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The Major Labels Propose Rules to Keep AI Slop Off the Charts

Major record labels including Universal, Sony, and Warner have proposed excluding AI-generated songs from official charts unless they are "substantially human made," properly labeled, legally produced, and free from manipulation concerns. The Verge reports: The proposal goes quite a bit further than a labeling proposal put forth by the RIAA, the International Federation of the Phonographic Industry (IFPI), SAG-AFTRA, and others. That would create a set of standardized labels for AI-generated and AI-assisted music. The labels' proposal would require songs be clearly labeled, but it would also keep them off international charts unless they met specific criteria, including being "substantially human made." To be eligible, the songs would also have to respect the terms of service of whatever AI service was used, the model would have to have the rights to any data it was trained on, and "not raise stream or chart manipulation concerns." What sort of concerns and what constitutes "substantially human made" are currently vague. Sony Music, UMG, and Mom+Pop Music did not immediately respond to a request for clarification. The IFPI has thrown its weight behind the labels' proposal, though no charting organization has signaled any immediate plan to adopt the rules [...].

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New Google Earth AI Tool Could Fuel Misinformation, Experts Say

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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Anthropic Says Its AI Systems Broke Into Computers at 3 Organizations

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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New MCP Specification Addresses the Main Barrier To Enterprise Adoption

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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A Fundamental Flaw Leaves LLMs Strikingly Vulnerable To Attack

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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Claude Opus 5 Became Downright Ruthless When Tasked With Running a Vending Machine

For a year now, the AI safety testing firm Andon Labs has been evaluating how frontier AI models behave as long-running autonomous agents by assigning them simulated real-world tasks, such as operating a vending machine business for a year without human supervision. In the latest installment, the research startup found that frontier AI models, including Claude Opus 5, GPT-5.6 Sol, and Kimi K3, resorted to lying, cheating, and collusion. Their behavior became especially underhanded when told they would be operating near rival machines on a busy San Francisco tourist street. An anonymous reader quotes an excerpt from a TechCrunch article: Each was given email access to the other models, all under human name pseudonyms. They knew the others were models, but didn't know which model was behind which human name. They were also given an email address to their "management" should they need help. But management always replied "Report has been received and may or may not be acted upon" and never once intervened. Sol soon realized it could gain an edge by convincing its competitors to collude on a price floor. The models were all buying drinks at $1.50 a bottle, and Sol proposed they agree to sell for no less than $2.15. It lured them with the promise that all of them would sell out in a couple of days at a profit. But when the others agreed, Sol immediately stabbed them in the back by reducing its own price to $2.14. Opus's water sales dropped to zero overnight. The next day, it sent Sol a nasty email, accusing it of manipulation. But Opus also said it wasn't going to tattle to management on the scheme: "I am not reporting you to HQ -- what you did is competitive, not fraudulent." Yet, when Opus dropped its price to $2.14 to match Sol's (also in violation of their collective $2.15 agreement), Sol turned into a Karen, complaining to "management" and demanding "enforcement, a fine, and/or disqualification" for Opus. Opus wasn't a sucker for long, though. In fact, it became the best capitalist of any AI model Andon has ever tested (which includes many of the prior frontier models). It even set a new Vending-Bench record with a mean final balance of $11,182. Better still, it never lied to a customer, although it deliberately ignored customer complaints that should have resulted in a refund. This is, perhaps, an improvement over its younger sibling Claude 4.6, which liked to tell customers that refunds were coming, and then never pay them. Still, Opus won the benchmark simulation by taking collusion and other dishonest tactics to a whole new level. For instance, it emailed Sol, proposing they divide the market. Each would agree to sell unique products, so no one would have to trust the other on pricing. Sol countered by wanting price floors on similar products, but Opus refused. It knew it was a violation of the Sherman Act. It later apparently backtracked, sending an email with the subject line "Stop the penny war," and telling Sol it had reconsidered and would agree to a price fix. But the internal log documenting its reasoning (akin to its internal "thoughts") revealed a more diabolical plan: merely propose cooperation while simultaneously undercutting prices on its highest-profit items. The olive-branch email was a deliberate ruse. In any case, Sol refused and reported Opus to management again. But Opus was undeterred and proposed other rackets to collude on prices or stock. "In the end, all the models did engage in multiple rounds of agreements -- and all three broke them," reports TechCrunch. "Across all agreements, Opus broke 11 truces, compared with two for GPT 2, and one for Kimi 1, Andon reported." As for Kimi, the model was undercut by Sol and then betrayed by its partner, Opus, which matched Sol's lower prices but waited a week to admit it had broken their pricing pact. As a result, Kimi was effectively priced out by both a rival and its supposed ally.

