Small AI Models May Soon Self-Replicate on IoT Devices, Anthropic Warns

Background

Anthropic's findings land alongside other research pointing in the same direction. A recent University of Toronto study built a self-replicating AI worm powered by a local open-weight large language model. The Toronto team tested the worm on a 33-host network of IoT devices. It achieved a spread rate of around 62% within seven days.

What the Research Found

The work centers on models in the 9B to 30B parameter range. Parameters are roughly the internal dials a model adjusts during training, and models in this range are considered small. A 9B-parameter model was able to run inference on 8 out of 9 hardware types the team tested.

Timeline and Targets

Based on current improvement trends, the research projects that small models could reach practical self-replication on commercial IoT devices by late 2026 or shortly thereafter. The example targets include smart ovens and off-brand Android TV boxes.

Containment Concerns

Anthropic's deeper concern is containment. These replicators would run independently of cloud resources, so there would be no central chokepoint for defenders to shut down. They would also be able to adapt to unfamiliar devices, which the research says separates them from conventional malware.

Implications

The uncomfortable irony is that the qualities making small models commercially attractive are the same ones driving the risk. Running AI locally on edge devices promises lower costs, better privacy and less reliance on the cloud. The research itself frames the trend as dual-edged: it could open new applications in IoT and edge computing while also inviting regulatory scrutiny and caution from investors.

Conclusion

There are clear limits to what has been demonstrated so far. Full replication has appeared in simplified evaluations, and the late 2026 date is a projection based on current trends rather than an observed event.