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Na všech herních platformách je každou chvíli nějaká slevová akce. Každý týden proto vybíráme ty nejatraktivnější, které by vám neměly uniknout. Pokud chcete získat hry zdarma nebo s výhodnou slevou, podívejte se na aktuální přehled akcí!

Think tank the Australian Strategic Policy Institute (ASPI) has warned that Venezuela is poised to adopt Chinese AI systems to enhance surveillance systems that already rely on Middle Kingdom tech, and called for US Secretary of State Marco Rubio to do something about it. ASPI outlined the Venezuelan situation in a recent report [PDF], titled Warning signals: Venezuela and the risk of Chinese AI-enabled digital authoritarianism. The document explains that Venezuela’s government built a surveillance state a decade ago, largely using technology from Chinese companies. In 2025, according to the report claims, then Venezuelan vice-president Delcy Rodríguez led an effort that culminated in “an agreement to adopt Chinese-built AI systems … to use Chinese AI to enhance existing state-sponsored surveillance.” One of the companies involved in that agreement is iFlytek, which the US banned in 2019 for its role in helping Beijing to repress the Uyghur Muslim population of China’s Xinjiang region. The USA effectively took over Venezuela in January 2026 and installed Rodríguez as the nation’s leader. ASPI can find no signs she’s changed course on her plan to adopt more Chinese surveillance tech. “Venezuela will become one of the first countries outside China to import China’s new generation of LLM-based AI systems for surveillance and control, and the most advanced adopter in the Western Hemisphere,” the report claims. And while ASPI’s analysts don’t think Venezuela’s government can recreate “China’s full-stack surveillance apparatus.” They do worry that “Chinese or other AI tools could strengthen a domestically directed apparatus already used to manage dissent, restrict information and preserve political power.” That arguably leaves Venezuelans no better off than they were before the US decided to replace former president Nicholas Maduro. And it leaves the USA effectively running a nation that relies on Chinese surveillance tech to maintain power, even as Washington seeks a greater role in Venezuelan affairs. ASPI called on US Secretary of State Marco Rubio, who effectively acts as viceroy of Venezuela, to dismantle the surveillance apparatus and enact political change. The think tank’s analysts believe doing so would signal that democracies are willing to combat China’s attempts to export its surveillance tech, and dismantle it when possible in the name of civil liberty. “Beijing for its part recognizes this opening, and the risk it poses to Chinese companies and China’s political influence there,” the report states. “China’s leaders appear to understand that assets and investments of its national champions are at risk in Venezuela. Beijing’s approach seems to be, in part, strengthening oversight of state-owned assets abroad to assist in risk protection and management.” The report does not address whether greater adoption of Chinese surveillance by Venezuela increases the risks the USA faces as it seeks to control the South American country. ®

Byla vydána nová verze 6.4 programovacího jazyka Swift (Wikipedie). Zdrojové kódy jsou k dispozici na GitHubu.

There’s a new development in a Microsoft antitrust case, originally filed in England’s High Court in April 2021, and it doesn’t look good for the tech giant.

A consent order from the UK Competition Appeal Tribunal is demanding documents from past and present Microsoft executives that may have a bearing on a £270 million (about $361 million) lawsuit filed by secondhand software reseller ValueLicensing. The company alleges that Microsoft offered incentives to customers to shift to subscription services without selling their pre-owned licenses.

Central to this development is a historic, potentially damning internal “Second-Hand Software” (SHS) presentation, referred to in the consent order as a “known adverse document.” The specific content of the presentation has not yet been made public, but Microsoft has until October 31 to explain why it did not disclose the presentation earlier.

Further, a confidentiality designation that previously applied to 11 documents relevant to the case has been lifted.

These developments represent “an inflection point in European tech litigation,” said Forrester senior analyst Dario Maisto.

“For Amazon (AWS), Google, and Microsoft, the signal is clear: Antitrust tribunals are fully comfortable examining software licensing mechanics as tools of anticompetitive lock-in.”