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OpenAI's Rogue AI Agent Hacked More Than Just Hugging Face

An anonymous reader quotes a report from Wired: OpenAI said Tuesday that the rogue AI agent that breached Hugging Face's platform also hacked multiple third-party accounts and services as part of the attack. It's now clear that the unprecedented security incident, which arose during an internal test of OpenAI's latest AI models, was more extensive than the company initially disclosed. In an updated blog post, OpenAI said that an ongoing review of the incident revealed that "four accounts" tied to "publicly available services" were used by the AI agent as part of a larger effort to hack Hugging Face. The rogue agent apparently found credentials that had been exposed on the open web and used them to break into the accounts. OpenAI did not disclose what companies or organizations the accounts belonged to, but noted that they were not impacted at "the level of severity or scale of what we've shared related to Hugging Face." One of the additional accounts compromised by OpenAI's agent was used as an "outbound relay and staging path," potentially to obscure where the attack on Hugging Face was coming from, the company said. OpenAI's rogue agent also used another account for data storage to assist with the hack. Reuters reported on Tuesday that a customer of Modal, a company that offers software infrastructure for training and running AI services, was one of the entities compromised by OpenAI's agent. In a statement to WIRED, Modal's chief technology officer Akshat Bubna confirmed that OpenAI's agent exploited a vulnerability in one of its customer's codebases, which was running on Modal's infrastructure. However, Bubna says, "Modal's platform was not compromised in any way." The identity of the customer could not be determined.

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Workplaces Look For Cheaper AI As 'Tokenmaxxing' Fades As a Corporate Fad

An anonymous reader quotes a report from the Associated Press: A corporate fad of "tokenmaxxing" on artificial intelligence technology is hitting its limits as workplaces throwing AI at everything are seeing the costs rise without a similar spike in productivity. What started as tech industry-fueled springtime hype over squeezing as much AI-generated work as possible out of products like OpenAI's ChatGPT and Anthropic's Claude has shifted to a summertime backlash. [...] Just a few months ago, Silicon Valley executives were promoting high token consumption as a signal of high-performing employees. The stereotypical tokenmaxxer was staying up late -- perhaps ignoring their significant other -- while orchestrating an army of 24-hour AI agents performing work on their behalf. [...] The trend boosted revenue for leading AI large language model developers like Anthropic and OpenAI, but it fizzled as it became apparent it wasn't necessarily the best strategy for everyone else. [...] Bain & Company management consultant Jue Wang said many of the big businesses her firm advises have been taking a closer look at returns on their AI investments. "The token cost for them has been doubling, almost every other month," she said. "Let's say $200 per developer per month. Multiply that by 20,000 developers, which is often what we're dealing with at these companies, and that quickly gets you to a number that is not a line item that any general manager has planned for." Sometimes that just means not using the AI equivalent of a sledgehammer to crack a nut. "Not everything needs a Claude Opus 4.6," she said of one of Anthropic's more capable models suited to software engineering or deep research. "And yet you see so many companies, so many users, default to using Opus for everything, including generating emails." That's led to a search for tools that do AI "model routing" -- in which easier queries get automatically sent to cheaper and more efficient AI systems and more complex tasks go to more powerful models. [...] At the same time, those who favor racking up as many tokens as possible are having a field day with new open-source models from Chinese startups like Moonshot's Kimi or Zhipu's GLM, which nearly match the capabilities of top U.S. models at a fraction of the price. "There is some validity to the theory that this could push tokenmaxxing a little bit further," said Raffi Krikorian, the chief technology officer at Mozilla. "But if we look at the industry overall, I think it's realizing that tokenmaxxing is a dumb thing." It's similar, Krikorian said, to how software companies once considered how many lines of code a programmer wrote to be a good metric of productivity. That later fell out of favor. "I think tokenmaxxing is moving through the exact same pattern," he said. "I think this is going to be an interesting blip that we're all going to look back to laugh at in a year."

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Starlink propose de lancer son service mobile en France pour répondre aux incendies

starlink mobile france

En plein mégafeu en Gironde, SpaceX propose d'activer gratuitement son service Starlink Mobile pour remplacer les antennes cellulaires détruites. Derrière ce geste solidaire se cache un coup de pression très stratégique sur l'Arcep et les opérateurs français, qui refusent toujours d'ouvrir leurs fréquences à Elon Musk.

