Code hidden inside PC motherboards left millions of machines vulnerable to malicious updates, researchers revealed this week. Staff at security firm Eclypsium found code within hundreds of models of motherboards created by Taiwanese manufacturer Gigabyte that allowed an updater program to download and run another piece of software. While the system was intended to keep the motherboard updated, the researchers found that the mechanism was implemented insecurely, potentially allowing attackers to hijack the backdoor and install malware. Elsewhere, Moscow-based cybersecurity firm Kaspersky revealed that its staff had been targeted by newly discovered zero-click malware impacting iPhones. Victims were sent a malicious message, including an attachment, on Apple's iMessage. The attack automatically started exploiting multiple vulnerabilities to give the attackers access to devices, before the message deleted itself.

Early this year, the US Federal Trade Commission forced facial recognition startup Paravision to delete a collection of improperly obtained face photos and machine-learning algorithms trained with them. FTC Commissioner Rohit Chopra praised that new enforcement tactic as a way to force a company breaching data rules to "forfeit the fruits of its deception." Roth and collaborators from Penn, Harvard, and Stanford recently demonstrated a flaw in that approach, showing that the unlearning system would break down if submitted deletion requests came in a particular sequence, either through chance or from a malicious actor. It will take virtuoso technical work before tech companies can actually implement machine unlearning as a way to offer people more control over the algorithmic fate of their data. Binns says that while it can be genuinely useful, "in other cases it's more something a company does to show that it's innovating."

A nascent area of computer science dubbed machine unlearning seeks ways to induce selective amnesia in artificial intelligence software. "This research aims to find some middle ground," says Aaron Roth, a professor at the University of Pennsylvania who is working on machine unlearning. Early this year, the US Federal Trade Commission forced facial recognition startup Paravision to delete a collection of improperly obtained face photos and machine-learning algorithms trained with them. FTC commissioner Rohit Chopra praised that new enforcement tactic as a way to force a company breaching data rules to "forfeit the fruits of its deception." Roth and collaborators from Penn, Harvard, and Stanford recently demonstrated a flaw in that approach, showing that the unlearning system would break down if submitted deletion requests came in a particular sequence, either through chance or from a malicious actor.

The US Federal Trade Commission (FTC), Washington's consumer watchdog, has filed a lawsuit against two businesses it accuses of operating cryptocurrency pyramid schemes. The FTC is taking action against Bitcoin Funding Team and My7Network over what it defines as "chain referral" scams, in which participants pay upfront entry fees in order to be able to recommend others to follow suit. The companies allegedly promised customers who made an initial investment of just $100 (£71) that they could earn an $80,000 (£56,938) monthly income from doing so - although payouts seldom amounted to anything like that. The two businesses defrauded an estimated 30,000 people worldwide between them, the lawsuit alleges. "Bitcoin Funding Team's structure, which created a continual chain of recruitment and recruitment-related payments, ensured that few participants would obtain the results depicted or projected by the defendants," the FTC's complaint reads.