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Doba, kdy byly digitální kompakty nejrozšířenějším typem fotoaparátu a jejich pořizovací cena byla podobná, jako je tomu dnes u mobilu střední třídy, je již dávno pryč. Na letošní rok však připadá jedno zajímavé jubileum: 30 let od uvedení prvního digitálního kompaktu Canon PowerShot.

Vyšetřování tragické havárie indického Boeingu 787 nabírá zpoždění • Detailní analýzu motorů a řídicích jednotek provádějí experti v USA • Záznamy odhalily záhadné vypnutí přívodu paliva těsně po startu

Anthropic said on Friday it will "abruptly disable" its most advanced artificial intelligence (AI) models, Claude Fable 5 and Mythos 5, for all users after the U.S. government ordered it to suspend access to the models for foreign nationals, whether inside or outside the U.S., citing national security concerns. The AI company said it received an order at 5:21 p.m. ET, instructing it to suspendRavie Lakshmananhttp://www.blogger.com/profile/[email protected]

Společnost Murena představila (YouTube) novou verzi 4.0 mobilního operačního systému /e/OS (Wikipedie) založeného na Androidu a LineageOS bez aplikací a služeb od Googlu.

NanoClaw, a secure agent framework, has partnered with supply chain platform JFrog to allow AI agents to fetch resources from JFrog's reviewed registries. Gavriel Cohen, creator of NanoClaw and co-founder of NanoCo AI, announced the tie-up on Thursday evening in San Francisco at a JFrog event that concluded with a World Cup watch party. Cohen explained that one of the features of Claw agents – OpenClaw and variations like NanoClaw – is that they can improve themselves by fetching tools and resources that they don't have. That works fine, he explained, when there's a manual approval process for accessing known local data. But it's not ideal for npm packages, even when the agent involved is sandboxed and isolated as it is in NanoClaw. Malicious code within a container may still be able to take harmful actions, even if the scope of potential activity is constrained. Developers, Cohen said, may not be familiar with a given package and it can take time to thoroughly assess whether a package is legitimate and uncompromised. "So we teamed up with JFrog and we integrated NanoClaw with JFrog's registries," said Cohen. The arrangement provides a way to reduce the agent's exposure to untrusted content. When the agent downloads new tools and libraries, the software comes from a vetted source. Cohen also announced the availability of what he called an agent factory, his company's homegrown system used to handle pull requests (PRs) using NanoClaw agents. The agent factory, he explained, is an attempt to triage pull requests, which have surged thanks to AI coding agents. "It's very easy now to point a coding agent at a repo and say, 'open a pull request for this repo,'" he explained. "And it's very difficult as a maintainer to tell the difference between a high quality contribution from somebody who's really using the open source project versus someone who's just trying to build up the reputation [using automated methods]. So to help us tackle this, we built an agent factory that helps us review every single contribution to NanoClaw." The agent factory is referred to as the PR Factory in the actual pull request. It's built with NanoClaw and hosted on exe.dev, a service that provides VMs with persistent storage. "When a PR opens, the factory spins up a dedicated worker agent for it, posts a thread to Slack, and the worker triages the change, reviews the diff, and proposes a test plan," Cohen explains in the documentation. "Nothing consequential happens on its own: merges, test runs, and credentialed GitHub actions each surface as an approval card in the thread, and only fire when a human clicks approve." Cohen acknowledged that some developers will think it's madness to process unsanitized PRs that could contain prompt injections or unsafe code. And he asked the assembled audience of developers how many had seen the phrase on the projected slide: "Never, ever, ever do this." Anyone who has spent time using and configuring AI agents in a development context has seen something of the sort in configuration files like Claude.md, which gets loaded as instructions to the underlying agent and model. "If you see something like this in the Claude.md file and the agent instructions say, 'Important: Never run drop database production,' it tells you two things. You know that that agent has deleted a production database before. And you know that it can actually still do it again. That's why the instruction is there." This elicited a knowing laugh from the audience. Cohen went on to say that the agent will do it again because instructions are not a way of enforcing security or safety. "Instructions help steer an agent AI towards valuable output, but it's not a safety mechanism," he said. "The only way to reliably prevent an agent from taking undesired action is not allowing it to take that action, not giving it the ability to take the action." That is the purpose of NanoClaw. ®

Ucelený přehled článků, zpráviček a diskusí za minulých 7 dní.

