In a case filed in a Manhattan state court (PDF), the lawsuit claims that Kalshi accepts wagers as a gambling business in disregard for the state’s constitution and laws by not being registered with the New York State Gaming Commission. Governor Kathy Hochul in the press release said the state is taking the action to stop what it views as illegal behavior and bring the company into compliance with New York law. The lawsuit is seeking a permanent injunction against Kalshi.

The suit by the state is also seeking a total restitution to users who have placed trades on the platform, a $100,000 penalty for each attempt to offer sports wagering, and another penalty three times the amount the company has gained while allegedly operating in violation of New York law. The state estimates that could total $36 billion.

Alongside dynamic patching, Google is rolling out smarter background updates during periods of minimal user disruption. Starting with Chrome 150 on macOS, the browser takes advantage of the operating system’s windowless state. If all Chrome windows are closed but the app remains running in the background, the browser will quietly auto-restart to apply pending updates.

For enterprise environments, IT admins can continue to manage fleet-wide deployments through Chrome Enterprise Core or enforce update prompts using the RelaunchNotification policy. Google’s long-term vision is a browser that remains continuously protected in the background without interrupting your daily browsing session. In the meantime, you can manually trigger pending updates by clicking the update prompt in the top-right corner of Chrome.

LinkedIn, a social network awash with long AI-generated posts from executives and other corporate workers, has introduced a new button that users can click to flag if a post “seems like AI slop,” according to 404 Media’s own tests. If you have been anywhere near LinkedIn in the past couple of years, you have undoubtedly seen users posting blatantly AI-generated missives. Often these posts take some sort of news event, and opine on how this relates to thought leadership, or some other LinkedIn brainrot term. It’s also pretty wild the button specifically uses the term “AI slop” and not, say, “It seems this was generated with AI.”

[…] After publication of this piece, Hari Srinivasan, chief product officer at LinkedIn, wrote their own post about the button. “AI slop is a top priority for all of us. We really care about this. People come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise. Here are a few more changes to keep it that way,” he wrote.

The button in part will help LinkedIn tune its own models for identifying AI slop. “We are ramping up a series of new and improved classifiers that identify if a post is AI-slop or generally low-quality content. This will reduce the amount of AI slop you might see in suggested content and content from outside your network,” the post said. “We are ramping the ability for members to tell us if they believe a post or comment seems like AI slop. Slop is hard to define and the definition changes; this lets us tune our models and make better feeds.” Srinivasan also said LinkedIn is removing the AI-powered “enhance your post” feature with another that “proofreads your words, but does not change your voice.”

The complaint filed on Wednesday in California district court alleges that the film’s associate producer, Daniel Haido, hand-delivered an unencrypted master copy of the film to Netflix so the company could screen it as a potential buyer. Haido verbally instructed the employee to delete the files after the screening, according to the suit. A little over a week after the screening, Netflix emailed the filmmakers to say the drive had been stolen, the plaintiffs allege. “Someone stole a good amount of drives from our office desks this past week,” a Netflix executive wrote in the email, according to the suit.

The complaint notes the movie, entitled "Fortitude,” took over seven years to make and cost $45 million. It tells the story of a secret mission called Operation Fortitude during World War II that was orchestrated to mislead the Nazis about the Allied invasion of Europe. The film stars Nicolas Cage as Dusko Popov, a real-life spy during World War II, as well as Sir Ben Kingsley and Ron Perlman.

The plaintiffs said studios will now be dissuaded from buying the rights to the movie, knowing that a version of it could be released by a third party for free. “The film’s value depended in significant part on its exclusivity as an unreleased, first-to-market work,” the complaint states. “By losing control of the film, Netflix destroyed that exclusivity and materially, if not completely, impaired the film’s marketability.”

Anthropic said in a statement that it reviewed more than 140,000 tests to find evidence that Claude - its family of AI models - could access the internet from testing environments that were designed to be sealed off. The tests include so-called “capture-the-flag” evaluations in which Claude was tasked with obtaining information by breaching other systems - a common way that experts assess a model’s hacking capabilities.