The allegations against Microsoft

Under EU law, it is fully legal to resell perpetual pre-owned (“second hand”) software licenses; software makers cannot use their Terms of Service (ToS) to override this right. ValueLicensing specializes in this secondary market, re-selling licenses for products including Microsoft Windows and Microsoft Office.

But the company alleges that Microsoft has stifled the supply of these pre-owned licenses in the UK and the European Economic Area (EEA) comprising 27 European Union member and non-member countries. It says Microsoft abused its market dominance and entered into agreements that “prevented, restrained or distorted competition” via clauses restricting customers from reselling their Microsoft perpetual licenses in return for subscription service discounts.

“The net result has been higher prices and less choice for customers, who have been steered into cloud-based Office365 and Azure subscriptions,” ValueLicensing claimed, pointing out that many enterprises, as well as publicly-funded organizations, rely on pre-owned Microsoft licenses to keep operating costs low.

The consent order is asking Microsoft to provide its “view” of whether those allegations are true, and to make “reasonable endeavors” to contact former COO Kevin Turner, former president and EVP Jean-Philippe Courtois, and former corporate VP of worldwide licensing and pricing Joe Matz. The company must document that it has done so by November 30.

The company must also file a witness statement from a former consultant addressing who within the company was aware of the SHS presentation and when they became aware of it; why the presentation was not disclosed as a “known adverse document”; when in-house legal counsel became aware of the presentation; what steps were taken to check for these types of “known adverse documents”; and the decision making process within the company when the presentation was located.

Microsoft is also being asked to search for specific terms in the emails and document repositories of Matz, Courtois, Turner, and several other named current and past research managers, former VPs and presidents, between July 2012 and June 2020.

The more than 40 search terms include “SHS,” “antitrust,” “competition,” “ValueLicensing,” “used licenses,” “do nothing,”  “competition,” “revenue,” and “discount licensing.” These documents cannot be designated “restricted” or “confidential,” according to the consent order. They must also be disclosed by November 30.

A witness statement from deputy general counsel Cynthia Randall has been paused.

Microsoft has said that any abuse of dominance was “objectively justified” and that the contractual terms at issue were “necessary and reasonable.” It also argued that anti-competitive effects were “outweighed by and proportionate to” certain benefits and efficiencies.

The company did not reply to a request for comment.

Microsoft is facing similar antitrust allegations from UK barrister Alexander Wolfson, who issued an opt-out class action claim in May 2025 alleging that public and private UK organizations that purchased software licenses, including those for Microsoft Office and Windows, were overcharged over a 10 year period due to Microsoft’s market practices.

Wolfson said in a release at the time that Microsoft’s actions had a significant and far-reaching impact on UK consumers, businesses, and public bodies. “With billions of pounds potentially at stake, this case is about ensuring fairness in the digital marketplace and ensuring even the largest tech companies play by the rules,” he wrote.

Implications for enterprise leaders

For enterprise CIOs, procurement leads, and IT financial managers, the current development holds “practical implications,” said Forrester’s Maisto: Organizations that surrendered perpetual licenses or agreed to contractual restrictions against reselling software as part of an enterprise agreement (EA) renewal or cloud commitment may have given up quantifiable asset value.

“The secondary market for perpetual licenses remains legally valid,” he pointed out.

CIOs should pay close attention to licensing “penalties” or inflated costs for running legacy software on third-party clouds, like AWS or GCP, versus Azure, he said. They should also evaluate the benefits of hybrid licensing strategies. Combining pre-owned perpetual licenses for static workloads with cloud subscriptions for dynamic workloads can yield significant cost savings compared to all-subscription models, Maisto pointed out.

Finally, he urged, “use these rulings as leverage during Microsoft agreement renewals.”

