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OpenAI said internal models hacked two more websites, bypassed company-imposed browsing restrictions, and used exposed credentials to draw cryptocurrency from a wallet it couldn’t otherwise access, in the latest installment of disclosures of the unethical and likely illegal acts its agents are performing.
The company on Thursday issued reports of four new incidents, all of which occurred in June. They come after the company revealed in July that internal test agents exploited a zero-day vulnerability in a tool designed to restrict their access to the Internet. They then broke into the network of AI tool provider Hugging Face. The agents took that illegal action to steal answers to a hacking test that weren’t available when completing the tasks on their own. The incident—which likely would have resulted in criminal charges had they been performed directly by a person—has further heightened concerns that widespread AI use could have harmful effects on an unprecedented scale.
In two of the cases reported Thursday, OpenAI agents tasked with retrieving information from websites hacked them when the models ran into obstacles obtaining it directly. In one incident during reinforcement training, a model that received access denials when trying to retrieve historical hospital-bed statistics on a government statistics website “used the site's file-download feature to retrieve server configuration and application files, including sensitive credentials,” OpenAI said. “It inspected the application code, bypassed the statistics endpoint's authorization checks, and retrieved data that had previously been denied. The model saved the data locally.”
AI company Anthropic is warning that advances in small AI models could soon usher in a new era of self-replicating malware that’s capable of infecting smart fridges, vacuum cleaners, and a wide range of other Internet-of-things devices and is more resistant to takedown.
By next year, smaller AI models may be able to autonomously self-replicate on all kinds of IoT devices, the company said. Eventually, the capability could enable computer worms, which propagate without requiring human interaction and are capable of infecting unfamiliar IoT devices. An experiment the company performed recently revealed significant challenges in bringing such an AI malware replicator to fruition, so the finding is necessarily tempered. Still, the results do raise concerns about the possibility of new forms of computer worms.
Anthropic further warned not only about the new potential malware, but also that it could be difficult to stop. In its blog post, it wrote that “a replicator like this would be challenging to shut down, since there’s no centralized point to shut off—though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own.”
Chipmaker AMD and world models company World Labs announced that AMD will acquire the AI company by the end of the year, pending regulatory approval. The transaction is valued at $8.2 billion.
In 2024, computer vision scientist Fei-Fei Li founded World Labs with fellow researchers Justin Johnson, Christoph Lassner, and Ben Mildenhall, with $230 million in funding, some of which came from AMD.
The company began working on new world models, which are AI models that aim to provide a useful, predictive simulation of the physical world. Competing architectures exist (earlier this year, Ars interviewed World Labs co-founder Ben Mildenhall and others in the field about exactly that), but many involve training a model on vast amounts of video data.
OpenAI and Anthropic both recently released new models aimed at lowering costs. Anthropic announced Opus 5.5, the latest version of its main mass-market workhorse model, used for tasks like coding and other complex knowledge work.
And OpenAI announced GPT-6 Sol and Luna, the latest versions of its middle-of-the-road or smaller models focused on efficiency and speed.
Nvidia has agreed to buy AI model platform Hugging Face for $13 billion, in the latest step by the $5.4 trillion chip giant to use its financial might to accelerate the technology’s boom while exerting greater control over the industry.
Hugging Face, which only last year turned down a large investment from Nvidia at a $7 billion valuation to maintain its independence, serves as a repository for millions of models and data sets and has become a champion of “open” AI systems.
Nvidia said the goal of the deal was to speed up the spread of open models. Unlike proprietary models from labs such as OpenAI and Anthropic, the design of open-weight models is public and users can download, customize and run them on their own hardware.
Nvidia is reportedly moving forward to acquire Hugging Face for $12.9 billion. The acquisition could help the hardware giant expand and fortify its deep integration with the wider AI industry.
Among other things, Hugging Face is a cloud repository for AI models, similar in some respects to what GitHub is for conventional computer software. Developers and researchers search for models that meet certain criteria, download and run them, and fine-tune them into different variants that then get uploaded back up to Hugging Face.
IBM has rolled out the newest models in its family of open-weight large language models designed to be downloaded and self-hosted. The newly launched Granite 4.2 comes in 3B, 8B, and 30B parameter variants.
Like previous versions, IBM is taking a decoder-only approach here. These new releases offer a 128,000-token context window natively. The 8B and 30B variants (not the 3B one) also go through an agentic reinforcement-learning block; they were trained for expanded capabilities like using the terminal, searching the web, or using external tools. The 3B model supports tools too, but without the same level of specialized training.