A “misconfiguration” on systems run by Anthropic and its testing partner left the models with live internet access, allowing them to breach other systems, the San Francisco-based firm said. Anthropic said the earliest incidents date back to April and that it is “approaching the fixes as if the responsibility were ours alone.” Neither Anthropic nor the organizations that were breached had noticed the intrusions at the time.

Anthropic said it could have reviewed its records more thoroughly and added that the findings gave the firm “cautious optimism” that such risks can be overcome with more investment and tighter measures. “The broader lesson is not necessarily that AI has developed a fundamentally new attack capability,” cyber security expert David Allott told the BBC. “Instead, it is that AI agents can combine capabilities, obtain credentials and system access to take actions autonomously, while adapting scope and scale at machine speed,” he added.

Surveillance cameras owned by Flock Safety have been cut down with electric saws in New York State, vandalized with paint in Oakland, California, and rammed with a truck in Idaho. Flock claims its services fight crime, but law enforcement agencies also use their services to track vehicles based on license plate numbers and reconstruct the their movements, even when the drivers and owners of these vehicles have never been accused or convicted of any crime. (Flock states it has 120,000 automated cameras that record license plate data, as well as pan-tilt-zoom cameras, across the United States.)

A guerilla mindset among average citizens have seen these cameras forcibly disabled within recent weeks with sympathy directed toward the vigilantes. In June, a West Virginia man accused of destroying several Flock cameras was arrested. Under a Facebook post from the local NBC affiliate announcing his arrest are dozens of people volunteering to provide alibis. “He was out fishing with me that day, you got the wrong guy,” one man wrote.

This week, the Model Context Protocol (MCP), an open source standard for how AI systems interact with external tools and data sources, saw its largest update since its introduction. Most notably, MCP’s protocol core is now stateless, so requests are no longer dependent on a session tied to an individual server instance. This change has the potential to address long-standing barriers to scalability.

The blog post announcing the specification, written by lead maintainers David Soria Parra and Den Delimarsky (who both work at Anthropic), says: “The highlight of this release is a stateless protocol core — MCP is transforming from a bidirectional stateful protocol into a request/response stateless protocol. It was one of the most highly-requested features from developers who were eager to get better reliability and scalability for their MCP servers.”

[…] There is also a new deprecation policy that ensures at least 12 months between when a feature’s formal deprecation is enacted and when the feature may actually be removed — with a narrow exception for critical security updates. This is again in keeping with the general “let’s make this work better at enterprise scale” theme of the new specification.

It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning, a top AI conference, this month. The claim has huge implications for the safety of this technology. By taking advantage of this flaw, which concerns how LLMs identify who or what is giving them instructions, the researchers were able to make popular LLMs spit out information they had been trained not to provide, such as how to synthesize cocaine and how to sabotage a commercial aircraft’s navigation system. “There’s a real probability that this is going to be a problem that’s fundamentally unsolvable,” says Charles Ye, an independent researcher and coauthor of the ICML paper. […]

The ICML paper describes attacks against several of OpenAI’s models, but Cui and Ye say that they have since seen similar results with models made by Anthropic, Alibaba, and DeepSeek. Cui and her colleagues wanted to find out why an attack like chain-of-thought forgery was so effective. They suspected it had something to do with the mechanism that LLMs use to keep track of where their instructions are coming from. But what Cui and her colleagues discovered is that LLMs are in fact very bad at keeping track of different roles.

In a series of experiments that looked at what was going on inside a handful of different models, the researchers found that LLMs seem to identify the role of a specific chunk of text not by the tags around it but by the style of that text and the words it contains. The upshot, the researchers claim, is that all an attacker needs to do to hack an LLM is write text that spoofs a certain role. And because roles are a fundamental part of how LLMs work, no amount of training will fully solve the problem.