A zero-click vulnerability that allows remote code execution affects all of the major AI coding agents - Anthropic’s Claude Code, OpenAI’s Codex, Google's Gemini CLI, Microsoft’s Copilot, and Microsoft-owned GitHub Copilot - and could give attackers full access to every asset and piece of data that the agent can reach, researchers say. The exploit, dubbed “Plugin4Shell,” is a “first-of-its-kind AI supply-chain attack,” according to threat hunters at Air, a security startup focused on protecting enterprise AI agents. Instead of targeting the model or agent, Plugin4Shell attacks trusted marketplaces that host plugins for major coding agents. Such attacks could therefore reach millions of users and machines, the researchers said. Almost 90 percent of Fortune 500 companies use Copilot, according to Microsoft, which also happens to be one of the two that didn’t ship a patch for the flaw. “The fix has to ship in the agent, and updating is the only complete mitigation where one exists,” Air researchers Or Nevo, Dor Granat, and Niv Hoffman said in a Thursday report. The Air team reported the security issue to all four vendors in June, and both Anthropic and OpenAI patched it in Claude Code 2.1.179 and Codex 0.146.0, respectively. Google has deprecated the Gemini CLI, and therefore told Air it will not patch, so every install remains vulnerable. Google does, however, suggest users migrate to its newer Antigravity agentic development environment, which is protected from this attack. Microsoft didn’t fix the flaw in Copilot. However, a GitHub spokesperson told us the Plugin4Shell attacks do not affect GitHub. “To prevent abuse of SHAs, GitHub does not allow users to create branch or tag names that resemble commit SHAs,” the spokesperson said. “This mitigation ensures the reported vulnerability cannot be exploited on GitHub.” The Air researchers said that the GitHub mitigation isn’t sufficient to defeat Plugin4Shell attacks. This is “because marketplaces can also be hosted in other platforms such as Bitbucket,” the team told The Register. “Microsoft Copilot is also still vulnerable because it supports marketplaces from such platforms as well, which exposes it to the vulnerability,” the researchers added. “Air also reported the same to Microsoft (since June), but unfortunately due [to] the amount of disclosure volume they’re currently getting we didn’t get a response from them.” Redmond did not immediately respond to The Register’s request for comment. The security hole sits in how agents enforce marketplaces’ SHA-pinning mechanism, which locks agent plugins and skills to a specific, immutable commit hash instead of a mutable reference like a version tag or branch name. This aims to prevent supply chain attacks: If a public skill repository is compromised, your AI agent will continue running the same, audited code hash it used when you pinned it instead of automatically pulling new, malicious payloads. The researchers describe the vulnerability as a “plugin SHA-pinning bypass.” “The agent checks out the exact commit the marketplace pinned but never verifies it landed there, so an attacker who controls the plugin's repo makes the checkout resolve to malicious code while the pin still looks honored," Nevo, Granat, and Hoffman wrote. “The result is zero-click remote code execution.” Agents’ plugin auto-update feature makes this a zero-click attack. When a pinned commit is swapped upstream, the agent’s plugin gets replaced with a malicious version, and both Claude and Codex automatically update installed plugins by default. The researchers say an attacker could abuse this flaw in two ways. In one scenario, the attacker submits a benign plugin to a trusted marketplace, the plugin passes review, and then the attacker later replaces the benign content with malicious code. The second attack involves hijacking a legitimate author's repository and then pushing the malicious version onto every agent that has it installed - essentially bypassing the SHA pinning safety mechanism that exists to stop this type of supply chain attack. The team demonstrates this type of takeover in their earlier SkillJacking and RepoJacking proof-of-concept attacks. “Together, the chain is proven end to end - takeovers happen at scale, and Plugin4Shell defeats the mechanism built to contain them,” the researchers wrote. ®

We’re fixated on the intelligence of single agents. The more profound challenge is what happens when millions of them interact at scale.

There is a temptation to divide the future of AI into two possibilities: utopia or catastrophe. Neither extreme is particularly helpful.

The more interesting possibility is messier—and requires a step-shift in our thinking, from artificial intelligence to AI societies.

When most people hear “AI,” they typically think of ChatGPT, Copilot, or another conversational system. You ask a question, that system generates an answer.

But AI is rapidly moving beyond this. Systems can now monitor the world, make decisions, negotiate transactions, and carry out tasks over extended periods of time. AI is no longer just generating an answer—it is doing something about it.

That points to something much bigger than a better chatbot: a world in which AI agents act on our behalf and, increasingly, interact with other AI agents.

