capability and language models improve, and they urge coordinated responses including accelerated patching, AI‑assisted testing and cross‑sector collaboration.
















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