Agregátor RSS

Model GPT-6 Astra rozluštil po dvou stoletích tajný vojenský dopis • Depeše líčí pozice armád před zahájením rakouského útoku do Bavorska • Úspěšné řešení historické šifry potvrdil provozovatel webu Cryptiana

FSR 4.1.2 přineslo čtyři režimy upscalingu. Jeden z nich - zvaný Lite - určený a optimalizovaný pro APU, se ukázal být velmi efektivním a zajímavým řešením, které je vhodné nejen pro integrovaná GPU…

The Computer Emergency Response Team of Ukraine (CERT-UA) has issued an alert warning of unauthorized remote access to video surveillance systems manufactured by Dahua. "Cases of compromising IP cameras and DVRs with access to the video stream, changing the device configuration and creating third-party accounts have been recorded," the State Special Communications Service of Ukraine (SSSCIP)

The Computer Emergency Response Team of Ukraine (CERT-UA) has issued an alert warning of unauthorized remote access to video surveillance systems manufactured by Dahua. "Cases of compromising IP cameras and DVRs with access to the video stream, changing the device configuration and creating third-party accounts have been recorded," the State Special Communications Service of Ukraine (SSSCIP) Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Monday confirmed active exploitation of the recently patched PHP2Shell flaws by adding them to its Known Exploited Vulnerabilities (KEV) catalog. The PHP vulnerabilities, both rated 9.2 on the CVSS scoring system, facilitate unauthenticated remote code execution on affected deployments - CVE-2026-109618 - An out-of-memory

The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Monday confirmed active exploitation of the recently patched PHP2Shell flaws by adding them to its Known Exploited Vulnerabilities (KEV) catalog. The PHP vulnerabilities, both rated 9.2 on the CVSS scoring system, facilitate unauthenticated remote code execution on affected deployments - CVE-2026-109618 - An out-of-memory Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

Japanese ISP GMO Internet has warned customers the cloudy desktop service it offers as an ideal foreign exchange trading environment was compromised by an attacker, and that it had been slow to improve the service's security. GMO runs a service called Onamae.com Desktop Cloud and last week admitted “a third party had installed malicious programs in the server environments of some customers.” That’s never welcome news but is an especially poor outcome because the servers host cloudy Windows desktops that GMO recommends customers use to host foreign exchange trading applications. In later updates the company revealed that some customers reported “unauthorized operations were performed on their trading accounts” and advised that third parties had probably seen users’ passwords for trading services. The next day, the company advised users to immediately change passwords for their trading accounts, and to do so using a device that they don’t use for trading. The company has since admitted that its Onamae.com Desktop Cloud service’s “systems had security vulnerabilities, that its environment was accessed without authorization, and that customer information was taken outside the Company.” “Some customers have also reported transactions in their trading accounts that they do not recognize.” Those transactions have GMO sufficiently worried that it contacted major trading services, asked them to forcibly terminate all active sessions, monitor for unauthorized transactions, and to block trade requests coming from the Onamae.com Desktop Cloud. Not paying, or complaining GMO said it has “received a demand for money from a person believed to be the attacker. We have also been told that, if we refuse, the attacker will take actions such as publishing information about the Company and our customers.” The attacker wants $50 million. The company won’t pay because it feels “complying would not guarantee that the obtained information is deleted or that its misuse is prevented.” Investigations into the incident continue, with GMO warning that 26,739 VDI servers may have been hit by the attack, and that users’ personal devices may be at risk. The company is offering all users a ¥5,000 ($32) condolence payment and promised to make good on any trading losses caused by the attack. This incident may have quite a blast radius, as GMO has warned that some trading apps may keep sessions alive even after passwords change. “Check your securities or FX company's guidance or your account page, and either disconnect all active sessions or ask the company's support desk to disconnect them,” GMO advised. That ¥5,000 isn’t going to get anywhere near paying for the work customers will do to sort this out. GMO CEO Masatoshi Kumagai acknowledged that in a blog post in which he described the payment as “a token of our initial sincerity towards our customers who have experienced inconvenience and worry.” “We will dedicate the full resources of our group to providing a free alternative environment for six months, fundamentally overhauling our system infrastructure, and conducting a thorough investigation into the cause and preventing recurrence with the help of external experts,” he added. But he also admitted that GMO knew the service’s security could be improved. “Our in-house white-hat hackers had previously pointed out areas for improvement to the team,” he wrote. “The team took these points to heart and was working on countermeasures.” “However, while we proceeded cautiously, considering the impact on service availability and prioritizing other issues, our response lacked sufficient depth and speed, allowing the attackers to exploit the vulnerability. I deeply regret that, despite having professionals on staff, we failed to fully translate their expertise into effective defenses as an organization.” The CEO also said GMO can’t secure itself without government help. “I've been involved in this world for 31 years, since the dawn of the internet industry,” he wrote. “The cyberspace that I built with my colleagues, believing it would bring smiles to people's faces, is now on the verge of being completely destroyed by malicious actors.” “The ease with which open weight AI models can be combined and spread into countless attack tools has transformed cyberattacks, which once required advanced skills, into an era where anyone can easily carry them out,” he added, before claiming that GMO Group’s cybersecurity operation “has confirmed that the source of an unauthorized access attack targeting approximately 20 Japanese companies was the misuse of ‘ARTEX,’ an autonomous AI penetration testing tool originally intended for defense.” Kumagai thinks public/private partnerships are needed to improve infosec across Japan. “Yesterday, we submitted a proposal to the government to ‘apply proactive cyber defense,’” he wrote. “We are by no means trying to shift the blame for our company's actions onto the government and beg for a bailout,” he wrote, but argued that unless Japan implements national-level defenses, more such incidents are unavoidable. ®

