New Report Highlights the Internet as the Primary Threat to Industrial Automation Systems  GBHackers News

Exposing a person's phone number could result in different security and privacy risks, experts warn.

Over 150 US Government Database Servers Exposed to the Internet - New Report  CybersecurityNews

Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of this approach lies in its ability to maintain structural coherence across the image while allowing for high levels of control during the generation process. As researchers began to apply these techniques to visual data, they found that structured prediction not only preserved spatial integrity but also supported tasks like image manipulation and multimodal translation effectively. Despite these benefits, generating high-resolution images remains computationally expensive and slow. A primary issue is the number of tokens needed to represent complex visuals. Raster-scan methods that flatten 2D images into linear sequences require thousands of tokens for detailed images, resulting in long inference times and high memory consumption. Models like Infinity need over 10,000 tokens for a 1024×1024 image. This becomes unsustainable for real-time applications or when scaling to more extensive datasets. Reducing the token burden while preserving or improving output quality has become a pressing challenge. Efforts to mitigate token inflation have led to innovations like next-scale prediction seen in VAR and FlexVAR. These models create images by predicting progressively finer scales, which imitates the human tendency to sketch rough outlines before adding detail. However, they still rely on hundreds of tokens—680 in the case of VAR and FlexVAR for 256×256 images. Moreover, approaches like TiTok and FlexTok use 1D tokenization to compress spatial redundancy, but they often fail to scale efficiently. For example, FlexTok’s gFID increases from 1.9 at 32 tokens to 2.5 at 256 tokens, highlighting a degradation in output quality as the token count grows. Researchers from ByteDance introduced DetailFlow, a 1D autoregressive image generation framework. This method arranges token sequences from global to fine detail using a process called next-detail prediction. Unlike traditional 2D raster-scan or scale-based techniques, DetailFlow employs a 1D tokenizer trained on progressively degraded images. This design allows the model to prioritize foundational image structures before refining visual details. By mapping tokens directly to resolution levels, DetailFlow significantly reduces token requirements, enabling images to be generated in a semantically ordered, coarse-to-fine manner. The mechanism in DetailFlow centers on a 1D latent space where each token contributes incrementally more detail. Earlier tokens encode global features, while later tokens refine specific visual aspects. To train this, the researchers created a resolution mapping function that links token count to target resolution. During training, the model is exposed to images of varying quality levels and learns to predict progressively higher-resolution outputs as more tokens are introduced. It also implements parallel token prediction by grouping sequences and predicting entire sets at once. Since parallel prediction can introduce sampling errors, a self-correction mechanism was integrated. This system perturbs certain tokens during training and teaches subsequent tokens to compensate, ensuring that final images maintain structural and visual integrity. The results from the experiments on the ImageNet 256×256 benchmark were noteworthy. DetailFlow achieved a gFID score of 2.96 using only 128 tokens, outperforming VAR at 3.3 and FlexVAR at 3.05, both of which used 680 tokens. Even more impressive, DetailFlow-64 reached a gFID of 2.62 using 512 tokens. In terms of speed, it delivered nearly double the inference rate of VAR and FlexVAR. A further ablation study confirmed that the self-correction training and semantic ordering of tokens substantially improved output quality. For example, enabling self-correction dropped the gFID from 4.11 to 3.68 in one setting. These metrics demonstrate both higher quality and faster generation compared to established models. By focusing on semantic structure and reducing redundancy, DetailFlow presents a viable solution to long-standing issues in autoregressive image generation. The method’s coarse-to-fine approach, efficient parallel decoding, and ability to self-correct highlight how architectural innovations can address performance and scalability limitations. Through their structured use of 1D tokens, the researchers from ByteDance have demonstrated a model that maintains high image fidelity while significantly reducing computational load, making it a valuable addition to image synthesis research. Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Teaching AI to Say ‘I Don’t Know’: A New Dataset Mitigates Hallucinations from Reinforcement FinetuningNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces LLaDA-V: A Purely Diffusion-Based Multimodal Large Language Model for Visual Instruction Tuning and Multimodal ReasoningNikhilhttps://www.marktechpost.com/author/nikhil0980/NVIDIA AI Introduces Fast-dLLM: A Training-Free Framework That Brings KV Caching and Parallel Decoding to Diffusion LLMsNikhilhttps://www.marktechpost.com/author/nikhil0980/Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation

