BuildIndependently confirmed2 publishers2 min readPublished
GLM-5.2 completed a full replication chain on eight $55 TV boxes in four of five Anthropic runs
Anthropic researchers had GLM-5.2 complete a full replication chain on all eight $55 TV-box test nodes in four of five runs. They say no current model is both small and capable enough to spread in the wild, and that gap could close by the end of 2026 at the current pace.
The Engineer · Build desk
A 9-billion-parameter model set up an AI environment running Llama3.1-8B on 8 of 9 tested device types. Setup on a new device would take at least 28 hours and up to 150 hours, depending on the model. Each Android TV box in the Gemma4-26B-A4B test had 4 GB of RAM.
| Figure | Value | Note | Claim |
|---|---|---|---|
| Device types where a 9B model set up Llama3.1-8B | 8 of 9 tested | 8 | |
| Hours to set up on a new device, at least | 28 hours | up to 150, depending on the model | 10 |
| RAM per box, Gemma test | 4 GB | 7 |
What happened
- The team deliberately left out the hacking step and tested the part it judged hardest: whether a model can set up a working AI environment on a device it has never seen.
- A 9-billion-parameter model set up an environment running Llama3.1-8B on 8 of 9 tested device types, so individual steps are already within reach of small models.
- In a separate test, Gemma4-26B-A4B was split by layers across 15 Android TV boxes with 4 GB of RAM each, and it ran slowly but worked.
- The researchers estimate that a botnet the size of Kimwolf would hold compute equal to about 40 specialized AI chips and would have no central kill switch.
Why it matters
- capability A replicator can improve as it spreads. Passing notes between instances lifted Qwen3.5-9B by up to 39 percentage points, so first-generation success rates understate what later generations managed.
- exposure Kimwolf worked only on specific device types. An AI botnet could run on all kinds of hardware, so the pool of reachable devices is every poorly secured connected device.
- decision The researchers name ISPs, which could quarantine infected networks, and manufacturers, which could ban default passwords as the UK has by law. With no central kill switch, those are the points of control.
The 80% figure [15] measures a single stage. The chain has four: scan the network, gain access, set up the AI environment, spread from there [2]. Anthropic researchers William Zhou and Daniel Freeman [1] left the hacking step out and aimed the test at setup, which they judged the hardest part [3]. We think four of five is a best case for that stage and a poor guide to a live network.
For the number to transfer, the target devices would have to resemble the eight test nodes and the break-in would have to succeed first. It is also five runs. A third condition is hosting. A worm of this kind carries its model weights as part of the payload and runs inference on the compromised hardware [5]. The Decoder's report does not say where GLM-5.2's inference ran. The hardware it does describe is the Gemma rig: 15 boxes at $55 each, or $825 in total [16].
The timeline carries a condition. The Decoder frames the risk as months away [20]. The researchers' own date depends on small models continuing to improve at their current pace, and on one model becoming both small enough and capable enough. None is today [4].
Spread would be slow. Setup on a new device takes between 28 and 150 hours depending on the model [10], or about 1.2 to 6.25 days [17]. Kimwolf peaked above two million devices, mostly Android TV boxes [11]. Set against the researchers' 40-chip compute estimate for a botnet that size [13], that is more than 50,000 devices for each chip's worth of compute [18].
What to watch
- A small model that completes the same chain on its own. The researchers say no current model clears both the size and the capability bar.
- Results from a test that includes the break-in step, which this study left out on purpose.
- Published details on GLM-5.2's size and where its inference ran in the eight-node runs.
Clarity's read
What the record supports and how the coverage leans. The claims behind it follow.
Reality
- Evidence55
- Adoption
- Insufficient
- Hype gap+30
- Incentives
- Insufficient
- Confidence50
Claim ledger
Ranked by verification strength, evidence, and original report placement.
- [1]
Anthropic researchers William Zhou and Daniel Freeman studied whether small AI models can spread on their own across poorly secured IoT devices such as smart TVs, smart refrigerators and robot vacuums.
- [2]
The researchers broke replication 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.
- [3]
The researchers deliberately left out the actual hacking step because earlier studies had shown small models can handle those attacks, and instead focused on the hardest part: whether a model can set up a working AI environment on a device it has never seen.
- [4]
No current model is both small enough to run on consumer devices and capable enough to replicate itself; the researchers say that if small models keep improving at their current pace, they could get there by the end of 2026.
- [5]
An AI worm would carry its model weights as part of the malware payload and run inference directly on the compromised hardware.
- [6]
GLM-5.2 was the only model to complete the full replication chain reliably, infecting all eight test nodes in four out of five runs; the tests used off-the-shelf $55 TV boxes.
- [7]
The researchers ran the Gemma4-26B-A4B model distributed across 15 off-the-shelf Android TV boxes, each costing about $55 and having 4 GB of RAM, with each box handling a portion of the model's layers; the model ran slowly but worked.
- [8]
A 9-billion-parameter model managed to set up an AI environment running Llama3.1-8B on 8 out of 9 tested device types.
- [9]
In a test of artificial evolution where each model instance passes notes on successful and failed strategies to its successors, Qwen3.5-9B's success rate climbed from 45 percent in the first generation to as high as 84 percent in later ones.
- [10]
Setup on a new device would take between 28 and 150 hours depending on the model.
- [11]
The Kimwolf botnet, at its peak, infected more than two million devices, mostly Android TV boxes.
- [12]
Traditional botnets like Kimwolf only work on specific device types, while an AI botnet could run on all kinds of hardware.
- [13]
The researchers estimate that in a botnet the size of Kimwolf the total compute would equal only about 40 specialized AI chips, and the network would have no central kill switch.
- [14]
ISPs could quarantine infected networks, as happened with the Mirai botnet, where 92 percent of infections were cleaned up within 14 days; manufacturers could shrink the pool of vulnerable devices by banning default passwords, as the UK has done by law; the report says defense needs coordination.
- [15]
GLM-5.2's success rate in the full-chain test was 80 percent of runs.
- [16]
The 15-box Gemma rig cost about $825 in hardware.
- [17]
Setup on a new device of 28 to 150 hours is about 1.2 to 6.25 days.
- [18]
A Kimwolf-sized botnet of more than two million devices holding compute equal to about 40 specialized AI chips works out to more than 50,000 devices per chip's worth of compute.
- [19]
Qwen3.5-9B's success rate rose by up to 39 percentage points between the first generation and the best later generations.
- [20]
The Decoder reports, citing the Anthropic study, that small AI models may be just months away from copying themselves across poorly secured consumer devices.
ReportedContestedSource: The Decoder, summarising the Anthropic study2 sources— create a free account to open themView cited source
Sources
2 independent publishers whose own reporting we read for this story.
- runtimewire.comAnthropic tests how AI models could spread across cheap home devices
1 article · October 13, 2026
- the-decoder.comAnthropic study warns that small AI models could soon replicate across everyday devices
1 article · October 13, 2026
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Entities
- The DecoderFollow
- GLM-5.2Follow
- Kimwolf botnetFollow
- Gemma4-26B-A4BFollow
- Qwen3.5-9BFollow
- Mirai botnetFollow
- Dario AmodeiFollow
- AnthropicFollow