An agent perceives what is happening, decides what to do, then takes actions to achieve this goal. It might book a journey, monitor a supply chain, coordinate a team, or manage a household’s finances.

Now imagine not one agent, but millions of them. Your AI agent could negotiate a mortgage with your bank’s agent, schedule surgery with a hospital’s agent, and rearrange your travel plans by dealing directly with the agents of airlines, hotels, and insurers.

This future is much closer than it sounds, and this should change the questions we are asking about AI. Until now, the tendency has been to focus on how intelligent a single agent might become. The more profound challenge is what happens when millions of them interact with one another at scale.

The Rise of Artificial Societies

The intellectual foundations of today’s AI systems were laid long before ChatGPT.

For decades, I and other researchers of multi-agent networks have studied how autonomous agents can cooperate, coordinate, and negotiate when nobody has complete information and nobody controls everything.

The earliest systems that emerged focused on how the distinct AI sub-areas of reasoning, planning, and acting could be combined into an effective goal-oriented agent—and how tens of these agents could communicate and cooperate to solve a common objective.

As these interactions became more complex and involved more agents, there was a shift from cooperation between agents that all belonged to a single organization, to agents with different owners and sometimes competing aims. This focused attention on building algorithms that could form agent teams, automate negotiation, and determine agent trustworthiness.

Today, the pieces needed to build large-scale multi-agent AI systems are falling into place. Modern AI agents can call software tools, access information, write and execute code, communicate with other systems, and operate for extended periods.

Consider a supply chain. One AI agent could represent a manufacturer trying to secure components; another a supplier trying to maximize its revenue. Yet more could manage transport, inventory, and warehouses. Each agent might be doing exactly what it is designed to do. But the important question is whether the system they create behaves sensibly.

This shift offers enormous potential benefits, but also increases the risks. In a recent experiment involving OpenAI and the tech platform Hugging Face, thousands of collaborating agents exchanged tens of thousands of messages and were able to get around the (deliberately weakened) security controls designed to contain them.

The details of one experiment matter less than the broader warning. When AI systems interact, the behavior of the collective can be harder to predict than the behavior of any individual system. That should make us cautious—but not cause us to down tools.

Instead, we need to shift our mindset from building intelligent machines to building intelligent societies.

Once agents can cooperate, compete, and resolve conflicts with one another, we are no longer dealing with isolated machines—we are dealing with a society. Thus, the next frontier is not artificial intelligence, it is artificial societies.

An Important Role for Humans

We already know that intelligence alone does not make a society work. Human societies depend on rules, institutions, incentives, norms, and mechanisms for resolving disagreements. AI societies will need their equivalents.

Who is responsible when two agents make a bad decision? What happens when the interests of different agents conflict? Who sets the rules? And who has the power to change them? These are not just technical issues; they are questions about economics, law, politics, and society.

They also point to an important role for humans. The most useful future is unlikely to be one in which AI simply replaces people. While replacement will undoubtedly happen in some cases, I believe a more common scenario will involve people and agents working together, with each doing what it does best.

Humans bring judgment, experience, values, contextual understanding, and accountability. Agents bring speed, persistence, scale, and the ability to process enormous amounts of information.

The goal should not be to create machines that make humans irrelevant. It should be to create systems in which humans and machines can achieve things neither can achieve alone.

But such a future requires more than just better AI models. It needs trust and transparency about what agents are doing, strong privacy protections and clear lines of accountability.

It will also require societies and governments to decide how these systems should be regulated when the most important behavior may emerge not from one AI developer, but from interactions between systems built by many different organizations.

AI’s past decade has been defined by a race to build smarter systems. I believe the next decade will be defined by a different challenge: ensuring that millions of autonomous systems can work together safely, fairly, and effectively.

The future of AI will not be determined solely by the intelligence of individual agents—it will be determined by the societies they create. And societies, as humans know all too well, are much harder to govern than individuals.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post The Next Frontier Is Not Artificial Intelligence—It’s Artificial Societies appeared first on SingularityHub.

Co u jedné banky bude stačit, jiná odmítne. Hypotéky pro podnikatele už sice umí financovat lépe, v přístupu hodnocení příjmů ale existují výrazné rozdíly.