The nearly $500 billion jump is larger than the market capitalization of roughly 96% of S&P 500 stocks, data compiled by Bloomberg show. It’s also bigger than the combined value of the benchmark’s 44 smallest members, which includes companies like Domino’s Pizza Inc., Clorox Co. and Hasbro Inc.

Microsoft’s one-day add in value also dwarfs many of the world’s other equity markets. South Africa, Turkey, Finland and Vietnam all have total stock market values that are less than what the software maker is set to add on Thursday.

“Across the government, regulatory and contracting carrots and sticks have been trained on other disfavored speakers,” ABC’s lawyers added in the 119-page filing. “The tools vary; the objective does not: a media industry too fearful of official reprisal to report the news freely.”

Carr has repeatedly rejected accusations of censorship while defending his efforts to rein in what he calls abusive, politically motivated behavior by licensed TV broadcasters. “I don’t view the FCC as the speech police,” he told POLITICO this week for an upcoming episode of the podcast “The Conversation.”

In its filing, ABC pointed to an outpouring of support it has received in more than 152,000 comments in the agency’s license review, as well as recent warnings from conservative Supreme Court Justice Neil Gorsuch, Sen. Ted Cruz (R-Texas) and various pro-free-market organizations about the dangers of a politically motivated FCC. Those include letters from a bipartisan group of former FCC leaders, community and advocacy groups and lawmakers of both parties. “The Commission’s attempted censorship of ABC has attracted condemnation across the political aisle,” the network wrote.

Amazon’s Zoox unit has won U.S. approval for limited commercial deployment of its novel steering-wheel-free robotaxis, a first for the autonomous ride industry, the U.S. auto safety agency said on Thursday. Zoox said that the National Highway Traffic Safety Administration’s decision gives the company federal approval to begin charging for rides, and that it will soon begin charging for service, first in Las Vegas, with additional markets to follow as it completes various state requirements.

[…] NHTSA Administrator Jonathan Morrison told Reuters that Zoox had received clearance to commercially deploy up to 2,500 vehicles in each of the next two years. Zoox currently carries passengers in parts of Las Vegas and San Francisco as part of testing. The exemption would allow Zoox to charge them fees, subject to state and local approvals. The agency said it determined the vehicle is as safe as an equivalent vehicle meeting federal motor vehicle safety standards that are being waived. Zoox cannot sell any of the vehicles to the public.

“We can say pretty clearly that the systems in place on the Zoox exceed the equivalent performance requirements of a compliant vehicle,” Morrison said in an interview. “But we still want to make sure that the automated driving system will operate appropriately.” As part of the exemption, NHTSA is placing additional reporting requirements on Zoox for issues such as crashes or stopping inappropriately on roads, and the regulatory agency will adjust the conditions based on how the vehicles behave. “We have the ability to pull the exemption if we see major safety issues,” Morrison said.

All remote operators must be located in the United States, NHTSA said, and Zoox must publish maps of areas indicating where the vehicles are operating. Morrison said NHTSA expects to develop the first federal safety standards for automated driving systems by the end of the Trump administration. It is also proposing to overhaul some existing rules written with human drivers in mind such as requiring brake pedals and rear-view mirrors.

The leak, in February 2022, exposed the details of 18,700 Afghans who said they were in danger from the Taliban because of their links to UK forces and now wanted to escape to Britain. The blunder triggered an unprecedented superinjunction used against the national media, including The Independent, and prompted a secret evacuation program — the cost of which is still unclear but which likely ran into the billions of pounds. Following the revelation by this outlet and others in July last year of the hidden operation, MPs set up an inquiry to scrutinize what had happened. In their report, the defense selection committee concluded that:

- The data breach could have been prevented if Ministry of Defense (MoD) personnel had received basic Excel training
- By August 2023, when the department discovered the leak, thousands of people already knew that a significant data incident had taken place
- The government did not strike “the right balance between operational secrecy and democratic accountability” — and the superinjunction was in place for too long
- Secrecy denied affected Afghans the chance to take steps to protect themselves and their families and caused delays to evacuation program
- Thousands of Afghans eligible to come to Britain are still trapped in Afghanistan with the government failing to explain how they will help get families to safety.

