• 15,500 domains were actively used to deliver cloaked AI investment scams
• Cloaking ensures harmful content is shown only to targeted victims
• Commercial tracking software allows cybercriminals to scale operations without building infrastructure

Cloaking has shifted from a supporting tactic into a central layer of cybercriminal infrastructure, and commercial tools are now widely embedded in cybercrime operations at scale.

A four-month analysis of malicious activity by Infoblox and Confiant identified roughly 15,500 domains linked to malicious tracker deployments.

Billionaire media mogul Barry Diller doesn’t think OpenAI CEO Sam Altman is untrustworthy, despite recent reporting to the contrary. Onstage at The Wall Street Journal’s “Future of Everything” conference this week, Diller vouched for the AI exec, who has been accused by some former colleagues and board members of being manipulative and deceptive at times.

Diller, who is friendly with Altman, was responding to a question about whether or not people should put their faith in Altman to ensure that artificial intelligence benefits humanity.

In particular, he was asked about the theoretical form of AI known as artificial general intelligence, or AGI, which could one day outperform humans on any task.

The media exec, a co-founder of Fox Broadcasting and chairman of IAC and Expedia Group, said that while he believes Altman is sincere in his pursuits, that’s not really the area of concern people should be focused on. Rather, it’s the unknown consequences that will result from AI.

“One of the big issues with AI is it goes way beyond trust,” Diller said. “It may be that trust is irrelevant because the things that are happening are a surprise to the people who are making those things happen. And I’ve spent a lot of time with various people who’ve been in the creation mode of AI, and they have a sense of wonder themselves. So…it’s the great unknown. We don’t know. They don’t know,” he explained.

“We have embarked on something that is going to change almost everything. It is not under-reported. Now, whether these huge investments are going to come through — I couldn’t care less. I’m not invested in it, but progress is going to be made,” Diller added.

Still, the media mogul said he believes that most of the people leading the charge are good stewards, saying he believes that Altman is sincere and “a decent person with good values.” (Diller wouldn’t say which of the AI leaders he thinks is insincere, we should note.)

“But the issue is not their stewardship. The issue is … it’s dealing truly with the unknown. They don’t know what can happen once you get AGI, and we’re close to it. We’re not there yet, but we’re getting closer and closer, quicker and quicker. And we must think about guardrails,” Diller noted.

Plus, he warned, if humans don’t think about guardrails, then the alternative is that “another force, an AGI force, will do it themselves. And once that happens, once you unleash that, there’s no going back,” Diller said.

Meta is beefing up its age-verification mechanisms with an AI system that analyzes images and videos on Instagram and Facebook for “visual cues,” such as height and bone structure, to identify and delete accounts of users under the age of 13. The company announced the move amid a wave of cases in which hundreds of children have managed to evade social network access restrictions, even through simple tricks such as drawing on a mustache.

The new approach is part of a series of measures Meta adopted as part of an AI-based security strategy designed to correct the limitations of traditional methods, which rely heavily on self-reported age. With this change, the company seeks to reduce the ease with which minors access platforms that, in theory, are restricted to them.

In a press release, Meta explained that it is implementing several tools to identify contextual indicators that allow estimating a person’s age. This process includes the analysis of posts, comments, bios, and descriptions, with special attention to references related to school years or birthday celebrations—elements that can offer clues about the real age of the person who manages the account.

These tools are in addition to automated analysis techniques aimed at detecting physical traits from imagery shared to Meta’s social platforms. These include characteristics such as height and bone structure. Meta is careful to stipulate that this system is not face recognition, as it does not seek to identify specific individuals in images or videos. Instead, the company notes that, “by combining these visual insights with our analysis of text and interactions, we can significantly increase the number of underage accounts we identify and remove.”

If, based on these elements, Meta suspects that an account is managed by a child under 13, it will be suspended. The user will have to revalidate their age using the procedures established by the company to regain access; otherwise, the profile will be permanently deleted.

