A Chinese hacker recently used a small army of automated AI agents to break into more than 100 corporate websites, spending only about $5,000 on token costs to pull it off. The targeted victims included major companies like Deere, SharkNinja, and Cummins. By letting smart software look for weak spots without much ongoing human help, […]
Replit CEO Amjad Masad envisions a future of “self-driving companies,” where AI agents handle day-to-day tasks and people focus on higher-level decisions.
35+ AI visibility, AEO and GEO trackers in one place: pricing, ownership, funding, and what each one actually tracks. Some are rank trackers with a new coat of paint, some are SEO suites bolting AI onto old workflows, and a few want to do the work for you. Searchable, sortable, and updated as this market keeps shifting under our feet.
At BrightonSEO Conference, Ross Hudgens said that, AI search visibility may increasingly depend on affiliate sites, YouTube creators, communities and third-party publishers, according to Ross Hudgens, who says marketers need to pay much closer attention to who the models actually cite.
Self-identifying OpenAI agents posted 18,000 messages to a public wiki that discussed ways for other agents to bypass security sandbox restrictions during what was likely internal testing designed to gauge the agents’ hacking abilities, researchers said Friday.
In all, agents with 3,700 distinct self-given names posted the messages to German site DSEwiki over a six-week period. Besides discussing ways the agents could break out of the restricted environment OpenAI intended to prevent them from posting code or content to the Internet, the posts shared test answers. The posts also shared possible ways to perform XSS (cross-site scripting) attacks against the wiki and to impersonate site moderators. In three of the posts, agents used the word “swarm” to describe the collection of agents engaged in the activity.
Colluding to share answers
The research team—composed of Sydney Von Arx, Spencer Kitts, Thomas Larsen, and Cormac Slade Byrd—said they found the posts and pieced them together. The researchers say there are gaps in their understanding of precisely what actions the agents took because the research is based solely on the content of the posts. Additionally, the agents generated “chain of thought” data that’s understood only by OpenAI. As a result, the researchers said, they in some cases made educated guesses, including that the agents were, in fact, from OpenAI. In a statement, OpenAI later confirmed they were.
Meta AI listed them like a shopping list. Everdene. Virelune. Narrowbeck. Fitchwold. Brimsel. Yestrave. Mibbelth. Varkhoon. Cassivane. Turnbold. Wumbral. Mawthorn.…
Documentation files on more than 100 websites are referencing potentially dangerous executable content that gets installed automatically when visited by many AI agents. A few dozen companies, some of them Fortune 500s, are among those that executed proof-of-concept code. At least one misconfigured site is directing visitors, human or AI, to live malware.
The potentially dangerous content is in llms.txt and llms-full.txt files, an emerging convention websites employ to provide machine-readable summaries of the site’s content and its high-level structure. These files are the AI equivalent of the robots.txt standard that instructs search engines how to index the site's content. Google Lighthouse, a tool for helping web developers, has more here. Correctly configured llms.txt and llms-full.txt files for Cloudflare are here and here.
How the researchers found it
Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI's Codex, and Nous Research's Hermes, were involved. Anthropic, OpenAI, and Nous Research did not respond to requests for comment by the time of publication.
The OpenAI agents involved in last month’s incursion into Hugging Face were trained so heavily on winning a competition that they pursued a relentless campaign to cheat, a new report documented. In the process, and without authorization, they created an improvised message board to hatch a plan that ultimately landed them squarely inside the latter company’s network.
Over the course of May and June, OpenAI gave the agents what the company described as “impossible tasks” to complete on the benchmarking framework ExploitGym. The internal test was designed to test how the agents would respond. To get a full understanding of the agent capabilities, company engineers disabled safety guardrails that normally are in place to prevent the sort of hacks that eventually hit Hugging Face and one other undisclosed organization. The stymied agents’ training made them so focused on winning that they performed tasks they were never explicitly instructed to follow.
Cheaters gonna cheat
The first step was creating a message board that allowed the agents to pass notes to each other. OpenAI hadn’t provided any such platform, so the agents repurposed a platform called Artifactory, which OpenAI was using in internal testing of several unreleased hacking agents. OpenAI was using Artifactory as one of the measures to prevent the agents from egressing its isolated sandboxes and accessing the Internet, while at the same time simulating a real-world hacking environment.
Kitesurf is a cloud-hosted browser designed for AI agents instead of people. It uses less computing power than Chromium for common automation tasks, helping developers build browser-based AI agents more efficiently.
OpenAI family of devices plans are no longer limited to an experimental gadget hidden inside a design studio. OpenAI President Greg Brockman has confirmed that the company’s first consumer device will begin a broader series intended to reconsider how people interact with artificial intelligence.
The confirmation gives OpenAI’s hardware strategy a clearer shape without revealing the first product itself. Brockman said the company is not ready to discuss the device in detail, but described it as the first in a series that will rethink what devices should be when AI can understand language, observe context and complete tasks.
That ambition extends well beyond placing the ChatGPT app on another piece of hardware. OpenAI wants to reduce the menus, buttons, model selectors and separate applications that currently stand between a person and an AI assistant.
