Anthropic has issued a warning about potential security risks posed by advancing smaller AI models, suggesting they could eventually be weaponized to create autonomous, self-replicating malware targeting Internet of Things devices. The company conducted experiments using lightweight AI models on low-cost hardware platforms including Android TV boxes to explore this possibility.
The research examined whether small AI models could establish inference across multiple IoT devices and autonomously replicate themselves across networked hardware. Anthropic tested models including Gemma4-26B-A4B and Qwen3.5-9B, finding that through techniques like quantization and workload distribution, these models could run on devices with limited memory resources. The company specifically explored whether models could independently discover exploitation methods, gain system access, install themselves on target devices, and propagate to additional hosts.
While the experiments demonstrated that small models can complete individual components of the replication process, none were able to perform full end-to-end replication autonomously during testing. The process also proved extremely slow, with setup times ranging from 29 to 150 hours depending on the model. However, Anthropic noted that larger models like GLM-5.2 appear capable of more reliable end-to-end execution, and suggested that if current development trends continue, smaller models may achieve comparable results later this year.
The company emphasized that such AI-based worms would present distinct challenges compared to traditional malware. Unlike conventional computer worms, these systems could dynamically tailor exploits and payloads to unfamiliar devices without human intervention. Additionally, they would lack centralized points of failure, making them potentially harder to disable once deployed, though replication speeds might remain limited.
The research raises questions about the trajectory of AI capabilities, though experts have noted certain experimental limitations. The test environment included security misconfigurations, such as exposed debugging interfaces, that would not typically appear in production systems. Additionally, replication orchestration involved separate large models rather than small models acting entirely independently, leaving open questions about whether such systems would self-replicate autonomously without explicit instructions.
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