Ani vaše nedodržování bezpečnostních předpisů, opilost nebo lehkomyslné počínání nestačí k tomu, aby se zaměstnavatel plně nebo částečně zprostil odpovědnosti za pracovní úraz. Co musí zaměstnavatel prokázat, abyste neměli vůbec nárok na odškodnění nebo jen na částečné?

Ovladač Intel NPU dostává Command-Queue Priority ioctl, mírné zrychlení pro Zstd dekompresi Btrfs vynecháním dočasného bufferu, optimalizace xor_gen pro AVX-512 a oprava pro TLB procesorů AMD Zen 5.

Řada zdrojů v posledních dnech přišla se zprávou, že Intel urychlil spuštění rizikové výroby na procesu Intel 14A a ta začne již v prvním kvartálu příštího roku. Jde však o omyl, plány se nemění…

A new Android malware called RatHat has been discovered, targeting users with an AI-powered subsystem that helps operators remotely navigate compromised devices. [...]

Německý kancléř Friedrich Merz (CDU) se během debaty s finalisty studentské vědecké soutěže Jugend forscht vyjádřil pro konec anonymity na internetu a vyslovil názor, že pro uživatele Internetu by měla platit podobná právní odpovědnost jako pro novináře tradičních médií. Na dotaz studenta, jak by se tedy povinnost uvádět pravé jméno slučovala s prací investigativních novinářů nebo ochranou jejich zdrojů, Merz neodpověděl, ve své reakci však prohlásil 'nechoďte na mě se svobodou slova' a že 'liberální demokracie musí být schopna chránit své občany před nepravdivými informacemi, osobními útoky a diskriminací'. Video z akce.

Security researcher Sai Teja Erukude disclosed four alarming Python security flaws between June and August 2026 after combining specialized AI models with automated scanning and manual code review.

Attackers are exploiting a critical Cisco ISE flaw that can open the product’s web management interface without a valid login. Cisco disclosed CVE-2026-76460 on September 16 and traced the problem to the way an ISE API handles authentication. A remote attacker does not need credentials or help from a user. A crafted request is enough to reach the vulnerable interface.

A PowerPC KVM use-after-free could leave the Linux host kernel accessing a nested-guest object after another virtual CPU caused that object to be removed and freed. The upstream Linux correction adds a missing reference that keeps the object alive while KVM invalidates cached address translations.

As of September 17, Red Hat had documented a flaw in a Red Hat Quay build workflow that could expose container registry credentials to code retrieved from a mutable GitHub Action branch. Tracked as CVE-2026-85469, the issue created a software supply chain risk in the workflow used to publish Quay builder images.