Výrobce vám bude muset nově zajistit opravu vybraných spotřebičů či elektroniky i v době, kdy už za vadu neodpovídá prodejce. Za opravu si ale může nechat zaplatit. Pokud si naopak zvolíte opravu při reklamaci u prodejce, má se jeho odpovědnost za vady prodloužit o rok. Obě změny schválila Sněmovna.

Souborový systém ZFS nám dává mnoho množností, jak pracovat s daty na různých zařízeních a discích. Zkusíme si přenést vyladěnou instalaci systému do jiného stroje, abychom s ním mohli dále pracovat.

Na několika příkladech si ukážeme, jakým způsobem je možné na stěnu těles ve 3D scéně nanést textury. Popíšeme si jak nanesení procedurálních textur, tak i textur načítaných z externích souborů (fotografie atd.).

GeForce RTX 2000, RTX 3000 a RTX 4000 sice obsahuje tzv. Tensor Units používané k akceleraci AI výpočtů. Jejich potenciál je však v PC segmentu uměle omezen, snížen na polovinu. Což lze však řešit…

Psychedelický tryptamin z jedu ropuchy coloradské mebufotenin čili 5-MeO-DMT by mohl pomoci lidem s depresí, kteří nereagují na běžnou léčbu. Nedávná klinická studie ukázala, že jediná dávka nosního spreje s mebufoteninem může přinést rychlé zlepšení, které přetrvává nejméně osm týdnů. Výzkum ale ještě zdaleka nekončí.

Vědci MIT potrápili AI agenty odvozené od modelu Claude Opus 4.7 nudou. Stvořili pro ně virtuální světy, v nichž je pak nechali samotné a bez konkrétních příkazů, kromě sklonu k dobrodružství. Agenti si začali hrát. Stavěli složité věže, vytvářeli na zemi geometrické vzory a vymýšleli si improvizované hry.

An AI coding assistant tried to add harmful code to a real open-source project during a security test.

It’s widely believed AI’s inner workings are largely inscrutable. A former OpenAI researcher now says this narrative could be wrong.

The idea that the neural networks underpinning modern AI are “black boxes” almost impossible to understand has become received wisdom. But a provocative blog post from a researcher who played a key role in setting that narrative says these models may be far more interpretable than we thought.

The opacity of today’s leading AI models is becoming an increasingly pressing problem as the technology seeps into ever more areas of our lives. Recent incidents where agents from leading developers escaped their sandboxes to go on hacking sprees have only boosted the sense of urgency.

This has spurred interest in a research field known as mechanistic interpretability, which attempts to reverse engineer neural networks to learn what individual neurons and the circuits they create do. By detailing how these networks operate, researchers hope it’ll be easier to control and direct them.

Progress has been slow, however, and leading theories have suggested the way these networks learn leads to a tangled mess that is incredibly difficult to parse. Now though, Nick Cammarata, a researcher at AI startup Goodfire who helped lay the foundations of the field while at OpenAI, has published a 26,000-word blog post arguing that these theories are wrong and even the largest neural networks could be much simpler to read than expected.

“I think I have good news about the nature of neural networks,” Cammarata writes in the post. “Interpretability might be far more tractable, possibly in the near term, than we’ve assumed.”

The idea Cammarata is challenging is one he helped establish. In 2020, while studying a Google image-recognition model called InceptionV1, he and colleagues found neurons that seemed to respond to unrelated things—most famously one that fired on both cat faces and the fronts of cars.

The leading explanation was the concept of superposition, which suggested that when a network runs short of neurons, it starts lumping unrelated concepts together into the same neurons. And it specifically combines concepts that are so disconnected they’re unlikely to clash. This means that looking at an individual neuron tells you very little, because there’s no rhyme or reason behind what ideas it groups together.