Meta also announced that it will expand the scope of its technology to detect users between the ages of 13 and 15 and automatically assign them teen accounts. This type of profile incorporates content restrictions and parental controls enabled by default, with the aim of providing a safer environment for this age group.

Meta began implementing age-verification tech in 2024 for Instagram users in the United States, Australia, Canada, and the United Kingdom. Now, the mechanism will be extended to Instagram accounts in Brazil and 27 European Union countries. In addition, these practices will be applied for the first time to Facebook users in the US, with plans to expand to the EU and UK next month.

Looking All Grown-Up

The new measures have been interpreted as a response to a preliminary ruling recently issued by the European Commission, which concluded that the company led by Mark Zuckerberg is in breach of the Digital Services Act for allegedly failing to effectively prevent children under 13 from using its platforms. The EU body found that the company lacks sufficiently effective mechanisms to block such access and that its current systems for identifying and suspending accounts below the age threshold are insufficient.

These criticisms are supported by the results of a survey conducted by the nonprofit Internet Matters. After surveying nearly 1,300 children and their parents in the UK, the study revealed that approximately one-third of children have successfully evaded government-imposed restrictions on access to social networking sites. In some cases, the methods employed are particularly striking.

The report, titled “The Online Safety Act: Are Children Safe Online?” showed that 46 percent of 9- to 16-year-olds believe that circumventing age controls is very easy. In total, however, only 32 percent admitted to breaking the rules.

A few months before Elon Musk left OpenAI’s board of directors in February 2018, he tried to recruit Sam Altman to join a “world-class AI lab” within Tesla. Musk went as far as offering the OpenAI CEO a Tesla board seat, according to emails and testimony presented in federal court on Wednesday during the Musk v. Altman trial. The emails were shown to a jury during the cross examination of Shivon Zilis, a former OpenAI adviser and board member who is also the mother of four of Musk’s children.

Musk’s core claim in this lawsuit is that Altman and OpenAI president Greg Brockman effectively stole a nonprofit, using the $38 million Musk invested to create a private company worth more than $800 billion today. On Wednesday, lawyers for Musk showed video depositions of former OpenAI CTO Mira Murati and former OpenAI board member Helen Toner, to raise concerns over Altman’s alleged history of deceit.

OpenAI’s legal team has responded to Musk’s claims by questioning his true motives, arguing that the Tesla CEO has had “sour grapes” ever since he failed to assume control of OpenAI in 2017. He has since started a rival, for-profit AI lab. OpenAI’s lawyers used Zilis’ cross-examination on Wednesday to bring up evidence about Musk’s alleged plans to subvert OpenAI, and tried to suggest Zilis was privy to those plans. As it pertains to this case, one of Zilis’ most important roles at OpenAI was acting as a conduit between Musk and Altman.

In a text from February 2018 presented as evidence, Zilis—then an OpenAI adviser, as well as a Neuralink and Tesla executive—asked Altman, “Did you think through a B Corp subsidiary of Tesla?”

“There was documentary evidence that, at several points, Mr. Musk had contemplated seeking to join Sam Altman to the board and offered that option,” said OpenAI lawyer William Savitt outside the courthouse on Wednesday. “It was part of Mr. Musk’s effort to corrupt OpenAI and absorb it into Tesla … he was trying to get Altman to abandon the mission and be part of Tesla.”

In an email to Tesla’s VP of communications, Sarah O’Brien, from November 2017, Zilis shared a draft of an FAQ page about an event Tesla was planning to hold at the NeurIPS AI conference. “The purpose of this event is to share that Tesla is building a world leading AI lab(?) which will rival the likes of Google / DeepMind and Facebook AI Research,” the drafted FAQ read. The document continues, “One major issue for Tesla is when people think of Elon and AI, they think of OpenAI.”

Another part of the FAQ labeled “Who?” lists several Tesla executives who were planned to lead the unit, including Musk and Andrej Karpathy, a former OpenAI researcher. Altman’s name is listed next to Musk’s with two question marks beside it.