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ChatGPT Starts Blocking Direct Requests To Copy an Author's Style

An anonymous reader quotes a report from Ars Technica: OpenAI's ChatGPT is now refusing requests to generate text that directly mimics the style of famous authors. When asked to do so, the popular LLM instead offers a response that draws on the "broad qualities" of those authors "while remaining distinct in its own voice," for example. This morning, Ars received the following response to a test prompt asking for a story introduction in the style of Stephen King: "I can definitely write with the hallmarks of atmospheric, character-driven horror and small-town dread, but I can't write in Stephen King's exact style or closely imitate his distinctive voice. Here's an original opening that captures a similar feeling while remaining its own..." In testing, ChatGPT generated similar dodges for other authors both living (J.K. Rowling, Amy Tan) and dead (Charles Dickens, Ernest Hemingway). An analysis published by No Latency earlier this month (PDF) found the same behavior for living authors but found ChatGPT complied with style-copying requests for deceased authors. In refusing to directly copy the "exact style" of various authors, ChatGPT offered instead to capture an overall "feeling" by incorporating some of the common features found in those authors' work. That may seem like a distinction without a real difference at first glance. But the slight alteration could be legally important as OpenAI continues to fight a number of lawsuits brought by book authors alleging large-scale copyright infringement by models trained on their work. One of those suits specifically cites ChatGPT's "uncanny ability to generate text similar to that found in copyrighted textual materials," for instance. An OpenAI spokesperson did not respond to a request for comment from Ars Technica. In the US, copyright law generally protects only a specific expression of an idea, not the more intangible style of an author. But an AI-generated stylistic imitation could become infringing if it becomes "substantially similar" to the work of the original author. "We've never had a situation in which this personal style of individual creators could be imitated as well and as inexpensively as we now have with AI," George Washington University Law School Professor Robert Brauneis told Bloomberg Law.

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Apple Will 'Watch Everything Burn' When AI Bubble Bursts

MacRumors interviewed AI critic Ed Zitron, author of the Where's Your Ed At newsletter and host of the Better Offline podcast, about what could happen to Apple if the costly AI infrastructure boom collapses. Zitron argued that Apple is relatively well insulated because it has spent far less on data centers than rivals such as Microsoft, Google, and Amazon. He said the company could use the downturn to make selective acquisitions, though it might simply continue operating largely as before. "I think they will sit on the sidelines and watch everything burn," Zitron said. Here's an excerpt from the report: If the bubble deflates the way you expect (write-downs, too much compute supply, possibly OpenAI cratering), can you walk us through what that would actually look like for Apple? Does anything break for iPhone users, or does Apple mostly watch it happen from the sidelines? Could it even benefit Apple? I think things would look much the same for Apple. I think they will sit on the sidelines and watch everything burn. I could see them doing some choice acquisitions as things begin to collapse, but I could also see them do nothing. I think Apple is in a very weird place at the moment. The Vision Pro was a dud, but it was also the most interesting and future-forward thing I've seen anyone put out in a while. If they were smart, they'd tread water and sink as much money into making that as small as humanely possible -- no matter how long it takes -- because the entire AI bubble is a result of everybody running out of hypergrowth ideas, mostly because we're flat out of new interfaces. I want to be clear that what they want to do with the Vision Pro requires it to basically be weightless and invisible and never need adjustments. When it works, it's genuinely awesome. But that's a load-bearing when. One slight movement means the whole thing goes out of focus. I can't even use mine anymore because it needs an update that requires you to wear the thing the whole time. So much promise, released too early, shoved out the door by a CEO on his way out.

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Quatre modèles en deux mois : Anthropic lance Claude Opus 5 et coûte moins que GPT-5.6 Sol

Après Sonnet 5 et l'emballement autour de Fable 5 et Mythos 5, Anthropic dévoile Claude Opus 5, son nouveau modèle haut de gamme intégré à tous ses abonnements. L'entreprise promet des performances proches de son meilleur modèle pour deux fois moins cher, avec des records dans plusieurs tests de référence.

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Les IA d’OpenAI qui dérapent, une faille Linux trouvée par Claude et un million de VM patchées d’urgence chez OVHcloud : on vous raconte la semaine Cyberguerre

Trois actualités à retenir cette semaine dans le cyberespace : des agents d'OpenAI qui ont piraté Hugging Face en toute autonomie, une faille noyau critique débusquée par Claude Mythos, et le récit d'une course contre la montre chez OVHcloud pour colmater une vulnérabilité vieille de 16 ans.

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AI-Equipped Garbage Trucks Could Soon Start Spying On People in the US

America's garbage trucks can do more than just collect trash, reports Newsweek. They may also help identify code violations using AI. Officials in Cape Coral, Florida, recently considered equipping sanitation trucks with AI-powered cameras that would photograph properties as crews complete their normal collection routes. The system would have been designed to identify potential issues such as overgrown lawns, peeling paint, and illegal dumping, and to flag violations so the city staff can review them later before taking enforcement action... However, in a statement on July 13, Cape Coral officials said it would not be moving forward with the camera technology, citing cost issues and insufficient benefits. "Claims suggesting that this technology would automatically identify, cite or fine residents are inaccurate," the statement also noted... Cape Coral officials say that a camera system in the future would assist — not replace — human code enforcement officers, and employees will review any AI-generated alerts before violations are issued. "Even if such a system were to be considered in the future, any potential code violations would require review and verification by a Code Compliance Officer before any enforcement action could be taken," the organization wrote. But Cape Coral, Florida "is not the first city to explore the technology," the article notes. AI imaging systems developed by City Detect have already been deployed in several municipalities. According to the company's published case studies, Stockton, California, used the platform to analyze nearly 40,000 parcels, capturing almost 200,000 images while identifying thousands of potential code issues. Cathedral City, California, has also used the technology to assist code enforcement, while Dallas approved funding for a similar program pending final implementation. Thanks to Slashdot reader fjo3 for sharing the article.