Published June 5, 2025 10:00am EDT close Don’t be so quick to click that Google calendar invite. It could be a hacker’s trap Cybercriminals are sending fake meeting invitations that seem legitimate. NEWYou can now listen to Fox News articles! Americans’ personal data is now spread across more digital platforms than ever. From online shopping habits to fitness tracking logs, personal information ends up in hundreds of company databases. While most people worry about social media leaks or email hacks, a far less visible threat comes from data brokers.I still find it hard to believe that companies like this are allowed to operate with so little legal scrutiny. These firms trade in personal information without our knowledge or consent. What baffles me even more is that they aren’t serious about protecting the one thing that is central to their business model: data. Just last year, we saw news of a massive data breach at a data broker called National Public Data, which exposed 2.7 billion records. And now another data broker, LexisNexis, a major name in the industry, has reported a significant breach that exposed sensitive information from more than 364,000 people. A hacker at work (Kurt "CyberGuy" Knutsson)LexisNexis breach went undetected for months after holiday hackLexisNexis filed a notice with the Maine attorney general revealing that a hacker accessed consumer data through a third-party software development platform. The breach happened on Dec. 25, 2024, but the company only discovered it months later. LexisNexis was alerted on April 1, 2025, by an unnamed individual who claimed to have found sensitive files. It remains unclear whether this person was responsible for the breach or merely came across the exposed data.MASSIVE DATA BREACH EXPOSES 184 MILLION PASSWORDS AND LOGINSA spokesperson for LexisNexis confirmed that the hacker gained access to the company’s GitHub account. This is a platform commonly used by developers to store and collaborate on code. Security guidelines repeatedly warn against storing sensitive information in such repositories; however, mistakes such as exposed access tokens and personal data files continue to occur.The stolen data varies from person to person but includes full names, birthdates, phone numbers, mailing and email addresses, Social Security numbers and driver's license numbers. LexisNexis has not confirmed whether it received any ransom demand or had further contact with the attacker. An individual working on their laptop (Kurt "CyberGuy" Knutsson)Why the LexisNexis hack is a bigger threat than you realizeLexisNexis isn’t a household name for most people, but it plays a major role in how personal data is harvested and used behind the scenes. The company pulls information from a wide range of sources, compiling detailed profiles that help other businesses assess risk and detect fraud. Its clients include banks, insurance companies and government agencies.In 2023, the New York Times reported that several car manufacturers had been sharing driving data with LexisNexis without notifying vehicle owners. That information was then sold to insurance companies, which used it to adjust premiums based on individual driving behavior. The story made one thing clear. LexisNexis has access to a staggering amount of personal detail, even from people who have never willingly engaged with the company.Law enforcement also uses LexisNexis tools to dig up information on suspects. These systems offer access to phone records, home addresses and other historical data. While such tools might assist in investigations, they also highlight a serious issue. When this much sensitive information is concentrated in one place, it becomes a single point of failure. And as the recent breach shows, that failure is no longer hypothetical. A hacker at work (Kurt "CyberGuy" Knutsson)7 expert tips to protect your personal data after a data broker breachKeeping your personal data safe online can feel overwhelming, but a few practical steps can make a big difference in protecting your privacy and reducing your digital footprint. Here are 7 effective ways to take control of your information and keep it out of the wrong hands:1. Remove your data from the internet: The most effective way to take control of your data and avoid data brokers from selling it is to opt for data removal services. While no service promises to remove all your data from the internet, having a removal service is great if you want to constantly monitor and automate the process of removing your information from hundreds of sites continuously over a longer period of time. Check out my top picks for data removal services here.Get a free scan to find out if your personal information is already out on the web.2. Review privacy settings: Take a few minutes to explore the privacy and security settings on the services you use. For example, limit who can see your social media posts, disable unnecessary location-sharing on your phone and consider turning off ad personalization on accounts like Google and Facebook. Most browsers let you block third-party cookies or clear tracking data. The FTC suggests comparing the privacy notices of different sites and apps and choosing ones that let you opt out of sharing when possible.3. Use privacy-friendly tools: Install browser extensions or plugins that block ads and trackers (such as uBlock Origin or Privacy Badger). You might switch to a more private search engine (like DuckDuckGo or Brave) that doesn’t log your queries. Consider using a browser’s "incognito" or private mode when you don’t want your history saved, and regularly clear your cookies and cache. Even small habits, like logging out of accounts when not in use or using a password manager, make you less trackable.GET FOX BUSINESS ON THE GO BY CLICKING HERE4. Beware of phishing links and use strong antivirus software: Scammers may try to get access to your financial details and other important data using phishing links. The best way to safeguard yourself from malicious links is to have antivirus software installed on all your devices. This protection can also alert you to phishing emails and ransomware scams, keeping your personal information and digital assets safe. Get my picks for the best 2025 antivirus protection winners for your Windows, Mac, Android and iOS devices.5. Be cautious with personal data: Think twice before sharing extra details. Don’t fill out online surveys or quizzes that ask for personal or financial information unless you trust the source. Create separate email addresses for sign-ups (so marketing emails don’t go to your main inbox). Only download apps from official stores and check app permissions.6. Opt out of data broker lists: Many data brokers offer ways to opt out or delete your information, though it can be a tedious process. For example, there are sites like Privacy Rights Clearinghouse or the Whitepages opt-out page that list popular brokers and their opt-out procedures. The FTC’s consumer guide, "Your Guide to Protecting Your Privacy Online," includes tips on opting out of targeted ads and removing yourself from people-search databases. Keep in mind you may have to repeat this every few months.7. Be wary of mailbox communications: Bad actors may also try to scam you through snail mail. The data leak gives them access to your address. They may impersonate people or brands you know and use themes that require urgent attention, such as missed deliveries, account suspensions and security alerts.Kurt’s key takeawayFor many, the LexisNexis breach may be the first time they realize just how much of their data is in circulation. Unlike a social media platform or a bank, there is no clear customer relationship with a data broker, and that makes it harder to demand transparency. This incident should prompt serious discussion around what kind of oversight is necessary in industries that operate in the shadows. A more informed public and stronger regulation may be the only things standing between personal data and permanent exposure.CLICK HERE TO GET THE FOX NEWS APPShould companies be allowed to sell your personal information without your consent? Let us know by writing us atCyberguy.com/Contact.For more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/Newsletter.Ask Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers to the most-asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com. All rights reserved. Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.