The FAQ is marked up with notes including that Altman could be a moderator for the NeurIPS event, which “could be a forcing function for Sam to commit to TeslaAI.” Another note reads that Tesla AI’s “strategy had yet to be defined and some of it may be deeply proprietary.”

Zilis testified on Wednesday that Altman never ended up joining Tesla, and the AI lab and the NeurIPS launch event never came to fruition. She also testified that Musk reached out to Karpathy about recruiting him to Tesla. Savitt told reporters that Zilis’ testimony on Karpathy is “directly contrary to what Mr. Musk told the jury just a few days ago.” Earlier in this trial, Musk testified that Karpathy left OpenAI of his own volition.

AI

For AI agents, seeing is expensive

Businesses deploying AI agents to automate computer usage may be spending far more money than necessary if those agents try to emulate human visual interaction.

Reflex, an enterprise application platform, recently set out to compare vision agents with API agents.

A vision agent in this context refers to an AI agent that mimics human interaction by relying on image processing and optical character recognition to operate an application. In this instance, that’s Claude Sonnet navigating a web app user interface via browser-use 0.12, a tool for automated web browser operation.

An API agent here refers to Claude Sonnet interacting with a web app via tools and APIs. The agent calls the same handling mechanisms that the UI calls and receives structured data in response, rather than a web page screenshot that must be analyzed.

“Two agents target the same running app: one drives the UI via screenshots and clicks, the other calls the app’s HTTP endpoints directly,” explained Palash Awasthi, head of growth at Reflex, in a blog post. “Same Claude Sonnet, same pinned dataset, same task. The interface is the only variable.”

The following task was presented to each agent: “A customer named Smith has complained about a recent order. Find the Smith with the most orders, accept all their pending reviews, and mark their most-recent ordered order as delivered.”

According to Awasthi, the API agent completed the task in just eight calls. It listed pending customer reviews, accepted them, and marked the order delivered. 

The vision agent, however, found only one of four pending reviews because it failed to scroll the page where it would have seen the three other reviews hidden off-screen.

Analyzing and interpreting a web page visually is fundamentally more challenging for an AI model than interacting with API calls and tools.

When the prompt was revised to help the vision model perform better, the vision agent still took ~17 minutes, significantly longer than the API agent at ~20 seconds. The vision agent also consumed a lot more tokens – ~45x more.

The company made its test available as a benchmark for those interested in trying to reproduce the results.

Awasthi said that the cost difference between the two approaches reflects the architecture – vision agents need to see and seeing is costly – each screenshot demands thousands of input tokens to process.

Anthropic estimates that processing a 1000×1000-pixel image with Claude Sonnet 4.6 uses about 1,334 tokens. 

The vision agent expended around 500,000 input tokens and around 38,000 output tokens to complete its task. The API agent used around 12,150 input tokens and around 934 output tokens.

For Awasthi, the lesson is that while vision agents may be necessary for interacting with apps you don’t control, inwardly focused agents should target APIs. ®

OpenAI’s rumoured smartphone might be arriving earlier than anyone expected.

According to analyst Ming-Chi Kuo, the company is now aiming to mass-produce its first “AI agent phone” in the first half of 2027. This is a significant shift from earlier timelines, with earlier estimates pointing closer to 2028.

The accelerated push suggests OpenAI is getting more serious about hardware, and quickly. Kuo says the move is partly tied to a potential IPO. A flagship device could help strengthen its pitch to investors. Additionally, there is growing competition in the emerging “AI phone” space.

Details are still thin, but the early picture is interesting. The device will run on a custom chip based on MediaTek’s Dimensity 9600, potentially built on TSMC’s next-gen process. It could also feature dual AI processors. These processors would handle things like vision and language tasks simultaneously. This is a hint at how OpenAI wants this to function less like a traditional smartphone and more like an always-on assistant.