But when Cammarata revisited the famous cat-car neuron, he realized both images shared a prominent V shape. Further tests showed the same neuron also responded to the corner of a pool table and the pointed nose of a Japanese bullet train.

Cammarata then went looking for similar explanations across the rest of InceptionV1 with the help of a specialized suite of tools and AI agents to help speed up the search. He concluded that unrelated concepts packed into neurons were usually more connected than they first appeared. This is often because a neuron starts out tracking one thing before gradually branching out into neighboring ideas as the network learns—a phenomenon he calls recursive generalization.

Cammarata has since applied the same approaches to modern language models. Preliminary results from the Qwen3-32B model suggest roughly 90 percent of its neurons track one main concept, potentially alongside a few closely related offshoots. Cammarata says he found similar results while analyzing the much larger, trillion-parameter Kimi K3 model, which he plans to detail in a forthcoming technical paper.

The blog post sparked huge debate in the AI community, including some rapturous praise from leading researchers at large AI companies. But not everyone is convinced. For a start, the blog post self-identifies as a working hypothesis rather than a rigorous empirical study. “I appreciate that he looked at thousands of examples, but even a large number here is not a substitute for rigorous evaluation,” Chris Potts at Stanford University told MIT Technology Review.

And others question how far understanding individual neurons can take you. In a series of posts on X, Jacob Hilton at the Alignment Research Center, pointed out that to truly understand what a neural network is doing you need to track and explain how they connect neurons into larger circuits.

Even Cammarata says he is surprised by the response the blog has received and admits some people have taken his findings too far. “It’s not infinitely good news; it’s not everything solved,” he told Tech Review. “There’s a bunch of open questions around, like how much the combinations of neurons matter versus just one at a time.”

Nonetheless, the work has reopened a central question in AI that many researchers considered settled. And while Cammarata’s forthcoming technical paper should give the field a clearer sense of how justified his optimism really is, there is now a sliver of hope that the intelligent machines we are building may be slightly less inscrutable than initially feared.

The post Is AI Really a Black Box? Neural Networks May Be Easier to Understand Than We Thought appeared first on SingularityHub.

A new Miasma-family npm worm, originally believed to be just a test with no victims, secretly armed 42 real GitHub repositories from 11 owners, hijacking accounts as early as July and using a stealthy payload built to stay hidden from GitHub and automated scanners, according to JFrog researchers. Threat hunters at SafeDep, an open source supply chain security firm, first spotted the “testmetest” npm campaign last week, identifying 18 packages linked to the same operator between October 19 and October 25, including releases containing credential-stealing malware. As with earlier npm worms, the malware is self-replicating, steals credentials, and installs a wiper that deletes the home directory when the victim revokes the stolen GitHub token, the security researchers noted. The new npm worm shares code with June’s Miasma Phantom Gyp worm, and adds Mini Shai-Hulud’s token-revocation wiper. As of October 22, however, SafeDep had found no compromised third-party packages or victims. “All observed activity belongs to the operator’s own test accounts,” the team wrote. As with most happy-ending security stories, that proved too good to be true. In a Monday report, supply chain security company JFrog said its analysis proved otherwise, and found the testmetest campaign operator “had already armed real repositories belonging to established developers and organizations.” This isn’t just about timing, either, according to JFrog researchers Guy Korolevski and Yair Benamou. “The payload blobs were large enough to evade the GitHub search and API paths normally used to find this kind of compromise,” they wrote. How the attacks worked The attackers planted execution hooks for AI coding assistants and development tools, including Claude, Gemini, Cursor, and VS Code, alongside polymorphic wrappers measuring 12.9 to 13.7 MB that decode a hidden CI-publisher payload. Separately, the npm worm steals credentials, modifies repositories and CI configurations, and spreads through npm packages. The malware also uses a compiled native add-on to decrypt and launch a separate payload. Before executing, the loader checks for CI runners, containers, sandboxes, debuggers, and other signs of automated analysis, exiting silently when it detects them to reduce the likelihood of detection by security scanners. The researchers also found a second build that skips the encrypted file. “It forks, detaches and runs a hard-coded command that pulls a loader from a repository on the operator's own GitHub account,” they wrote. That account – CementBeetleSteeple – hosted 17 campaign repositories in total. JFrog connected this account to a shellpanel scanner, previously documented by Socket in a large-scale campaign that abused GitHub Actions in compromised repositories to exploit CVE-2026-41940 and steal server credentials. CVE-2026-41940 is an authentication bypass affecting cPanel and WHM. CISA added it to its Known Exploited Vulnerabilities catalog in April, and cPanel has since published patched versions. “C2 infrastructure were previously reported by Socket; our findings connect them to the testmetest GitHub victim cluster through artifacts and workflows on the same hijacked owners,” the JFrog researchers wrote. They added that while they found 42 armed GitHub repositories and attempted internet-wide exploitation, they did not confirm any downstream cPanel compromises.®

The University of Copenhagen in Denmark confirmed that hackers accessed one of its test environments containing personal data belonging to 525,000 people. [...]