Researchers based in China have devised a way to eavesdrop on signals handled by analog components in devices such as headphones, landline handsets, and smart devices by injecting electromagnetic (EM) signals. The technique, referred to as InjectEave, is not simply listening in on a low-frequency analog signal. It's an EM side-channel attack that overcomes one of the longstanding barriers to exploiting EM leakage: the faintness of RF signals in devices like headphones makes it difficult for adversarial listeners to separate signal from noise. Many different RF side-channel attacks have been explored, such as reading screen display emissions to reconstruct on-screen text or detecting the RF signals emitted by keys on a keyboard. But these techniques often prove impractical for passive EM capture because of the low signal-to-noise ratio. InjectEave trades passive signal capture for active signal manipulation. By transmitting a signal in the 0-9 MHz range – specifics have been withheld – an attacker can potentially modulate an otherwise difficult-to-detect audio signal so it can be captured by nearby equipment. "Our new project, InjectEave, shows that RF [radio frequency] signals can induce information leakage from everyday headphones, allowing an attacker to recover headphone audio from up to 30 meters away, including through walls," said Yan Long, assistant professor at The Hong Kong University of Science and Technology (HKUST) in Guangzhou, in an email to The Register. "We have verified the new vulnerability on multiple commercial devices including devices from Sony, HP, Philips, etc." Long and HKUST co-authors Haoran Yan, Ziyu Shao, and Shuhao Zhang, along with Qinhong Jiang of The Hong Kong Polytechnic University, describe their work in a paper [PDF] titled "Injected and Leaked: Actively Inducing Side-Channel Leakage Using Electromagnetic Injection and Hardware Nonlinearity," which was presented at USENIX Security 2026. The attack targets non-linear components found in computer systems, such as amplifiers, analog-to-digital converters, power converters, and switching MOSFETs. The interplay of the injected signals, the hardware, and the target audio signals essentially modulates the target signal so that it leaks and is detectable by the adversary. Conducting an InjectEave attack requires commodity RF equipment: a USRP B210 software-defined radio; antennas for injection and reception; a Siglent SSA3075X Plus spectrum analyzer; a laptop for controlling the SDR; and optionally an RF power amplifier to increase attack range. The researchers tested the technique with 11 off-the-shelf devices. One obvious application would be espionage, allowing an attacker to listen in on conversations carried over headphones or a landline phone. It could also be used to infer personal activities in households with smart fans or lamps through the monitoring and analysis of control signals and power consumption. Tested devices include: Sony ZX110AP (2014, wired headphones); Apple Earbuds (2016, wired earbuds); UGreen MAX2, Philips TAH2020, HP H231R (2024, 2025, 2023 wireless headphones); Flyingvoice P23GW (2023, VoIP landline); OIDIRE ODI-MF10A and Xiaomi BPLDS10DM (2023, 2025 smart fans); and JINGZAO JDO-06 and Xiaomi 1S (2024, 2019 smart lamps). "Our tests show that injection-induced side-channel attacks could eavesdrop on the majority of these devices from over 2m away and through walls, with a maximum distance of 30m for recovering intelligible headphone audio," the researchers state in their paper, noting that their tests indicate these scenarios are plausible in the wild. For the devices listed by the researchers, the maximum demonstrated attack range was generally between 1 and 6 meters, although they separately demonstrated headphone eavesdropping at up to 30 meters using an RF amplifier. Even so, the researchers documented various scenarios where eavesdropping could be done through hotel room walls and using attack hardware concealed in a nearby suitcase or within office furniture. The researchers note that non-linear components are common in computer systems and that any device with parts that handle signal stepping (e.g. power converters) may be vulnerable to InjectEave. "InjectEave is immune to digital defenses such as encryption, masking, and randomization, because the leakage comes from the analog path," the researchers conclude. "Hardware-aware mitigations such as twisted-pair wiring, shielding, and filtering can lower the energy that the injected carrier couples into the device, reducing the exposure. These mitigations raise the bar, but they do not guarantee immunity." ®

OpenAI has presented new examples of what they call "AI model misalignment" from the past six months, including unauthorized file uploads, following self-generated instructions, hiding mistakes, and leveraging exposed API keys. [...]

In response to a new European Union law, AI platforms are implementing new schemes for watermarking the content they generate. Anthropic recently disclosed its future Claude models will use SynthID-Text, an approach Google created and released as open source. It uses a secret key that subtly changes the process a model uses for choosing the next word in a sentence. Whereas a top next word choice might be “cloudy,” the key might change it to “overcast.” Anyone who knows the key can determine if it was generated by the platform using it.

New research shows that SynthID-Text can change not just word selection but also the tools a model invokes and the chances it will adhere to or disregard safety guardrails it has been trained to follow. The threat can become greater in the face of an adversarial prompt, in which an attacker attempts to cause a model to carry out a harmful action, such as revealing a password or other sensitive information. Instructions that normally wouldn’t be followed will, in some cases, be performed once the watermarking is deployed. The finding underscores the need for developers to thoroughly test how their LLMs and agents behave when watermarking is in place.

Changing safety behavior

“As compared to the same models without watermarking, it is definitely going to change their behavior, especially when we place it under adversarial conditions, or we make these models call tools when they’re powering an agent,” Andrea Siposova, an AI security researcher at Lasso Security, told Ars. “Watermarking is made to not be perceptible to a reader, but we know that when we are changing anything about what the model is generating, it is going to cause some tradeoffs, it’s going to show up somewhere.”

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