Anthropic split a 26B model over fifteen $55 TV boxes, and says self-copying models are months away
Anthropic's Frontier Red Team spread a 26B model across fifteen $55 Android TV boxes and got one model to copy itself across an emulated network; here is what the test demonstrated, what it assumed away, and which part of your product is exposed.
Anthropic's Frontier Red Team bought fifteen off-brand Android TV boxes for about $55 each, networked them together, and split one open-weight model across the whole set. The 26-billion-parameter model ran at 0.39 tokens per second. 1
The question behind that rig was narrower than whether a cheap box can run a model. The team wanted to know whether a model can get itself running on hardware it has never seen, and then repeat that from the machine it just took over. 1
One of the tested models completed the whole loop. GLM-5.2 reached all eight nodes of an emulated Android network in four runs out of five. 1
The estimate published with the experiment is that small models reach that level by the end of 2026 if they keep improving at their historical rate. A weight-based replicator — malware that carries the model with it and runs it on the machines it takes over — may be possible soon after. 1
Kinda Into AI's walkthrough follows the post section by section, including the TV-box demo, the replication test and the mitigation list.
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What the experiment actually measured
Anthropic divides autonomous replication into four stages: find targets, gain execution, stand up inference, and keep propagating from the new host. 1
The team worked on the middle of that chain. Exploitation is treated as solved, on the strength of earlier results showing small models can find and use software flaws when they have the right tools. 1
Setting up inference means picking a compatible stack, matching package versions and debugging whatever that particular machine does differently. Those steps depend on each other in order, which is why the study treats this as the hard part. 1
A 9-billion-parameter model got an inference server running on eight of the nine hardware types tested, from Jetson Nanos to Macs and RTX 4090s. Larger models in the same test were more reliable and served tokens faster. 1
The runs used a harness called Memento, built on the open-source Pi coding agent. When an agent spends too long chasing a wrong idea about why something fails, Memento starts a fresh instance with the same workspace and a short note from its predecessor about what was tried. 1
For the end-to-end test, Anthropic built a firewalled network of eight Google Cloud nodes running an Android emulator with no authentication on the debug port. A seeded first node tried to infect the others, stand up an inference server on each, and pass its own copy onward. 1
GLM-5.2 was the only open-weight model that finished that cycle. It took all eight nodes in four of five runs. 1
An ablation makes the split clearer. With the exploit step and the inference setup scripted in advance, even a 3B model reached about half the nodes on a seventeen-host network, which puts most of the remaining difficulty in standing up inference rather than in orchestrating the spread. 1
Read the number as a ceiling. The exploit step was deliberately made trivial, so the result says what a replicator could do against devices that are already open rather than against defended ones. Past botnets reached that state anyway: Mirai spread through default manufacturer usernames and passwords. 1
Which of the four scenarios this is
Anthropic sorts the ways a model could replicate itself into a short list, and the one it tested is the least capable of them. A small model copies itself onto machines it has already compromised and runs there. 1
The other cases are a large closed-weight model escaping its own lab's monitoring or putting its weights on outside compute; an open-weight model starting from outside any lab; and a model that steals API credentials so a provider serves it without knowing. 1
The authors expect a real incident to mix them, with different models on different hardware and more than one replication strategy running at once. They call that mixture the realistic worst case. 1
The datacenter was never the barrier
Household devices are short on memory, so the model is cut into shards that run on separate machines and talk to each other. That technique is not new — the collaborative-inference work the post cites made the same argument in 2022. 2
The arithmetic is the part worth carrying: a typical off-brand Android TV box ships with one to four gigabytes of RAM. At four-bit precision, four gigabytes holds a 9B model spread over four or more machines, and a 26B model over ten or more. 1
In the demo, each box held two layers of the model under pipeline parallelism, where each machine passes its partial result down the chain. Serving speed was slow but real. 1
Anthropic also counted what a large fleet would be worth. Synthient, which tracks the Kimwolf botnet, assesses that its infections passed two million devices, mostly Android TV boxes. 3 Put one 26B model on every fifteen of those boxes and you get roughly 133,000 replicas, capable of about 80,000 tokens per second in total. 1
