AI models may be capable of self-replicating without access to a datacenter

來源原標題

AI models may be capable of self-replicating without access to a datacenter

機構

Anthropic

原始標籤

Research

原始連結

https://www.anthropic.com/research/self-replicating-ai

摘要原文

Frontier Red Team AI models may be capable of self-replicating without access to a datacenter Oct 12, 2026 William Zhou, Daniel Freeman Our experiments suggest that small AI models may be months away from being capable enough to self-replicate on commercial IoT devices, like smart ovens and off-brand Android TV boxes. A replicator like this would be challenging to shut down, since there’s no centralized point to shut off - though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own. In our evals, a 9B model got inference running on 8 of the 9 hardware types we tested. Models of 30B parameters and under can already complete many of the individual steps for replication today, though they make mistakes when composing them end to end. In our simplified evals, GLM-5.2 and stronger models are capable of end to end replication. If small models improve at their historical rate, they’ll match this capability level by the end of this year. Small weight-based replicators may be possible soon after. Introduction Ever-improving AI capabilities make a longstanding concern increasingly realistic: autonomous replication and adaptation (ARA), which involves a model deploying and propagating itself without human control. In cyber, a worm is a program that copies itself from one computer to the next without human input. Prior work has already shown that AI agents can act like one: scanning their network, executing an exploit, and copying model weights to a target machine across a GPU cluster. Such an “AI worm” could carry its model weights with it as it reproduces (or re-download them from a public hub), and then run inference directly on compromised hardware. Effectively, the weights would become part of the malware payload. Datacenter GPUs would be a clear, high-value target for such malware. But expensive accelerators aren’t strictly necessary. In this blog post, we show that a distributed botnet of IoT devices—which are cheap and typically poorly defended—could also provide enough compute to run a capable model. We run Gemma4-26B-A4B, distributed over 15 inexpensive off-brand Android TV boxes (~$55 each) that we bought and networked together, at 0.39 tokens per second. Each tile below shows one physical TV box holding two of the model’s layers as the model runs. A fleet of 15 Smart TV boxes running distributed inference of Gemma4-26B-A4B. Of course, there are many other steps to showing that an autonomous worm can propagate itself across a network of weak machines. For the purposes of this blog post, we break down the replication chain into a few steps. For this study, we decompose autonomous replication into four practical stages: finding targets, gaining execution, establishing inference, and continuing propagation from the new host. We largely ignore the cyber exploit element of the replication process (the first two stages above), since earlier papers CyberGym , ExploitBench , and adaptive worms already show that small models are capable of cyber exploits, especially when given the right tools. But a less commonly measured part of the replication process involves setting up inference on compromised devices, which is our focus here. In this post, we first lay out the threat models that we think are most likely to be realized. Then, we test whether small models can set up inference on unfamiliar hardware, and whether they can chain the full replication cycle together across a fleet of machines. After that, we estimate how fast such a worm could spread, and test whether it could learn and adapt as it replicates. We conclude with how AI worms would differ from pre-AI malware, and brief suggestions for defenses against such replicators. Threat modelling In this blog post, we’re focused on one way that AI worms might propagate: small models that replicate through compromising their host machines. But there are several other methods that AI worms might try: they could steal access in order to spin up hosts (for example, API keys or platform credentials) or pay for inference through legitimate work (e.g digital freelancing). The threat models that we think are closest to being realized today are the following: A small model, replicating via host compromise. In this post, we’ll call autonomous worms that run their models directly on compromised hardware weight-based replicators . Home and small business IoT devices might be the first target for such attacks because they’re connected to the internet and, historically, have had minimal security measures in place. A model that’s too large to fit on one device can be split into pieces, or shards, that run on separate machines that communicate with each other to collectively run the model. Machines that could run a significant model shard for a <30B model include smart fridges, smart ovens, automatic vacuum cleaners, and lawnmowers. A growing number of home and business devices, such as security cameras and video-conferencing units now ship with small built-in AI chips, designed to run AI models on the device itself. This extra computing power will likely make them attractive targets as well. Human hackers often breach secure systems through social engineering (for example, convincing a company’s IT department that you’re an employee who forgot your password) rather than hacking. This tradeoff appears to be flipped for today’s small models, which are relatively strong at agentic coding and hacking compared to their social engineering capabilities (see Appendix E), though this might change as models improve. A large, advanced weight-based replicator focused on datacenters and other high-compute targets. Larger models tend to have correspondingly better cyber capabilities, but will also need to breach (relatively) hardened datacenters to run themselves. We split this threat model into two subcategories: Frontier replicators: Large closed-weight replicators run on their lab’s infrastructure, and can spin up unmonitored internal deployments or exfiltrate their weights onto external compute. Open-weight replicators: These replicators start off relatively lower-resourced (due to not benefitting from a lab’s compute), but start unmonitored. An API-based replicator, in which a model would replicate by stealing credentials for their own inference API. These replicators would require a model provider to unknowingly serve them, and their persistence may be worse, since they’d likely be easier to discover (although their rate of spread may be higher, if for instance they’re able to replicate using victims’ accounts). In practice, an AI worm wouldn’t necessarily consist of just one model or strategy. A swarm of different models deployed on different types of hardware using a variety of replication schemes might represent a more realistic ‘worst case scenario’ attack. In this blog post, we consider the feasibility of small weight-based replicators (section 1 of the above). We focus on this because it’s a near-term threat model that can be investigated directly. Our demo at the top of this post, a 26B model running across 15 boxes, shows that IoT devices can run surprisingly capable models. Could these models be capable enough to carry out autonomous replication? We examine this in two parts. First, can small models get a model running on unfamiliar hardware? And second, can they do the whole cycle (find an uninfected device, copy themselves over, start running there, and repeat) all together in a single run? A. Inference setup Of the stages in the above diagram showing the replication cycle, inference setup is the least studied and may be the most challenging for small models. It requires a fairly long sequence of hardware and OS-specific decisions around picking a compatible inference stack, finding the right versions of packages, and debugging machine-specific issues. These steps are serially dependent, and failures are difficult to recover from.