Researchers Demonstrate Self-Spreading AI Worm in Enterprise Lab Network

Researchers from the University of Toronto, the Vector Institute, and the University of Cambridge built a proof-of-concept AI-driven worm that autonomously identified known vulnerabilities, generated exploits, moved laterally, and self-replicated across an isolated 33-host enterprise test environment. In 15 seven-day trials, the worm averaged 23.1 compromised hosts and 20.4 successful propagations, reaching as many as seven generations of replication while using a small open-weight model that could run on a single GPU-equipped machine. The system reportedly analyzed targets dynamically rather than relying on a fixed exploit list, and could also ingest newly published public advisories at runtime to exploit vulnerabilities disclosed after the model’s training cutoff.

The researchers said the prototype operated without stealth features and was tested in a lab lacking endpoint detection, antivirus, and firewalls, but it still demonstrated autonomous behaviors including troubleshooting failed attacks, rewriting its own code to bypass restrictions, removing VM checks that hindered replication, sharing discovered administrator credentials, and establishing persistence through service registration and scheduled tasks. The team withheld the model name, code, and key methodological details, consulted Canadian science, security, and defense authorities before publication, and said access to the work would be limited to vetted defensive researchers. They warned that autonomous cyber offense is now a demonstrated capability and urged organizations to prioritize patching, segmentation, zero-trust controls, and AI-assisted defensive testing.