Anthropic 警示 AI 蠕虫风险：分布式模型可跨智能设备自主复制传播

Anthropic says its research found ‘signs of life for AI worms,’ that could colonize household devices. Experts say they have a point.

Anthropic says its research found ‘signs of life for AI worms,’ that could colonize household devices. Experts say they have a point.

by Oct 14, 2026, 3 PM UTC

Image: The VergeRobert Hart is a London-based reporter at The Verge covering all things AI and a Senior Tarbell Fellow. Previously, he wrote about health, science and tech for Forbes.

Are legions of smart devices — fridges, ovens, vacuum cleaners, lawnmowers, security cameras, and TVs — conspiring to take over the internet? No. But they could soon be unwitting hosts to self-replicating AI models that spread from device to device and prove remarkably difficult to contain.

Welcome to the unsettling world of AI worms, where, depending on who you ask, the prospect of models spreading uncontrollably across household appliances is either an imminent cybersecurity risk or another case of AI companies spreading doomer-tinged hype. In a blog post published this week, Anthropic warns that small AI models could be “months away” from acquiring such capabilities. Its experiments offer some evidence to support that, but leave a huge gap between what researchers have demonstrated and the looming threat they foretell. Still, experts who spoke to The Verge say that while computer worms and fears of self-replicating AI are nothing new, recent advances suggest there are good reasons to take the warnings seriously.

Self-replicating machines have long haunted AI safety researchers. They raise the prospect of systems spreading beyond human control that are difficult, if not impossible, to shut down. As with many fears about advancing AI, attention has largely focused on increasingly powerful frontier models, such as OpenAI’s Astra or Anthropic’s Mythos. But a key limiting factor is their size: They are colossal. Potentially containing trillions of parameters — values learned during training — frontier models require vast computing resources, largely confining them to data centers, where they can be more easily monitored and, in principle, switched off.

Smaller models, however, present a different problem. They’re rapidly improving, can run on consumer-grade hardware, and are often freely available to download. Anthropic’s researchers wanted to know whether one could run across a network of smaller, less powerful devices. They successfully split Google’s Gemma 4, a model with 26 billion parameters, across 15 Android smart TV boxes, none powerful enough to run it alone. It worked, albeit very slowly.