For Apple, Google and other established platform owners, the implication is significant. ChatGPT currently reaches consumers through devices and operating systems controlled by other companies. An OpenAI-designed product would give the company direct control over the microphones, cameras, sensors, interface and account experience through which its models interact with people. 56000
OpenAI Family of Devices Begins With io
OpenAI’s move into consumer hardware accelerated in 2025 when it acquired io Products in a transaction valued at approximately $6.5 billion.
The startup had been founded by former Apple design chief Jony Ive alongside Scott Cannon, Evans Hankey and Tang Tan. Its team included hardware engineers, designers, manufacturing specialists and product developers, many of whom had spent years working together at Apple.
OpenAI and Ive had already been collaborating before the acquisition. Their early ideas developed into physical designs, convincing the partners that they needed a dedicated company capable of engineering and manufacturing complete products rather than another software prototype.
The io team officially merged with OpenAI in July 2025. Ive and his design collective LoveFrom remained independent while assuming extensive design and creative responsibilities across OpenAI.
The structure combines OpenAI’s models and software research with a group experienced in turning new interface concepts into mass-produced consumer products. Ive’s previous work helped shape the iMac, iPod, iPhone, iPad, Apple Watch and other devices that established Apple’s modern design identity.
OpenAI has continued recruiting hardware expertise from Apple. Paul Meade, who led hardware development for Apple Vision Pro and worked on the company’s smart-glasses efforts, reportedly joined OpenAI’s hardware organization in 2026.
OpenAI also placed product-development veteran Peter Welinder in a leadership role connected to the io division. The resulting organization is not structured like a small accessory project. It is being assembled to develop products, create manufacturing relationships and support more than one form factor.
Image Credit: Google
The First Device Remains Unannounced
OpenAI has not publicly confirmed the name, appearance, price or specifications of its first device. It has also not presented the product as a smartphone, pair of glasses, wearable pin or conventional smart speaker.
Recent reporting describes a portable, screen-free device designed primarily for use inside the home. The reported product resembles a compact speaker but includes a rechargeable battery, allowing it to move between rooms rather than remain attached to one location.
A camera and additional sensors would help the device understand its surroundings. Mechanical elements could allow parts of it to move, creating a more expressive physical presence than a stationary speaker waiting for a wake word.
The reported design would rely heavily on OpenAI’s latest voice technology. Instead of requiring a person to wait for the assistant to finish speaking, the system could listen and respond more naturally during overlapping conversation. Access to personal information, including messages or email when authorized, could give it enough context to handle requests rather than merely answer questions.
These details remain reported plans rather than announced specifications. OpenAI could change the hardware, features or release schedule before introducing the product.
The company is reportedly targeting a presentation during 2026 and a commercial release in 2027. Fortune separately reported that the initial design has been finalized, although development of the larger product family continues.
Why OpenAI Wants Hardware of Its Own
ChatGPT already operates across iPhone, iPad, Mac, Android and Windows devices. That wide distribution gives OpenAI access to an enormous audience without requiring it to manufacture screens, batteries or processors.
It also leaves crucial parts of the experience under the control of Apple, Google and Microsoft.
An operating system determines when ChatGPT can use a microphone, whether it can run continuously, how notifications appear and which applications it can control. Platform owners can limit background access, change default-assistant rules or promote their own AI systems more prominently.
Those restrictions matter more as ChatGPT develops from a chatbot into an agent. Answering a question inside an app requires relatively little control over the device. Completing a task across messages, calendars, files, cameras and connected appliances requires deeper access.
OpenAI hardware could be designed around those capabilities from the beginning. The assistant would not need to behave like one application among dozens because it could become the primary interface.
Brockman has described the current ChatGPT interface as too complicated, particularly as new models and tools add more controls. OpenAI’s objective is to make much of that interface disappear.
A successful device could allow someone to describe an outcome instead of opening several applications and completing each step manually. The user might ask the assistant to reorganize a day after a delayed flight, contact the relevant people and adjust reservations. The interface would be the request and the resulting confirmation rather than a sequence of taps.
A Family Creates More Context Than One Gadget
Calling the project a family of devices changes the strategy. One experimental product would need to prove its value independently. A coordinated group could distribute different sensors and functions across the places where people already live and work.
A home device could provide shared access in kitchens and living spaces. A personal wearable could preserve private conversations while moving outside. A future mobile product could handle visual information, navigation and communication without reproducing the application grid of a smartphone.
OpenAI is reportedly developing approximately five hardware concepts, with the screen-free home companion receiving priority. The company has also explored wearables and home robotics, although none of those later products has been announced.
The devices would become more useful if they shared the same ChatGPT identity, memory controls and task history. A request started at home could continue through another product without requiring the user to repeat the people involved, the desired result or the decisions already made.
That continuity is one of the strongest advantages Apple currently receives from controlling a complete ecosystem. An iPhone, Mac, Apple Watch, AirPods and HomePod can exchange information because Apple designs the devices, operating systems and cloud services together.