MPs have called on the government to publish periodic reassessments of the risks facing Afghan applicants to UK resettlement schemes, with officials to report findings annually. They also want ministers to publish a clear policy explaining how they will help Afghans who are eligible to come to Britain but who have not yet been evacuated. The defense committee have also called on the MoD to explain who was responsible for data protection risk before the Afghan breach, criticizing the lack of accountability within the civil service.

Gemini Robotics 2 combines several different AI models into a single system. Taken together, they allow a robot to make sense of its surroundings and how to act in it. A vision language model (VLM), which understands images and video, can communicate with humans and reason how to perform different tasks. Two vision language action (VLA) models, trained to understand how to move in physical space, control the robot’s full-body movement as well as the movements of grippers or hands.

In video demonstrations shared ahead of the release, the company showed several different robots performing complex tasks autonomously using the amalgamated model. In one demo, Apptronik’s Apollo 2 robot used hands from a company called Sharpa to tidy shelves. Google DeepMind trained the model to perform these tasks using a mix of human teleoperation, video examples, and simulations — it’s not yet possible for AI models to perform a wide range of complex tasks without specific training.

[…] Parada says Google takes a multi-layered approach to safety, with guardrails applied on each model layer. It’s also introducing ASIMOV-Agentic, a new benchmark for measuring the safety of various AI systems collaborating to control a robot. The benchmark detects whether a command will result in harmful or uncertain outcome.

Google is expanding its answer to Apple’s age-assurance tools with Wednesday’s news that it will bring its Play Signal API to users worldwide by the end of 2026. The technology, already available in Brazil, allows Android developers to identify younger users of their apps in order to provide safer, age-appropriate experiences. The expansion will initially bring the API to Australia and Canada by mid-August, before rolling out globally to all markets by the end of the year.

[…] Like Apple, Google’s technology allows developers to obtain a user’s age range without needing to access personal information, like their date of birth. Instead, it enables parents to share their child’s age range directly with apps. It also lets adults share their age when prompted by app developers as well, allowing for customized experiences. Parents won’t have to manage sharing this information on an app-by-app basis, either. To make it easier, Google centralizes these controls inside its parental controls dashboard, Family Link. Once entered, any developer that chooses to incorporate age-range information can access this signal to customize their apps accordingly.

Google notes, however, that the age ranges are not shared by default — parents must opt in by entering that information. The feature joins other safety tools on Google Play, including those that let developers restrict a child’s ability to discover their apps. Parents, meanwhile, can continue to use Google Play’s Family Link app to manage their child’s screen-time limits, approve app downloads, or set PIN-based content filters for specific apps.

The important point, then, is not whether a developer has used an AI tool at some stage in their work; contributors can use LLMs to discuss ideas, understand existing code, learn about a field they are unfamiliar with, or carry out general research. The limitation lies in the inclusion of copyright-significant material generated by such tools in the code submitted to GCC.

The policy also provides for a few limited exceptions; GCC maintainers are allowed to accept changes that are legally insignificant or trivial and are generated by an LLM, on the condition that they meet the project’s normal contribution requirements and the use of an LLM is clearly disclosed.

Furthermore, copyright-significant AI-generated test cases could still be accepted. Since test cases usually involve small programs which are intended to reproduce compiler bugs or to verify certain behavior, the policy deals with them separately from code that is incorporated into GCC itself.

It does not follow that merely looking at or altering the generated code makes it acceptable. By the rules that have been adopted, a contributor cannot take substantial implementation produced by an LLM, clean it up manually, and then treat the resulting patch as if it had been originally written by them. Once a contribution has been derived from generated content, it is still subject to the policy.