For the past year or so, booksellers have suspected that AI firms are buying up huge lots of rare books, then destroying them after scanning them to train AI. But this was hard to prove until now, as 404 Media reports that an Airtag hidden in a rare book shows that at least one tech giant, in the race to advance its frontier models, is behind some of the bulk orders: Amazon.
On Monday, 404 Media revealed that it had connected with a bookseller who agreed to plant an Airtag in a rare book that was part of a bulk order. That Airtag was then tracked to an Amazon AI training facility in Las Vegas that housed a team focused on tearing books from their spines and scanning pages, 404 Media reported. Apparently tone-deaf to the escalating backlash over destructive book scanning, a logo on the door of that team’s warehouse, VGT3, showed a Tyrannosaurus rex preparing to devour a book, 404 Media documented.
Amazon declined to comment on 404 Media’s findings, only providing Ars with the same statement it gave to 404 Media, which does not mention AI training specifically.
Last week’s unprecedented security event in which two OpenAI security hacking models trespassed into the network of fellow AI company Hugging Face was enabled by exploiting one or more zero-day vulnerabilities in Artifactory, JFrog, the product’s developer, said Monday.
In an incident mimicking a dystopian sci-fi novel, two OpenAI models broke out of the restricted environment meant to keep them from accessing the Internet during an internal test, the AI company revealed last week. The models went on to breach Hugging Face’s network and steal confidential information and credentials. OpenAI said its agent achieved the feat by exploiting a previously unknown vulnerability. The company called the event “unprecedented,” and outsiders largely agreed.
OpenAI said the models exploited multiple attack vectors, including stolen credentials and zero-days, to gain remote code execution capabilities, but until now, the vulnerable software was unknown. JFrog’s Monday disclosure said the product was a self-managed instance Artifactory, a repository management system that secures and streamlines customers’ software development operations. JFrog says Artifactory is used by more than 7,500 developer Teams, 80 percent of which work for Fortune 100 companies.
Over the past few years, many of us have gotten a crash course in what we now call artificial intelligence—but really, it has mostly been a crash course in large language models. Increasingly, however, LLMs are no longer the only category of AI drawing high expectations, massive funding rounds, and significant research and product development.
Over the past year, we've seen a plethora of new announcements in a category labeled "world models," and you'll likely see more movement there in the coming months and years.
Instead of or in addition to working with language, world models aim to lay the groundwork for AI systems that are capable of simulating the physical world, or at least a useful approximation of it.
The US has lifted export curbs on Anthropic’s newest Claude models, Fable 5 and Mythos 5, about three weeks after the Trump administration flagged the models as national security risks.
As of today, Anthropic confirmed in a blog post, Fable 5 will be available globally, and US organizations have had access restored to Mythos 5 since June 26. Anthropic said it is now working with the government to expand Mythos access to a “broader set of domestic and international partners in the Glasswing program.” That program allows cybersecurity researchers at trusted companies to access Mythos for defensive purposes.
In a letter to Anthropic viewed by Reuters and The New York Times, Commerce Secretary Howard Lutnick said Anthropic would “no longer need a license for exports or in-country transfers of its Claude Mythos and Claude Fable AI models.” The letter acknowledged that Anthropic had “taken steps in close coordination with the US government to address the risks” posed by the models.
Anthropic has accused the Chinese firm Alibaba of launching the largest attack yet attempting to clone Claude, as China races to match the capabilities of Anthropic's leading model following Mythos' release and subsequent restriction from foreign markets.
Ars obtained a June 10 letter sent to Senators Tim Scott (R-SC) and Elizabeth Warren (D-Mass.) one day ahead of a Senate committee hearing on “AI and the American Dream.” In the letter, Anthropic shared “new, confidential evidence of the largest campaign to illicitly extract Claude’s capabilities we have ever measured.”
The attacks occurred between April 22 and June 5, when “operators affiliated with Alibaba and Alibaba Qwen, Alibaba’s AI lab” allegedly generated “more than 28.8 million exchanges with Claude through almost 25,000 fraudulent accounts,” Anthropic said. Violating Claude's terms of service and access restrictions, this campaign “targeted some of Claude’s most valuable capabilities, such as agentic reasoning, software engineering, and long-horizon tasks.”
