Anthropic Warns Self-Replicating AI Worms Could Spread Across IoT Devices Within Months

Reviewed byNidhi Govil

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Anthropic's Frontier Red Team demonstrated that small AI models could autonomously replicate across IoT devices like smart fridges and Android TV boxes. While current models face significant speed and capability limitations, researchers project 30-billion-parameter models could achieve full replication by end of 2026.

Anthropic Demonstrates AI Models Running on Consumer Devices

Anthropic's Frontier Red Team has revealed research showing that small AI models could potentially self-replicate across internet of things devices, from smart fridges to Android TV boxes, without requiring datacenter infrastructure

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. The company successfully split Google DeepMind's Gemma4-26B-A4B model across 15 off-brand Android TV boxes, each costing approximately $55 and equipped with just 4GB of memory, achieving inference at 0.39 tokens per second

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. This represents roughly 18 words per minute, painfully slow compared to normal operations but proof that AI-powered worms could theoretically operate on low-power devices

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Source: Guru3D

Source: Guru3D

The researchers also demonstrated that Qwen3.5-9B successfully configured inference servers on eight of nine tested hardware types, spanning NVIDIA Jetson Nano devices, Apple Silicon Macs, Xeon servers, Google TPUs, and AMD MI300X accelerators

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. Unlike traditional malware requiring specific hardware configurations, these small AI models adapted their configurations dynamically to unfamiliar devices

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Testing AI Worm Replication Capabilities

In controlled experiments, Anthropic tested whether AI models could orchestrate complete replication sequences across networks. The team used eight Google Cloud nodes running Android 15 emulators with deliberately exposed Android Debug Bridge ports

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. Only GLM-5.2, a massive 744-billion-parameter model exceeding 1TB in size from Chinese start-up Z.ai, completed the full replication sequence across all eight nodes in four of five trials

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However, GLM-5.2 wasn't replicating itself onto smart device hardware. Instead, it established inference servers for the much smaller Llama 3.1-8B model on each target while running on separate infrastructure

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. Three other tested models failed to complete replication sequences at all

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. This gap between demonstration and threat remains significant, as Anthropic hasn't proven that small AI models can independently replicate themselves across consumer hardware without external datacenter support

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Significant Speed and Capability Limitations

Current implementations face substantial practical barriers. Establishing inference environments would require 28-51 hours for Qwen3.5-9B and 71-150 hours for Gemma4-26B-A4B on Android TV boxes

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. By comparison, conventional computer worms like WannaCry infected over 200,000 computers in a single day during the 2017 ransomware attack

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. An AI worm operating at demonstrated speeds would require more than two weeks to reach the same scale

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Anthropicacknowledges that models capable of reliably completing replication sequences aren't currently small enough to run on inexpensive target devices, while models fitting within device memory budgets can't reliably complete the entire process

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. Model weights also impose substantial bandwidth and storage costs on compromised hosts

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Timeline Projections and Expert Debate

Anthropicprojects that 30-billion-parameter models could achieve necessary capabilities for autonomous replication by the end of 2026, based on historical improvements measured through the Epoch Capabilities Index

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. The company warns that such AI-driven malware would be challenging to shut down since there's no centralized point to disable, and unlike traditional botnets, these systems could adapt and figure out unfamiliar devices autonomously

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Source: Tom's Guide

Source: Tom's Guide

Jeffrey Ladish, founder of AI safety nonprofit Palisade Research, emphasizes the significance: "Just because you don't have a big GPU doesn't mean that you can't be a host to one of these models"

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. However, cybersecurity experts remain divided on the threat level. Michael Bailey, professor at Georgia Institute of Technology, cautions against fearmongering, noting "We already had self-replicating malware. We had botnets"

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. Vyas Sekar, computer science professor at Carnegie Mellon, adds that "All of these are classical computer security threats"

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Historical Context and Future Implications

Computer worms have existed for over 50 years, beginning with Creeper in the 1970s created by Bob Thomas and Ray Tomlinson at BBN

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. The first malicious worm deployed by Robert Morris in 1988 infected thousands of computers, making him the first person charged under the US Computer Fraud and Abuse Act

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. What distinguishes potential AI worms is their ability to dynamically tailor exploits and payloads depending on targets, and to learn from failures by leaving notes for future replicators

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Source: Fortune

Source: Fortune

Anthropicsuggests future AI worms could resemble "a slow, distributed 'digital slime mold'" or "a swarm of fruit flies" spreading across networks

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. The company calls for stronger smart device security and preparation for scenarios where such replicators become persistent

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. Critics note Anthropic has repeatedly argued that open-weight alternatives to its closed AI models carry "more risk," raising questions about motivations behind the warnings

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. Regardless of debate around AI hype, the research highlights growing concerns about AI safety and cybersecurity threats as small AI models become increasingly capable on consumer hardware.

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