Digital Bumrush
Consider a scenario of a small group of defenders beset by overwhelming numbers. The cause looks hopeless, the attackers’ objective being to either starve them out or break through the defenses just one time to open the way for all out assault. It’s the setup of numerous action movies and fantasy stories. And in those stories, some fateful twist or last minute ingenuity saves the day and the defenders will win (or stalemate) somehow. Maybe reinforcements arrive. Maybe the walls hold and attrition whittles down the numbers. Maybe there’s mutually assured destruction. It’s a popular trope, and off the top of my head I can think of half a dozen movies that use it, with varying degrees of victory or lack thereof, for the hapless defenders.
We often say that art imitates life. But more often lately, life is imitating art. APT36, the Pakistan based threat actor also known as Transparent Tribe, has been using a campaign of AI-generated, middling to low quality but enormous quantity of malware attacks. Dubbed as ‘vibeware’, the emerging new family utilizes niche code languages like Nim, Zig, or Crystal to in essence copy the logic of more known languages like C and Go. And then a target is flooded with ‘disposable’ attacks in the hopes that one will slip past security simply through sheer volume. It’s less of an assault on defense as it is a tactic to overwhelm analysts trying to keep up with the noise. Bitedefender’s Radu Tudorica describes it as a distributed denial-of-detection.
The tactic takes advantage of something I’ve been concerned about for a while: the difference between human and machine speed. The trouble with AI-generated malware isn’t that it’s particularly sophisticated or innovative (although some of them are), it’s that it can evolve and shift at a rate that’s just impossible to keep up with for the real people trying to hold it at bay. Automation doesn’t suffer from fatigue or need lunch and bathroom breaks. In the hours when analysts are clocked out, the machine is still churning at scale. Regardless of my personal feelings on LLM’s, AI security is probably the best tool we have to fight against AI malware.
Vibeware often dead ends itself. Many versions of the generated code simply collapse under their own misconfigured or poorly written commands. But that isn’t the point of the attack strategy. Those sloppy, disposable bits are a feint. They make a lot of noise, filling up the logs with more traffic than one can reasonably be expected to sift through, and distract from the piece actually carrying a payload that’s now slipped by an exhausted triage analyst while they were busy tracking everything else. And even if the payload carrying malware is caught, there’s another one somewhere else also carrying a payload to another endpoint. All it takes is one successful incursion to become compromised; vibeware usually has several options, all aimed at a different aspect of the target, whether that’s the cloud services, the messaging apps, or the productivity ones.
So how do we stop it? ReversingLabs has also published a summary of vibeware, and quoted many experts in the security industry. Among them, Jason Soroko, a senior fellow at Sectigo, says that defending against automated malware is less about reacting to how it’s constructed and more about what it’s doing. Strictly enforcing zero-trust compliance to contain unauthorized outbound communication will cut down on the amount of ‘noise’ in the first place. Martin Zugec of Bitedefender says that organizations that have neglected their security hygiene are now going to have to play catch-up. Employing network segmentation, the principle of least privilege, and active endpoint monitoring, and making the environment hostile and unpredictable to attackers, he adds.
Ya know, the basics. I don’t what’s more astounding to me, that someone has figured out how to create digital siege engines or that we’re still having this conversation about baseline security in 2026. Either way, I expect this isn’t the only time I’ll be reporting on it.
Posted, 4/17/26
