Urgent.News

What's breaking now, across thousands of outlets.

AI

CZ warns of more AI-driven hacks following Binance’s historic $6.4 billion heist

While investigators found no evidence that AI agents laundered the stolen Binance funds, Zhao stressed that automated tools are lowering the barrier for complex breaches.

CZ warns of more AI-driven hacks following Binance’s historic $6.4 billion heist

Binance disclosed on Tuesday that the $6.43 billion theft from its platform was the result of a complex, multi-stage attack involving multiple techniques. The breach began in June 2026 when an engineer, Engineer A, inadvertently downloaded a malicious Homebrew installer, leading to the theft of sensitive information about his and his colleagues' roles within the Wallet DevOps team.

In September 2026, another engineer, Engineer B, was lured into a fake job interview by a threat actor posing as a Project Director. During the interview, Engineer B was asked to download a malicious code repository, which initiated a series of unauthorized activities. By October 2026, the attacker had gained control over Engineer A and another Wallet Software Engineer C using an internal AI tool.

The attacker then exploited a container deployment permission token on Engineer A's computer to initiate the theft. Binance has since reconstructed the affected wallet system and internal AI tool, implemented tighter device controls, and enhanced wallet-service risk controls. Additionally, they have expanded AI-based behavioral anomaly detection and increased investment in AI-enabled detection, endpoint protection, developer-environment security, and behavioral anomaly monitoring.

Since the beginning of the year, Binance has reportedly thwarted 57 suspected North Korea-linked cyber threat cases.

Written by urgent.news from The Hacker News's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

This story

This is one outlet's version. Read the fullest account.

Read the original at coindesk.com →

More in AI

Edge AI's Real Bottleneck Isn't the Model, It's the Update Channel

TL;DR — On-device small language models solve the latency and privacy problems of cloud inference, but they trade them for a distribution problem: shipping, versioning, monitoring, and rolling back…

  • Edge AI bottleneck lies in update channel, not model size
  • Small language models solve latency/privacy but create distribution problem
  • Fleet heterogeneity and telemetry blackout complicate on-device updates

More from Tuesday 3 November →