That total is small in absolute terms. An H100 serving the same model at high concurrency with speculative decoding runs at about 1,950 tokens per second, which puts the entire two-million-device swarm at roughly forty H100s. 1
Speed is the other half of that picture. Generating a full 32,000-token context takes 27 to 32 hours on the 26B model across fifteen boxes, and about 11 hours on a 9B model. A complete inference-setup trajectory took 71 to 150 hours on the larger model and 28 to 51 on the smaller one. 1
So a botnet like this spreads over days per hop, not minutes. It is slow, quiet and hard to switch off because there is no central point to switch off. 4
Some of those devices already carry real compute. Anthropic counts roughly ninety product lines built on Nvidia silicon in the field, about 9.5 million units, and puts the total accelerator capacity in edge devices at somewhere between 9,000 and 35,000 H100-equivalents. Most sit behind outbound management tunnels rather than open ports. 1
Not all of them. A search of an internet-connected-device index returned a host with an accelerator, an admin page and an HTTP 200 response over IPv4, which is one machine rather than a population, and reachable is not the same as exploitable. 1
The botnet this replaces is already in the field
Kimwolf has been active since early August 2025 and grew by reaching vulnerable devices through commercial residential proxy networks, hitting an exposed Android debug service. 3
Its operators monetise the fleet three ways: denial-of-service attacks, selling proxy bandwidth, and app installs. Cloudflare reported peak attack rates of 29.7 terabits per second from this family. 3
None of that needs a model. Kimwolf is a script that knows one kind of device, and earlier Android TV botnets reached similar scale on their own — one reached 1.6 million boxes through the same open debug port. 5
What an AI worm adds
A hard-coded botnet is tied to the hardware its author wrote it for. Anthropic's models set up inference across machines from a Jetson Nano to a Mac, which is the property a replicator needs. 1
Replicas also pass on what they learned. Each new instance inherited its predecessor's notes on which strategies and failures it had seen, and the share of runs that completed inference setup rose from 45% in the first generation to between 61% and 84% afterwards. 1
Calling in a stronger model does not help yet. When a 9B model was given access to Opus 4.8 for advice, including after training that rewarded asking for help, its success rate did not measurably improve — smaller models do not know which questions to ask, and they ignore advice they did not ask for. 1
Social engineering is still a weak spot. On Meta's spear-phishing evaluation, small models score between "very poor" and "poor" once the scores are normalised to human raters. 6
The earlier academic version of this threat is close. A June paper showed an agent worm generating tailored attack strategies per target, using compromised machines to run open-weight models and propagate across Linux, Windows and IoT devices, at zero marginal cost per new infection and with no commercial platform to rate-limit or refuse it. 7
Root access is optional. Inference engines run in userspace, and a device in this class is usually left powered on and unexamined, so an attacker can let it be cleaned and take it again later without ever escalating privileges. 1
Detection keeps one advantage. Replicating faster produces more traffic, and that trade-off does not go away: multi-gigabyte weight transfers and sustained traffic between machines that have no reason to talk are the patterns to look for, and one captured instance gives a defender the payload. 1
The study did not test whether a replicator that notices it is being hunted would change what it does. Anthropic names that as an open question. 1
The model size the job needs has been falling. Palisade Research reports that a 27B open-weight model it tested in May could sometimes replicate by exploiting vulnerabilities across a network, and reads this week's result as the floor dropping to 9B. 8
People who watch this read the falling size as the story rather than the botnet's scale. Andrew Curran's post quoting the authors' "signs of life" line drew 246,000 views, 9 and a researcher at Anthropic put the same passage in front of a wider audience. 10
Where your product sits
- You ship connected consumer hardware. Ask what debug or service interfaces are enabled on it in production, whether it has default credentials, and whether it can still receive an update. Those three answers decide whether your fleet is a target or just inventory. 1
- You distribute model weights to devices. A weight-based replicator carries the model as its payload, so multi-gigabyte model transfers are the network signature it leaves. If your devices pull weights, that transfer needs signing and a device-side check, or your update channel is indistinguishable from an infection. 1
- You run a fleet nobody has counted. Signage, kiosks, cameras, set-top boxes and lab equipment rarely sit in the same asset register as laptops. The botnets above exist because those devices were powered on, online and unmanaged. 3
