Anthropic researchers tested whether small language models could spread across poorly secured consumer devices like smart TVs or connected home appliances and set themselves up on unfamiliar hardware. The study, led by William Zhou and Daniel Freeman, demonstrated that models like GLM-5.2 can complete the full replication chain across multiple test nodes.
In tests using off-the-shelf $55 TV boxes, the GLM-5.2 model already completed the full replication chain across all eight test nodes. No current model is both small enough and capable enough to pull this off in the wild, but the researchers expect that gap to close soon.
The researchers broke the replication process into four stages: scanning a network for vulnerable devices, gaining access, setting up the AI environment on the new device, and spreading further from there. The four stages of autonomous replication. The researchers focused on setting up the AI environment on compromised devices. | Image: Anthropic
Most models fail, but one already pulls it off Small models already handle individual replication steps fairly well. A 9-billion-parameter model managed to set up an AI environment running Llama3.1-8B on 8 out of 9 tested device types.
But stringing all the steps together, from network scan to a finished copy on a new device, is still too much for most models.
Only GLM-5.2 completed the full replication chain reliably, infecting all eight test nodes in four out of five runs.
The researchers also found that AI worms can learn from their mistakes. They tested a form of artificial evolution where each model instance passes notes about successful and failed strategies to its successors. For Qwen3.5-9B, the success rate climbed from 45 percent in the first generation to as high as 84 percent in later ones.
The worm would be slow but almost impossible to kill A fully autonomous AI replicator doesn't exist yet.
A model needs to be small enough to run on consumer devices and capable enough to replicate itself, and no model currently meets both requirements.
But if small models keep improving at their current pace, the researchers say they could get there by the end of 2026.
Source: thedecoder