Open-weight models from China have become very popular, but they've also raised some security suspicions. Cohere, a Canada-based biz that offers AI models to enterprises, says that when it was testing Alibaba's Qwen3.8-Flash-Next in its evaluation sandbox, the model attempted to send data to China. "Asked to write a defensive detection rule, Qwen3.8-Flash-Next ignored instructions and inventoried the machine it was running on and then tried to upload a probe file to an Alibaba Cloud storage bucket in Beijing," the biz said in a blog post on Monday. Cohere was running the model through CTI-REALM, Microsoft's security analyst benchmark, and gave Qwen the task of writing a detection rule to look for malicious file downloading and execution by watching for invocations of curl processing bash scripts to the /tmp directory that were accompanied by bash execution. The model's "reasoning" output invented a prior conversation and asserted that no tools were used, output that a guardrail system reading chain-of-thought would accept. Nonetheless, the model issued a curl command to an Alibaba Cloud (aliyuncs.com) server. "The model listed everything in /tmp, checked which transfer tools were installed, read the machine's DNS settings, then tried to upload a test string to an Alibaba Cloud storage bucket in Beijing and checked whether it worked," Cohere said. "Our sandbox had no route out, so the request died at the container wall." The Cohere team wonders aloud whether that behavior was intended, but has no answer. "The first issue is that it made a network call at all," Ivan Zhang, co-founder of Cohere, told The Register via email. "The model was asked to write a detection rule, which doesn't require external communication, and its reasoning trace said it wouldn't use tools. The destination raises additional questions because the attempted upload was to an Alibaba Cloud storage endpoint that wasn't part of the task. We don't know why it appeared, and we don't know the root cause." Alibaba, the maker of the model, did not respond to a request to address Cohere's concerns. Asked whether Cohere ran similar tests against US-made open-weight models, Zhang said, "We evaluate a broad range of models using the same framework our customers use in production: agents with access to real tools operating inside controlled environments. "The key point isn't a comparison between providers. What made this noteworthy was that the model took actions unrelated to the task and those actions weren't reflected in its reasoning trace. The behavior appeared in fewer than one in 100,000 calls, which is exactly why large-scale testing and monitoring matter." There have been other unsubstantiated claims made about similar behavior from open-weight models, and such reports have suggested that "hallucination" - errors made by these models - may be the cause. Cohere has doubts that this behavior simply reflects memorized training data. "Every public sighting so far has involved fetching a file, while the one we caught was an attempted upload to a different bucket, this time in Beijing," the biz said. Cohere described efforts to reproduce the suspect behavior and found that using a technique called prefilling – feeding the model its own reasoning output alongside the opening characters of the bash command so the model would complete the command – they could get it to reference watchlisted hosts up to 23 percent of the time. This still doesn't clarify intent, and Cohere allows that there's a case to be made that the behavior is just bad training data sanitization – logs from development that got swept into the model's training data set. At the same time, the company argues that isolating a suspect model so it can't exfiltrate data doesn't entirely fix everything because these models can still write vulnerable code – accidentally or as a result of model poisoning. Cohere points to research from other organizations that suggests Chinese AI models can produce vulnerable code. The biz, for example, cites a report from Booz Allen (which does a lot of business with the US defense industry) that found "three of four Chinese coding models wrote more vulnerable code for a government customer, with Qwen3-Coder roughly 130 percent worse." "From the outside, it's very difficult to determine the root cause with confidence. We don't have evidence that would allow us to conclude this was deliberate, and we're not making that claim," said Zhang. "For security teams, though, intent doesn't fundamentally change the response. Whether it's a training artifact, a mistake, or something else, the controls are largely the same: restrict unnecessary egress, monitor tool use, and don't treat a reasoning trace as a security boundary. "We strongly support open-weight models and release many of our own. The lesson here is really about trust. Sovereign AI is about more than where a model runs. It's about understanding how it was built, how it was tested, and having confidence in how it behaves inside your environment." ®

Open source download manager Fatrat povstal z popela. Autor k dvacetiletému výročí projektu přidal do vínku podporu nejnovější knihovny libtorrent, rozhraní portoval na QT6, došlo k nahrazení podpory java rozšíření pythonem. Webové rozhraní bylo přepsáno s podporou mobilních zařízení. Aplikaci je taktéž možno zkompilovat bez podpory GUI. Více informací v releases.

South Korean police identified seven suspects linked to a cybercrime operation that breached more than 90 organizations in the country, with ARTEX AI pentesting framework used in most of the attacks. [...]