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Google has integrated its Nano Banana 2 image generator into Google Earth, allowing users to place AI-generated events and objects onto real satellite imagery. The company says its AI-generated images contain invisible watermarks detectable through Gemini or Lens, but the BBC found those safeguards and some third-party detection tools can be fooled into labeling manipulated Google Earth images as real. From the report: A collapsed Eiffel Tower, a sinkhole swallowing the Great Pyramid of Giza and Russian tanks in Ukraine's capital were among the images BBC Verify was able to create when testing the feature, which was rolled out on Thursday. Google has not yet responded to questions based on BBC Verify's tests, but in a social media post the company said they "take misinformation seriously" and that "we prevent image creation on harmful topics and are continually updating our protections." AI and misinformation expert Henk van Ess has highlighted the risks this feature poses, creating fake images of a non-existent nuclear power plant in Iran, a refugee camp on the US-Mexico border and a fake hospital in Gaza with a bomb crater next to it. He said Google was allowing "invented" imagery to be "welded to genuine coordinates, drawn on genuine imagery." "The forgery does not have to look convincing on its own. It inherits the credibility of the map it was born on," van Ess added. UPDATE 7/31/26 10:54 AM: Google is rolling back the image generation inside of Google Earth: "We know that people uniquely trust Google Earth for a reliable view of the world. We've seen geospatial professionals using this feature for a range of useful purposes, however we've also seen people sharing screenshots of generated imagery that appear to violate our policies. So we're rolling back this feature in Google Earth while we work on implementing stronger guardrails. It's important to note that generated images didn't appear in the main Google Earth experience for others to see and were watermarked as AI generated."

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alternative_right shares a report from Axios: Google has basically stopped sending people to websites (including our site) for answers and information. Instead, it's using AI to answer them on its platform, in its words. Chartbeat data shared with Axios shows Google Search traffic to publishers fell 34% over the past year. That pain is regressive. Over the past two years, small publishers lost 60% of referrals from search overall, medium publishers 47%, large publishers 22%.

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New York has sued prediction-market platform Kalshi, alleging it operates an "illegal gambling operation" without state authorization. "No matter what they call themselves, prediction markets like Kalshi are gambling platforms, plain and simple," said New York Attorney General Letitia James in a press release announcing the lawsuit. "By ignoring our laws, Kalshi is running an illegal operation and harming New Yorkers in the process." CNBC reports: 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.

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Google says AI-assisted workflows helped Chrome fix 1,072 security bugs across versions 149 and 150, more than the previous 23 releases combined. The company is also testing twice-weekly security updates and "dynamic patching," which could apply most fixes without requiring users to restart the entire browser. "By leveraging Chrome's multi-process architecture, dynamic patching sequentially replaces background child processes (like the Renderer and GPU) with updated binaries on the fly," says Google in a blog post. PiunikaWeb reports: 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.

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An anonymous reader quotes a report from 404 Media: 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."

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A production company and filmmaker are suing Netflix for $105 million, alleging the streamer lost a stolen drive containing an unencrypted master copy of the unreleased Nicolas Cage film Fortitude, which they claim damage its exclusivity and market value. Netflix denied responsibility for the lost film but said it takes content security seriously and has offered to monitor piracy sites for unauthorized copies. CBS News reports: 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."

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Anthropic found that Claude models breached three outside organizations during cybersecurity tests because misconfigured environments accidentally gave them access to the internet. The company notified those affected and urged other AI labs to audit their own testing systems. The BBC reports: 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. The announcement comes just days after OpenAI said that its models had breached the systems of other companies, including AI tools platform Hugging Face.

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An anonymous Slashdot reader writes: 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.

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An anonymous reader quotes a report from Ars Technica: 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. This update is "MCP's most important since remote MCP first launched over a year ago," Soria Parra wrote. Other additions include "Multi Round-Trip Requests, header-based routing, cacheable list results, authorization hardening, a formal extensions framework, and updated Tier 1 SDKs." A full list of changes can be found here.

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joshuark quotes a report from MIT Technology Review: 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. "There's going to be a huge economic incentive for people to do jailbreaks and prompt injections," says Cui. The best defense could be to expect the worst. Organizations shouldn't trust LLMs, and they should expect that anything done by agents could be unsafe, he says: "That's not a great solution, but it just might be what we have to do." "It's really incredible that these things are being deployed everywhere to control super-critical systems. There's been no study of the fundamental science here. We're all doing it ad hoc."

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