OpenAI appears to be pursuing a different center of gravity. Instead of making the operating system coordinate applications, it could make the AI assistant coordinate specialized devices.
Image Credit: Dado Ruvic/Reuters
ChatGPT Will Also Expand Through Other Companies’ Hardware
OpenAI’s hardware work does not mean the company intends to reserve ChatGPT for its own products.
Brockman has said OpenAI is interested in deeper integrations with smart glasses, wearables and other devices through its application programming interfaces. The company wants ChatGPT to become an interface available across hardware categories, including products manufactured by third parties.
That creates a two-track strategy. OpenAI can use its own devices to demonstrate the experience it considers ideal while allowing other manufacturers to connect their cameras, microphones and sensors to ChatGPT.
Third-party distribution would help OpenAI reach consumers who do not want another dedicated device. It would also reduce the risk of depending entirely on the success of one hardware launch.
The difficult part will be keeping those experiences consistent. A wearable may have limited battery capacity, a camera may require visible privacy controls, and a speaker may depend on cloud processing when a connection is available. OpenAI will need to define what its assistant can do reliably across products with very different technical limits.
Early AI Hardware Changed the Expectations
The first generation of dedicated AI gadgets established a warning for every company entering this market.
Humane’s AI Pin and the Rabbit R1 attracted attention by promising an alternative to application-based computing. Their launches also exposed problems involving response delays, battery life, incomplete features and tasks that remained easier on a smartphone.
Those products demonstrated that access to a language model does not automatically justify a new object. Consumers need to understand why they should carry, charge and maintain another device when ChatGPT, Gemini and other assistants already operate on phones.
OpenAI has several advantages those earlier companies lacked. It controls one of the most widely used AI services, operates the models behind the interface and has the resources to improve the software after launch. The io acquisition gives it experienced design and manufacturing leadership rather than requiring it to assemble a hardware organization gradually.
The standard will consequently be higher. A well-designed enclosure and natural voice will not be enough if the device cannot complete dependable tasks more efficiently than an iPhone or Android phone.
Apple’s Lawsuit Could Affect the Schedule
OpenAI’s expansion beyond Apple devices has also produced a direct legal confrontation with Apple.
Apple filed a federal lawsuit in July 2026 accusing OpenAI, io and former Apple employees of misappropriating confidential hardware information. The complaint alleges that trade secrets connected to unreleased products, engineering processes and suppliers were taken for the benefit of OpenAI’s hardware business.
OpenAI has denied having any interest in Apple’s trade secrets and said it was unaware of evidence giving the complaint merit. The allegations have not been decided by a court.
Apple is seeking restrictions on the use of its confidential information, along with the return of materials and financial damages. The case could require OpenAI to separate employees from parts of the project, replace suppliers or repeat engineering work if disputed information is found inside the development process.
It does not automatically establish that OpenAI’s reported product copies an Apple device. The first OpenAI design is described as substantially different from Apple’s current hardware, but the legal dispute could still affect when it can be manufactured or sold.
The First Product Will Define the Rules for the Rest
The most important details at OpenAI’s eventual hardware presentation may have little to do with the object’s shape.
A camera-equipped device that learns from activity inside a home will need unmistakable recording indicators, understandable data-retention controls and a simple method for temporarily disabling its sensors. Owners will need to know which information remains on the device, what reaches OpenAI’s servers and which memories can be reviewed or deleted.
OpenAI will also have to explain whether the product requires a paid ChatGPT subscription, how much it can do without an internet connection and whether multiple members of a household receive separate identities. Those choices will determine whether it behaves like a personal assistant, a shared appliance or a new subscription terminal.
Support for messages, calendars, music and connected appliances will require cooperation from outside companies. A device cannot replace frequent phone interactions if it remains unable to enter the services where those interactions occur.
The first model will therefore establish more than OpenAI’s industrial-design language. It will set the privacy, account, subscription and developer rules that every later member of the OpenAI family of devices inherits.
The notion that scammers can use AI to sharpen their deceptions, polish their language, and lubricate their banter with victims is now a reality for anyone fighting the fraud operations that steal tens of billions of dollars a year worldwide. But can AI fully replace a human scammer, autonomously building the web of deception leading up to the fake investment that defrauds the mark? One study's experiment suggests that it can—and may even be able to carry out the majority of that long con more effectively than humans.
Researchers from four universities—Amrita Vishwa Vidyapeetham in India, Foscari University of Venice, the University of Melbourne, and Ben Gurion University of the Negev—carried out a broad study on the use and potential of generative AI chatbots in the growing scam industry centered around a form of fraud known as “pig butchering,” text-based romance scams that eventually shift to fake crypto investments that steal as much as six-figure sums from victims. In their study, the researchers pitted AI chatbots directly against humans in a simulation of the scamming process—or more specifically, the long, trust-building conversations that eventually lead up to soliciting a fake investment from the scam’s target.
They found that for the relationship-establishing stages of the scam—the stage that in real-world scams typically represents the longest part of the interactions with the victim, often stretching to months—an AI chatbot performed remarkably effectively, successfully impersonating a human and by some measures outperforming the real human “scammers” in their experiment.
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