Blockchain-assisted cyberattacks surge fivefold, driven by Iranian and North Korean state actors, Russia-linked groups — open-weight LLMs are linked to an increase in attacks | Tom's Hardware

The blockchain was going to make fraud impossible, because everything on it is immutable and public. Chainalysis has now totted up what immutable and public are actually being used for: a 440% rise in blockchain dead drop attacks, with daily malicious blockchain writes rising from 2.06 to 11.1. Eleven-ish writes a day sounds like a hobby until you clock that each one is a message parked in a permanent global ledger — a dead drop under a park bench, except the bench is on the internet forever and audited by strangers. Fivefold growth, says the headline. Nothing advertises a lowered technical barrier to entry quite like a crime wave that files its own paperwork in public.

The engine, per the report, is the widespread availability of Chinese open-source AI tools. Free weights, no licence worth reading, and a technical bar low enough that less-experienced attackers can now run complex campaigns they'd previously have needed a state payroll to attempt. This is the meaningful step change Anthropic flagged when it examined GLM-5.3, back when 'a meaningful step change in the cyber capabilities available to attackers' was a research note rather than a Chainalysis line item. Step change. Such a soothing pair of words. It sounds like a stairlift.

Doing the stepping: Iranian and North Korean state actors plus Russia-linked groups, which is the cyber equivalent of discovering the pub quiz is won every week by the same three blokes with a copy of the answer sheet. Chainalysis attributes the rise to AI itself. Not a leak, not a stray zero-day, not one unfortunate sysadmin with a Post-it — the general availability of the technology the industry spent two years calling democratising. Every safety framework drafted in the last eighteen months assumed attackers would find the door eventually. The door is now open-source, and the lock was never fitted.