Pětatřicet kilo těžký humanoidní robot Pemba vyrobený čínskou společností Unitree Robotics stanul na vrcholu ekvádorské sopky Chimborazo. V plánu má výstupy i na další hory včetně Mount Everestu. Vše bez kyslíkového přístroje.

Pozoruhodný systém komunikace mezi ptačími rodiči a jejich nevylíhlými potomky slouží jako účinná ochrana nové generace před ničivými vedry.

Černé díry jsou sice ikonické objekty astrofyziky, ale se všemi svými absurdními nekonečny jsou v mnoha ohledech přítěží. Dvojice fyziků z Frankfurtu nabízí jako alternativu gravastary, které obsahují kromě běžné i temnou hmotu. Zhroucení do černé díry v nich zarazí jejich vlastní malý Velký třesk a vznik minivesmmíru, jehož rozpínání stabilizuje gravastar.

Extremely powerful large language models (LLMs) still operate as though they’re typing on a keyboard, processing workloads in a simple left-to-right fashion. But in locally-run, single-user scenarios, this sequential processing can leave graphics processing units (GPUs) and tensor processing units (TPUs) underutilized.

Google is betting that DiffusionGemma can get around this bottleneck. The new experimental open model generates text “exceptionally fast,” creating entire blocks of text simultaneously through diffusion techniques rather than through token-by-token processing. The company says this technique results in 4x faster inference compared to auto-regressive models that rely on sequential processing.

It can also save users money. Technology analyst Carmi Levy noted that existing pay-per-token monetization models “penalize the use of less than optimally efficient AI solutions.”

But DiffusionGemma “could herald a new generation of task-defined, efficient solutions that can enable expanded compute capacity without draining the operations budget,” he said.

A contrast to left-to-right processing

Built on Google’s Gemma 4 family and its Gemini Diffusion research, DiffusionGemma is a 26B mixture-of-experts (MoE) model designed to maximize text output generation.

It essentially shifts how models use hardware, giving processors a larger hunk of work each cycle so it can draft full 256-token paragraphs in sequence. This allows the model to generate text up to 4x faster on GPUs, Google claims. It activates only 3.8B parameters during inference, and, when quantized, can fit within 18GB VRAM on high-end consumer GPUs like Nvidia RTX 5090.

“It upgrades your model inference from a single, sequential typewriter to a massive printing press that stamps the entire block of text simultaneously,” Google research scientists Brendan O’Donoghue and Sebastian Flennerhag wrote in a blog post.

AI image generators begin with pure, random ‘visual noise’ and iteratively refine that into a finalized picture (what’s known as ‘diffusion’); DiffusionGemma applies this same process to text. It does not generate tokens in order, but begins with a “canvas of random placeholder tokens” that it processes in multiple passes, identifying the context tokens it feels are most relevant and using those to refine the rest.

The model has the ability to self-correct, using confidence scoring to re-evaluate tokens in the next pass. “The model iteratively refines its own output, allowing it to evaluate the entire text block at once to fix mistakes in real-time,” O’Donoghue and Flennerhag explained.

DiffusionGemma also has bidirectional attention, they wrote. “Generating 256 tokens in parallel with each forward pass allows every token to attend to all others.” This can be particularly helpful in domains that are non-linear in nature, such as mathematical graphs, code infilling, and in-line editing, they said.

DiffusionGemma is optimized across Nvidia’s hardware stack, making it compatible with consumer setups as well as with high-performance enterprise systems like Hopper and Blackwell.