The hole was quickly plugged, but millions of records may have been affected.

35,000 Solar Power Systems Exposed To Internet Are Vulnerable To Cyberattacks  CybersecurityNews

이메일 보안 기업 애브노멀AI(Abnormal AI)는 최근 보고서를 통해 VEC 공격이 기술적 취약점이 아닌 사람의 신뢰를 악용하는 방식으로 기존 방어 체계를 뚫고 있다고 진단했다. 보고서에 따르면 대기업 직원의 72%가 링크나 첨부 파일이 없는 사기성 벤더 이메일에 응답하거나 전달한 경험이 있는 것으로 나타났다. VEC 공격은 지난 1년간 전 세계적으로 3억 달러 이상의 도난 시도를 유발했으며,

Tapjoy studies have shown that mobile gamers prefer rewarded ads to interstitials 4-to-1. It’s a valuable insight for advertisers and developers alike when it comes maximizing ad revenue, but rewarded ads also have the potential to impact other parts of the user experience and your game’s performance overall.To ensure a healthy portfolio, it’s critical that developers understand the additional impact rewarded ads have beyond ad revenue. This includes their effect on metrics like in-app purchase conversion rate, average user spend, 30-day retention, and daily session count. To find out more, we conducted an in-depth analysis of eight different high-DAU apps across iOS and Android for several varying timeframes to find out how users behave when exposed to rewarded ads compared to those who aren’t.Higher IAP conversion ratesWe studied new app users during a one month period and segmented them into two different groups: Those who engaged with at least one rewarded ad and those who never engaged with an ad. We then compared the IAP conversion rates (or percentage of users that made a first time purchase) for each group.On average, users who engage with rewarded ads are 4.5 times more likely to make in-app purchase versus those who do not.7 out of the 8 of apps studied demonstrated higher conversion rates among those who engaged with ads versus those who did not.In the case of two of the apps studies, we found that users who engaged with an ad were over 9 times more likely to make a purchase.Higher average spend per userWe measured the average spend per user in each app for seven days before and seven days after a user’s first rewarded ad engagement.In all 8 apps studied, user spend increased significantly after they engaged with an ad. The average weighted increase in user spend was 326%. Among the apps studied, the boost in average spend per user ranged from just shy of 200% to over 500%.Increased 30-day retentionWe measured the 30-day retention rates of users who engaged with 1-6 rewarded ads during their first week of using an app. Three types of rewarded ad formats were studied (rewarded video, full-screen interstitial, and offerwall placements) and measured against the average 30-day retention benchmark for all apps.Players who complete just one rewarded ad in the first week — whether a video, full-screen interstitial or an offerwall engagement — have a retention rate of at least 50%, compared to the benchmark of 13%.Rewarded video has the most profound effect on retention of any ad type. 30-day retention steadily increases with each video view, ranging from 53% to 68%, which is 3.5-5 times greater than the benchmark.Higher average daily session countWe measured the average number of user sessions per day — both seven days before and seven days after a user’s first rewarded ad engagement — for each high-DAU app.In all cases, users engaged with apps more frequently after completing a rewarded ad.All 8 apps studied demonstrated a lift in the average number of user sessions among those who completed an ad.The average weighted increase in user sessions across all apps was 34%.What does this mean for developers?4 key monetization strategies became abundantly clear following our research:Make rewarded ads easy to find – To increase visibility, consider adding a button to your