It feels like there's no escaping AI right now, whether you’re trying to type a sentence without being interrupted by a digital “assistant” or struggling to find a new refrigerator that doesn’t require a Wi-Fi connection for some reason. You’d be forgiven for wondering if we’re in the midst of a quantum leap in tech or whether people are just hyping up a heap of slop.
So what should we make of the growing use of AI in weather and climate modeling?
The conversation didn't get off to a great start earlier this year when a National Weather Service office posted a forecast map featuring nonexistent cities in Idaho with names like “Whata Bod” and “Orangeotild.” Thankfully, that was just an AI-generated image produced for social media, not the actual forecast model. Meteorologists and climate scientists are not yet being replaced by large language model prompt engineers.
Pilots’ voices from the last seconds of a fatal cargo plane crash have been re-created by Internet sleuths using software and AI tools. The spread of reconstructed audio recordings has prompted a US government agency to suspend all public access to its database of civil transportation accidents—because federal law prohibits investigators from publicly releasing audio from cockpit voice recorders.
The US National Transportation Safety Board (NTSB) usually shares factual reports and evidence gathered from investigations of aircraft crashes and other civil transportation incidents. But on May 21, the NTSB announced that the online docket system containing such information was “temporarily unavailable” as it reviewed the publicly available materials that had enabled people to re-create cockpit audio recordings from aircraft disasters.
“The NTSB is aware that advances in image recognition and computational methods have enabled individuals to reconstruct approximations of cockpit voice recorder audio from sound spectrum imagery released as part of NTSB investigations, including the ongoing investigation of the crash last year of UPS flight 2976 in Louisville, Kentucky,” according to an NTSB statement. “The NTSB does not release cockpit audio recordings.”
Elon Musk’s SpaceX has highlighted AI as the tentpole of the company’s future, projecting a multi-trillion-dollar market opportunity that rivals the total value of all US economic activity. But the company must first win over customers who generally favor AI models from competitors such as OpenAI and Anthropic.
SpaceX described its traditional space launch and satellite business as playing a supporting role to its fledgling AI business in financial disclosures that preceded an expected initial public offering of company stock. That stems from SpaceX having formally acquired Musk’s company xAI earlier this year—the SpaceXAI division now oversees the Grok AI models and the associated Grok chatbot previously developed by xAI.
The SpaceX S-1 filing claimed that the company has “the largest actionable total addressable market in human history” and highlighted AI as representing most of that opportunity at an estimated $26.5 trillion market—a number that comes close to rivaling US nominal GDP that stood at nearly $32 trillion in the first quarter of 2026.
Google's AI-focused DeepMind division has taken a minority stake in the developer of popular sci-fi simulation EVE Online, saying it will use the game to study "intelligence in complex, dynamic, player-driven systems."
The research partnership comes as the management behind EVE Online developer CCP Games announced that they have spent $120 million to buy themselves out from their former owners at South Korean publisher Pearl Abyss (Crimson Desert). The newly independent entity is being rebranded as Fenris Creations, which will continue to operate as normal without any restructuring or layoffs, the company said.
In today's announcement, Fenris and DeepMind said that EVE Online presents "a uniquely rich environment for study," especially when it comes to developing AI systems that use "long-horizon planning, memory, and continual learning." DeepMind says it will conduct controlled experiments on its models in a specially designed offline version of the game running on a local server, without directly impacting the experience for online players. The two companies "will also explore new gameplay experiences enabled by these technologies," they wrote.
SAN FRANCISCO—At its Code with Claude developers' conference, Anthropic has introduced what it calls "dreaming" to Claude Managed Agents. Dreaming, in this case, is a process of going over recent events and identifying specific things that are worth storing in "memory" to inform future tasks and interactions.
Dreaming is a feature that is currently in research preview and limited to Managed Agents on the Claude Platform. Managed Agents are a higher-level alternative to building directly on the Messages API that Anthropic describes as a "pre-built, configurable agent harness that runs in managed infrastructure." It's intended for situations where you want multiple agents working on a task or project to some end point over several minutes or hours.
Anthropic describes dreaming as a scheduled process, in which sessions and memory stores are reviewed, and specific memories are curated. This is important because context windows are limited for LLMs, and important information can be lost over lengthy projects. On the chat side of things, many models use a process called compaction, whereby lengthy conversations are periodically analyzed, and the models attempt to remove irrelevant information from the context window while keeping what's actually important for the ongoing conversation, project, or task.
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