The computer worm has returned with a new accessory: a language model. Anthropic researchers explored whether small AI models could help create self-replicating infections on internet-connected devices, reviving an old malware idea with a more adaptable payload. The prospect is serious. The current performance, less so.
Traditional IoT worms such as Mirai install code and payloads on compromised devices. Anthropic’s proposed “small weight-based replicators” would carry a model’s weights—the data that lets it generate outputs—or download them from a public hub, then run the model on infected hardware. The weights, Anthropic said, would effectively become part of the malware payload.
The attack sequence the researchers examined has four steps: find targets, gain code execution, set up inference so the model can run, and propagate to new hosts. The difference from a conventional worm is that an AI agent could adapt its attempts to the device it encounters. It could also leave prompts, code, and notes for later copies when a particular approach fails. In theory, that gives the next replicator a small advantage over its predecessor. Even malware is now apparently expected to document its work.
That adaptability is not the same as a working autonomous worm. Anthropic said none of the lightweight models it tested could complete the full chain: discover devices, exploit them, install a payload, configure an inference server, and repeat the process. The hardest part may be getting a model running on unfamiliar hardware. That means choosing compatible software, installing dependencies, and debugging device-specific problems—a series of interdependent steps where one failure can stop the process.
The performance numbers are a reminder that “small” is relative. Gemma4-26B-A4B, distributed across 15 Android TV boxes, managed 0.39 tokens per second. Anthropic estimated a full inference setup would take 71 to 150 hours for an unoptimized version of that model, and 29 to 51 hours for Qwen3.5-9B. The researchers said the much larger GLM-5.2 model appears able to complete the process reliably, but that is not evidence that a lightweight model can already do the same.
So this is a warning about a possible direction, not a report of an AI worm currently roaming through smart TVs. The research suggests that models are getting better at handling the fiddly, device-specific work that could make malware more adaptable. It also shows how much practical friction remains: models need computing resources, compatible software, and a long chain of successful decisions. Anyone nostalgic for the era of worms spreading through poorly secured devices may recognize the premise. The machinery has changed; the vulnerable endpoints have not. For now, the gap between a plausible attack design and a self-sustaining infection is measured in more than a clever prompt.
Sources: Glitch Please! and Ars Technica — https://arstechnica.com/security/2026/10/anthropic-warns-small-ai-models-could-self-replicate-through-iot-devices/ — Reporting on Anthropic’s research into small weight-based replicators.
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