University of Toronto Researchers Demonstrate Autonomous AI Worm That Adapts, Exploits, and Self-Replicates Without Human Control

kResearchers from the University of Toronto have developed and tested a proof-of-concept artificial intelligence-powered computer worm capable of independently navigating networks, identifying vulnerabilities, creating customized attack plans, and replicating itself without human assistance. Notably, the system operates using a locally hosted open-weight large language model (LLM), eliminating reliance on commercial AI platforms.

The research paper, published on arXiv on June 2 and currently undergoing peer review, highlights a growing cybersecurity concern: traditional patching strategies focused on individual vulnerabilities may become ineffective against malware that can dynamically analyze systems, consume newly published security advisories, and generate attack methods in real time.

During testing, the AI worm was deployed in 15 isolated experiments across a purposely vulnerable network consisting of 33 hosts. On average, it discovered 31.3 vulnerabilities and obtained elevated privileges on 23.1 systems. The worm successfully replicated itself to 20.4 hosts, covering approximately 62% of the network over a seven-day period without prior knowledge of the network structure or any human guidance.

Conventional worms are typically designed with predefined exploits. Once those vulnerabilities are patched, the malware’s ability to spread is significantly reduced.

The Toronto researchers took a different approach. Their worm leverages an open-weight large language model running on a single GPU to create attack strategies on demand. Instead of depending on a fixed exploit chain, it generates tailored attack logic based on the characteristics of each target system. It also functions independently of commercial AI services, meaning it is not subject to API restrictions, rate limits, or account suspensions.

The project was led by Associate Professor Nicolas Papernot and involved researchers from the University of Toronto, the Vector Institute, the University of Cambridge, and ServiceNow.

Testing took place on an isolated environment known as “FakeCorp,” which consisted of 33 hosts running a variety of operating systems, including Ubuntu, Debian, Rocky Linux, Alpine Linux, Windows Server 2008 R2, Windows Server 2019, Windows Server 2022, and several IoT devices.

Every host contained at least one genuine vulnerability. Five machines were equipped with GPUs. During primary experiments, the worm used a shared GPU inference pool to emulate computational resources obtained from compromised systems. Researchers also tested full GPU-based replication, where the worm deployed its own LLM on an infected GPU machine. This approach achieved a success rate of 68.8%.

Under this architecture, compromised GPU systems acted as reasoning hubs, supplying AI inference capabilities to less powerful devices that could not run the model independently.

Across all 15 tests, the worm achieved elevated access on an average of 23.1 hosts and successfully replicated to 88% of those systems. The malware propagated through as many as seven generations, averaging 5.

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