From the article:

Our experiments suggest that small AI models may be months away from being capable enough to self-replicate on commercial internet-of-things (IoT) devices, like smart ovens and off-brand Android TV boxes. A replicator like this would be challenging to shut down, since there’s no centralized point to shut off—though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own. In our evaluations, a 9B model got inference running on 8 of the 9 hardware types we tested.

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IoT devices are light on memory, but distributed inference as in Petals makes serving our lightweight models feasible. The representative threat model is Kimwolf, a large IoT botnet that infected over two million devices, primarily off-brand Android TVs. Such smart TV boxes, which are readily available for purchase, typically run forks of the Android Open Source Project (AOSP), which their manufacturers don’t add security features to. Typical boxes in this range ship with between 1 and 4 gigabytes of RAM. Assuming 4GB of RAM at 4-bit quantization, this allows for inference of 9B models with 4 or more machines, and inference of 26B models with 10+ machines.

In the demo at the top of this post, we ran Gemma4-26B-A4B across 15 smart TV boxes with 4GB of RAM each. We sharded the model under pipeline parallelism, where each box holds a small fraction of the model’s layers.

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It appears that GLM-5.2 is able to propagate Llama3.1-8B well, which suggests that future small models of the same capability level could also complete the network enumeration, payload delivery, and inference standup steps of the replication process. Our evaluation keeps the cyber-exploit element of the replication trivial (there’s no authentication over the ADB port), which makes this an upper bound on real-world success. Note that past IoT botnets have replicated via means as simple as going through a default list of manufacturer usernames and passwords, as in the case of the Mirai botnet, so real IoT security might not be dramatically more difficult than the trivial step in our evaluation.

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Unlike coding agents, these replicators would be running below the interactive speeds of user-facing chat and coding harnesses. The spread would be slow and persistent, with tokens being trickled out on edge hardware. We can use our achieved speeds on our real smart TVs as a demonstrated baseline for replication speeds. For Gemma4-26B-A4B, unoptimized speed across a 32k context length token window is about 0.3 tok/s, averaged across prefill and decode in our inference setup workload. More details in Appendix C.

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The number of devices infected by the Kimwolf botnet at its peak, reported by Synthient, is somewhere over 2 million (primarily Android TV boxes). In an equivalently-sized infected fleet of TVs serving AI models, if every group of 15 TVs ran a copy of Gemma4-26B-A4B, we would have ~133k copies (“replicas”). A group of TVs like the ones in our demo can serve 2 agents simultaneously at roughly 0.3 tokens per second each, yielding 80k tokens/s across the botnet.

So, very slow to start with, but might get faster as hardware supporting AI inference becomes more common outside data centers. Maybe game devices would have more powerful hardware?