Because it is released under the Apache 2.0 license, developers can freely use, modify, distribute, and commercialize the software using their preferred tools. It can be run on GPUs or in the cloud through Google Cloud Model Garden or Nvidia NIM, and is available on Hugging Face, GitHub, and vLLM, with support for the open-source library llama.cpp coming soon.

Key use cases

The model is particularly useful in local workflows that are “speed critical,” such as generation of non-linear text structures, and unlocks what Google calls “new patterns of model behavior” like multimodal understanding and generating and rendering code in near real-time.

Levy explained, “DiffusionGemma is particularly well suited for interactive coding and editing where its efficiency allows rapid processing and iterations,” noting that its ability to fit within 18GB of VRAM and its deployability on commonly available local GPUs can potentially benefit customer service-related workloads that lean heavily on real-time interaction and local processing.

“DiffusionGemma also incorporates a thinking mode that is especially adept at problem solving,” he said. For instance, the model was fine-tuned to play Sudoku, a typically challenging task for autoregressive models because each token depends on future tokens. This “rather handily” illustrates the model’s capability to solve more complex problems, Levy noted.

Limitations

Google freely admits that DiffusionGemma is geared to specific workflows, and there are “key trade-offs.”

The model is engineered for small batch size inferencing and low-latency, high-speed generation low-to-medium batch sizes on a “single capable accelerator.”

In high-QPS cloud serving environments, (where infrastructure is designed to handle tens or hundreds of thousands of requests per second with ultra-low latency), DiffusionGemma’s parallel coding “offers diminishing returns,” and can even result in higher serving costs, Google conceded. In addition, its overall output quality is lower than that of standard Gemma 4, which is built for apps demanding maximum quality.

However, Levy noted that while DiffusionGemma “can be less precise than other models in certain workloads,” subsequent refinement cycles could overcome this limitation.

While Google isn’t sharing runtime costs, it’s clear that this is an efficiency play, he added. “When deployed across the kinds of workloads that would optimally benefit from its architecture, DiffusionGemma seems to have the potential to reduce processing overhead and related costs,” he said.

This article originally appeared on InfoWorld.

Maine has taken its public data breach reporting portal offline after fraudulent breach disclosures were published on the state's website, prompting a review of procedures to prevent abuse in the future. [...]

Attackers took over more than 400 packages in the Arch User Repository (AUR) this week and rewrote their build scripts to install a credential stealer on any machine that built them. The malware is a Rust binary built to harvest developer secrets. When it lands with root, it can also load an eBPF rootkit to hide itself. The AUR is Arch Linux's community package collection, and it is separate Swati Khandelwalhttp://www.blogger.com/profile/[email protected]

One of the world’s most active ransomware groups exploited a critical vulnerability in Oracle’s PeopleSoft software suite and used it to target about 100 customers and extort at least one of them to pay up in exchange for not leaking stolen data, researchers said.

The group, tracked as ShinyHunters, had been exploiting the PeopleSoft vulnerability for more than two weeks before Oracle flagged it. CVE-2026-35273, as the vulnerability is tracked, carries a severity rating of 9.8 out of 10, making the former zero-day one of the year’s most critical vulnerabilities to be exploited.

Google’s Mandiant security team said it’s an SSRF (server-side request forgery), a vulnerability that allows attackers to send requests from a susceptible server to systems used by the targeted organization. Oracle said the SSRF is remotely exploitable, and the company has issued a stopgap mitigation but has yet to fully patch the flaw. Google has confirmed that victims are receiving extortion demands.

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V Arch User Repository (AUR) bylo kompromitováno přes 400 opomíjených balíčků (jejich seznam). Útočník do nich začlenil škodlivý npm balíček atomic-lockfile, který krade citlivá data uživatelů. Publikována byla předběžná analýza spouštěného malwaru deps.

