Have you ever counted the computers in your house? Tally them up now. I’ll wait. You’re probably including your desktop and laptop computers, if you own some. I bet you’re also including the smartphones and tablets. Depending on the size of your family, that might mean 10 at most. Right?
Your count is almost certainly wrong. I’ve got no fewer than 170 computers in my house. That’s because my home, like nearly everyone’s, is now chock-full of devices that a reasonable person might not even know are computers. Mine include speakers and thermostats, wall switches and electric plugs, door locks and cameras, printers and televisions, ceiling fans and window blinds. Robot vacuums, lawnmowers, and pool cleaners also log on. So do some refrigerators, toasters, and washing machines. And that’s just a sample. Together, this family of supposedly smart gadgets is sometimes referred to as the “internet of things,” or IoT. If you’ve got a thing that you interact with via an app, then it’s almost certainly online and chattering in the background.
IoT gadgets have always been insecure, both by design (to simplify operation or allow them to phone home to a manufacturer) and by habit (because normal people are not network administrators who can secure them). If compromised by network exploits, default-password intrusion, or other security vulnerabilities, these devices—these computers—can be harnessed by malicious parties from anywhere in the world to overwhelm a target with a distributed denial-of-service attack; to relay traffic for fraud or hacking; to collect data for sale, use, or ransom; or to spread malware that perform these and other malicious acts. Homes aren’t the only targets, either. Businesses are also at risk, because so many of their operations involve machinery that has been (foolishly, but understandably) connected to the internet. Just this week, hackers attacked hundreds of French supermarkets, spoiling millions of dollars worth of food by warming up refrigerators and freezers remotely.
None of this is new. But Anthropic, the company that makes Claude, published a blog post on Monday warning that artificial intelligence could make internet-of-things vulnerabilities worse. Ordinary malware follows the instructions its perpetrators wrote. That means it’s usually designed to exploit a specific vulnerability in a particular device—baby monitors, say—and then to use that vulnerability to run malicious software that does something specific, such as sending unwelcome traffic in a DDoS attack. AI introduces a new risk. Malware could install an AI model on many other nearby devices. Then a hostile program could, in effect, “chat” with that AI to carry out new kinds of pernicious acts. An Anthropic experiment successfully installed a small AI model across 15 Android TV boxes—the type of device that nearly every home contains, and that sends data to and from the internet constantly, even when the set appears to be turned off. Once the AI model is in place, malware programs with access could use it to help the malicious software gain access to even more devices, something that ordinary IoT exploits cannot do. That could help malware spread faster and more effectively.
[Peter Wehner: What if AI is the end of the world?]
The idea of a self-replicating AI botnet sounds profoundly dire, especially given the versions of that idea that philosophy and science fiction have imagined: Agent Smith copying himself over other people in The Matrix, the evolutionary swarm of autonomous machines in Stanisław Lew’s The Invincible, Nick Bostrom’s imagined machine so hell-bent on making paper clips that it extinguishes human life along the way. Recent worries about a hypothetically imminent AI-caused apocalypse have surely perked the public’s ear for anxiety about any type of wild AI fecundity. Not just We’re all gonna die, but A swarm of toasters and vacuum cleaners will be the agent of our doom.
But AI-faciliated IoT malware is concerning for the opposite reason: because it is so ordinary and mundane. The millions of small, internet-connected computers that malicious actors might exploit have remained at least somewhat resilient because those attackers usually need to target them explicitly. External changes, such as firmware updates and network alterations, can limit or eliminate the threats. If malware has access to the rough equivalent of a tiny ChatGPT, however, it might be able to make previously difficult or uneconomical attacks newly possible. In the blog post, Anthropic notes that newer devices with built-in AI chips, such as video-conferencing units and security cameras, might become particularly attractive targets, because their hardware makes running a hostile AI model easier. Beyond those obvious marks, previously hard-to-target, low-value computers—including budget televisions, digital picture frames, smart appliances, or older devices left unused but still connected to a network—might become newly penetrable.
The consequences of AI-accelerated malware remain more or less the same as those of the ordinary malware that is very likely running on your home network right now, without your knowledge. Instead of stealing your intimate photos or siphoning off your bank account, it mostly sends traffic to other networks, phones home with data to aggregate and sell, or routes traffic for shady activities to evade detection. That risk could expand should the AI-assisted spread that Anthropic predicts come to pass—a larger number of infected devices issuing greater disruption and facilitating more criminal activity and targeted exploitation.
The self-replicating AI botnet does not issue a substantially new kind of threat—at least for now. But sometimes a quantitative increase in a risk’s exposure can produce a qualitative change in that risk’s consequence. Take antibiotic resistance, for example, or wildfires. When a landscape accrues substantially greater dried materials that can fuel a fire, the same number of successful ignitions can spread much faster and more broadly. Climate change, human expansion into wildlands, and invasive-species infestations, among other factors, have increased both the chances for fire and its consequences. Exposure to wildfire risk becomes much greater than the simple presence of additional kindling might suggest. An AI-assisted IoT malicious-software inundation could increase the impact of malware substantially.
[Matteo Wong: How long until AI hacks everything?]
Even then, the outcome would be more witless and banal than the wildfire metaphor suggests. AI-assisted software hacks could, in theory, mess with the actual computers on a network, as in the French-supermarket attack. But instead of shutting off your home’s smart fridge, coordinated, global, AI-enabled malware is more likely to make networked-computing life harder and more miserable for everyone.
Email spam might offer a better metaphor for AI malware’s likely future. Junk email was annoying for years, but it remained manageable for a long time: You deleted the forwards from your weird uncle, and a simple spam filter caught much of the obvious junk. Eventually, though, email automation became cheap enough that it could both target enormous lists of addresses and personalize attacks, for phishing or exploitation. The low success rates of any single spam or phishing email ceased to be a problem once enormous mountains of junk email could be sent cheaply and easily. Now your inbox is ruined. Every email you receive from a stranger feels useless because you have begun to assume that email is all scams and garbage. (Phone calls and text messages are starting to feel the same way.)
AI malware could follow suit, adjusting probes for greater success, slowing down every computer and network by overwhelming it with garbage-processing and traffic, issuing a nuisance arms race of vulnerability-driven updates and patches, and perhaps even assembling a patchwork of previously valueless local-network data into quilts of persistent, individualized deception or harassment. A dumb risk with good odds, if low consequence, might prove to be more awful than a terrifying one that is highly unlikely.