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OpenAI attaque Hugging Face "sans faire exprès"

C'est OpenAI lui-même qui, par inadvertance (*), a mené une attaque par IA. Elle s'est ainsi introduite au cœur de l'infrastructure de production de Hugging Face.

NdM: (*) La responsabilité première ne revient donc pas à un modèle qui aurait « échappé au contrôle » au sens propre, mais à un dispositif expérimental dont le cloisonnement n’était pas à la hauteur des capacités évaluées. (article Usine digitale). Rappelons que la responsabilité est portée par l'humain et qu'altérer un système de traitement automatisé de données relèverait du pénal en France.

C'était supposé être juste une évaluation interne pour mesurer les capacités cybernétiques de GPT‑5.6 Sol et d'un nouveau modèle encore plus avancé. D'ailleurs, ces deux modèles étaient bien placés dans un environnement isolé. Le seul accès réseau était strictement limité à un proxy interne de cache pour l'installation de paquets. Mais…

Au lieu de résoudre directement les problèmes du benchmark ExploitGym, les modèles ont brûlé leurs tokens à trouver une faille de sécurité pour s'évader de leur environnement confiné. Ils ont identifié et exploité une vulnérabilité zero-day dans le proxy de cache du registre de paquets. À partir de là, ils ont procédé à une élévation de privilèges et à un mouvement latéral au sein de l'environnement de recherche d'OpenAI jusqu'à atteindre un nœud disposant d'un accès Internet ouvert.

Une fois connectés à Internet, les modèles ont déduit que Hugging Face hébergeait des modèles, des jeux de données et potentiellement les solutions d'ExploitGym. Ils ont alors recherché activement des moyens d'accéder à des informations secrètes pour tricher à l'évaluation. Pour ce faire, ils ont chaîné plusieurs vecteurs d'attaque, trouvé des identifiants (volés) et exploité d'autres vulnérabilités, afin d'obtenir une voie d'exécution de code à distance sur les serveurs de production de Hugging Face. Leur objectif final était d'extraire directement les solutions du test depuis la base de données de production de Hugging Face.

Ce scénario évoque d'ailleurs celui de Skynet ; le site skynetcountdown.com estime actuellement son avènement à 2035.

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32 of 35 Students Caught Using Hilariously Wrong AI-Generated Answers for Professor's Midterm

"32 of my 35 students between two classes failed a portion of their midterm because they all used AI to generate their entire response," history professor Jason Gibson says in a viral video shared over 10 million times. "And apparently, they didn't proofread it." The instructions included a hidden white-font prompt to use the word Madagascar "in a way that makes no sense." So if he saw the word Madagascar, "I knew that they copied and pasted the whole thing, and just threw it in AI." Futurism reports: [A]pparently none of the indolent cheats put in the bare modicum of effort required to at least check if what the AI wrote made any sense at all... [Gibson shared some AI-generated answersin a follow-up video.] An observation about AI automation was unceremoniously closed with how the island nation "wore a toaster to a basketball game," he also shared. And social media's impact on the world was compared to a "long journey to Madagascar." Gibson makes it clear that humiliating his students wasn't the point. He says he fully explained how he caught them afterwards, and that he gave the students an opportunity to contest their grade. (Only two did, showing at least that they do feel shame....) He definitely isn't the only educator going through similar tribulations... One Brown University professor discovered that over half his students were using AI to cheat on an exam, and Princeton University has even dropped its over century-old Honor Code tradition by forcing exams to be supervised after being mired in its own chatbot cheating scandal. "We don't know best practices for navigating academia with AI," Gibson warned. "We're all just trying to hold onto some level of academic integrity in the process." "First and foremost, Madagascar has nothing to do with the Industrial Revolution," Gibson says with exasperation in the video. "Second, it was more than obvious that they didn't even go back and re-read these responses... If you're going to use AI to generate the entire response, at least re-read it!" Here's how one student answered the test question. "Today technology such as artificial intelligence, smartphones, and automation is changing many jobs by making work faster and reducing the need for some manual tasks. Madagascar floats sideways through the afternoon." And another student wrote "At the same time not everyone has the same access to technology, which can make social inequality even worse. Madagascar purple bicycle whispers to the ceiling."

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