A headline feature introduced in the latest release of Windows 11, 25H2 is Administrator Protection. The goal of this feature is to replace User Account Control (UAC) with a more robust and importantly, securable system to allow a local user to access administrator privileges only when necessary. This blog post will give a brief overview of the new feature, how it works and how it’s different from UAC. I’ll then describe some of the security research I undertook while it was in the insider preview builds on Windows 11. Finally I’ll detail one of the nine separate vulnerabilities that I found to bypass the feature to silently gain full administrator privileges. All the issues that I reported to Microsoft have been fixed, either prior to the feature being officially released (in optional update KB5067036) or as subsequent security bulletins. Note: As of 1st December 2025 the Administrator Protection feature has been disabled by Microsoft while an application compatibility issue is dealt with. The issue is unlikely to be related to anything described in this blog post so the analysis doesn’t change.

While our previous two blog posts provided technical recommendations for increasing the effort required by attackers to develop 0-click exploit chains, our experience finding, reporting and exploiting these vulnerabilities highlighted some broader issues in the Android ecosystem. This post describes the problems we encountered and recommendations for improvement. Audio Attack Surface The Dolby UDC is part of the 0-click attack surface of most Android devices because of audio transcription in the Google Messages application. Incoming audio messages are transcribed before a user interacts with the message. On Pixel 9, a second process com.google.android.tts also decodes incoming audio. Its purpose is not completely clear, but it seems to be related to making incoming messages searchable.

With the advent of a potential Dolby Unified Decoder RCE exploit, it seemed prudent to see what kind of Linux kernel drivers might be accessible from the resulting userland context, the mediacodec context. As per the AOSP documentation, the mediacodec SELinux context is intended to be a constrained (a.k.a sandboxed) context where non-secure software decoders are utilized. Nevertheless, using my DriverCartographer tool, I discovered an interesting device driver, /dev/bigwave that was accessible from the mediacodec SELinux context. BigWave is hardware present on the Pixel SOC that accelerates AV1 decoding tasks, which explains why it is accessible from the mediacodec context. As previous research has copiously affirmed, Android drivers for hardware devices are prime places to find powerful local privilege escalation bugs. The BigWave driver was no exception - across a couple hours of auditing the code, I discovered three separate bugs, including one that was powerful enough to escape the mediacodec sandbox and get kernel arbitrary read/write on the Pixel 9.

Over the past few years, several AI-powered features have been added to mobile phones that allow users to better search and understand their messages. One effect of this change is increased 0-click attack surface, as efficient analysis often requires message media to be decoded before the message is opened by the user. One such feature is audio transcription. Incoming SMS and RCS audio attachments received by Google Messages are now automatically decoded with no user interaction. As a result, audio decoders are now in the 0-click attack surface of most Android phones. I’ve spent a fair bit of time investigating these decoders, first reporting CVE-2025-49415 in the Monkey’s Audio codec on Samsung devices. Based on this research, the team reviewed the Dolby Unified Decoder, and Ivan Fratric and I reported CVE-2025-54957. This vulnerability is likely in the 0-click attack surface of most Android devices in use today. In parallel, Seth Jenkins investigated a driver accessible from the sandbox the decoder runs in on a Pixel 9, and reported CVE-2025-36934.

KYBERCENTRUM vydalo knihu ceského kryptologa a popularizátora Pavla Vondrušky, která dokazuje, jak muže veda o kódech a šifrách být fascinující a dobrodružná.
Kniha byla v drívejším vydání v edici OKO zcela vyprodána a nebylo ji možné získat.
Nyní je tedy možnost ji zakoupit v e-shopu KYBERCENTRA. Ale pozor k prodeji touto cestou bylo uvolnen pouze omezený pocet 200 kusu .

Kniha p?edního ?eského popularizátora kryptologie dokazuje, jak fascinující a dobrodružná m?že v?da o kódech a šifrách být.
Kniha vyšla v 2006 v nákladu 8000 ks a byla brzy zcela vyprodána.
Kniha nyní vyjde pomocí Crowdfundingu v rámci projektu Centra kybernetické bezpe?nosti, z. ú. (KyberCentrum).
Podpo?te tento projekt a stanete se vlastníci této knihy.