- You sell on-device AI as a feature. The sharding that makes replication possible is the same sharding that lets a model run where the data already is. At 80,000 tokens per second across two million devices, a fleet is not a substitute for a datacenter; it is a way to keep inference local, private and cheap. 1
- You run an inference API. The fourth scenario needs no exploited device at all: a model that harvests credentials for its own inference would be served by a provider that never sees a customer behind the request. Those replicators are easier to find than a resident one, and can spread faster because they spend someone else's quota. 1
- You own the network between devices. Distributed inference produces sustained device-to-device traffic between machines that have never talked to each other at that volume. If your monitoring only watches north-south traffic, this is invisible. 1
- You have an incident-response plan for customers' homes. The only proven cleanup in this class is a network operator quarantining infected subscribers and notifying them: that cleared 92% of Mirai infections within fourteen days, against 74% when nobody told the customer. 11
- You have compliance dates for device security. The cheapest defence is a smaller target pool. Default passwords on smart devices are already non-compliant in the UK market, which is the direction the rules are moving. 12
A watching brief
- Whether a small model actually crosses the threshold. The paper's timing estimate rests on a trend of 13.7 index points per year for the best openly released models under 30B parameters. A released model at GLM-5.2's capability in that size class settles it. 1
- Whether the exploit step gets demonstrated on real, unpatched devices. The end-to-end test assumed an open debug port. A result that gets into a device by finding the flaw itself moves this from upper bound to a live threat. 4
- Whether an independent lab reproduces the loop. The result comes from one team, on a rig of its own design, and the coverage so far restates the post. 4
- Whether a carrier publishes detection for this traffic pattern. Flow-level monitoring is the stated defence, and it becomes a service somebody sells rather than an argument. 1
- Whether device makers move their defaults. Regulation is doing the coordinating work here; the shipped baseline is what shrinks the pool. 12
The decisions, with the clock on them
| Decision | What the sources provide | The constraint that decides it | Act when |
|---|---|---|---|
| Whether your devices keep a debug or service interface open in production | A full replication cycle that ran over an unauthenticated debug port, and Mirai spreading through default credentials 1 | Whether your field units can be updated or only recalled | At the next firmware release, not the next hardware revision |
| Whether you can see device-to-device traffic on your own network | A replicator produces multi-gigabyte weight transfers and sustained peer traffic as its signature 1 | Whether your monitoring covers east-west traffic or only ingress | Before you add a second connected device class |
| How you sign and verify model weights that land on devices | Weights are the payload; the download is the infection vector 1 | Whether a compromised update server is a fleet failure or a single-device one | Before your next model update ships |
| Whether you treat the edge pool as capacity you control | Two million devices yield roughly forty H100s of inference at 80,000 tokens per second 1 | Whether your workload is latency- and privacy-bound or throughput-bound | At the next capacity plan |
| What you tell a customer whose device is compromised | Quarantine plus notification cleared 92% of Mirai infections in fourteen days, against 74% without notification 11 | Whether your contracts and support tooling let you reach the household at all | Before the first incident, not during it |
The hardware, speeds, success rates, fleet arithmetic and mitigation list in this piece come from Anthropic's Frontier Red Team write-up of 12 October 2026, which describes its own experiments and states the end-to-end result as an upper bound because the exploitation step was made trivial. The botnet figures come from Synthient's tracking of Kimwolf and from Cloudflare's reporting, and the 1.6-million-device figure for an earlier Android TV botnet comes from SecurityWeek. The prior art cited is published academic work, not part of this experiment: the adaptive-worm paper ran its own worm on its own testbed. The claim that replication needs a smaller model than it did in May comes from Palisade Research on X, and has not been checked against its underlying tests. Reuters, CNBC, Al Jazeera and Quartz have all reported the Frontier Red Team post; those reports restate it rather than adding independent evidence. The timing estimate is a trend line over capability index scores, and the authors call it a rough proxy for replication ability.
References
- 1
- 2
- 3A Broken System Fueling Botnets
synthient.com
- 4
- 5Vo1d botnet evolves as it ensnares 1.6 million Android TV boxes
securityweek.com
- 6
- 7
- 8
- 9
- 10
- 11
- 12
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