app’s home screen or storefront, or utilize in-app messaging or push notifications to promote rewarded offers. Tapjoy’s Native-to-Earn, Message-to-Earn, and Push-to-Earn features make it easy for developers to add and manage these additions right from the dashboard.Explore currency sale promotions – Try running limited-time offers in which your players earn more currency than usual for every rewarded ad they complete. These currency sales can drive serious spikes in revenue. With Tapjoy, it’s easy to personalize your currency sale with custom branding and to control the payout ratio.Introduce ads early – Getting users to interact with ads early in their gameplay increases the chance that they will continue to engage with or make a purchase in your app. Rewarded ads introduce players to the mechanics and benefits of your in-app currency, so it’s important to make ads visible and easy to access during the player’s first few sessions.Integrate multiple rewarded formats – Integrating multiple formats — such as rewarded video and offerwall combined — provides developers with the best opportunity to monetize their users and maximize revenue. Diversifying formats not only unlocks higher eCPMs, but it provides players with the freedom to pick and choose the rewarded offer that appeals most to them.

alternative_right shares a report from Phys.Org: Using conventional propulsion and low-energy trajectories, it takes six to nine months for crewed spacecraft to reach Mars. These durations complicate mission design and technology requirements and raise health and safety concerns since crews will be exposed to extended periods in microgravity and heightened exposure to cosmic radiation. Traditionally, mission designers have recommended nuclear-electric or nuclear-thermal propulsion (NEP/NTP), which could shorten trips to just 3 months. In a recent study, a UCSB physics researcher identified two trajectories that could reduce transits to Mars using the Starship to between 90 and 104 days. The study was authored by Jack Kingdon, a graduate student researcher in the Physics Department at the University of California, Santa Barbara (UCSB). He is also a member of the UCSB Weld Lab, an experimental ultracold atomic physics group that uses quantum degenerate gases to explore quantum mechanical phenomena. [...] As outlined on its website, conference presentations, and user manual, the SpaceX mission architecture consists of six Starships traveling to Mars. Four of these spacecraft will haul 400 metric tons (440 U.S. tons) of cargo while two will transport 200 passengers. Based on the Block 2 design, which has a 1,500 metric ton (1,650 U.S. ton) propellant capacity, the crewed Starships will require 15 tankers to fully refuel in low Earth orbit (LEO). The cargo ships would require only four, since they would be sent on longer low-energy trajectories. Once the flotilla arrives at Mars, the Starships will refuel using propellant created in situ using local carbon dioxide and water ice. When the return window approaches, one of the crew ships and 3-4 cargo ships will refuel and then launch into a low Mars orbit (LMO). The cargo ships will then transfer the majority of their propellant to the crew ship and return to the surface of Mars. The crew ship would then depart for Earth, and the process could be repeated for the other crew ship. Kingdon calculated multiple trajectories using a Lambert Solver, which produces the shortest elliptical arc in two-body problem equations (aka Lambert's problem). The first would depart Earth on April 30th, 2033, taking advantage of the 26-month periodic alignment between Earth and Mars. The transit would last 90 days, with the crew returning to Earth after another 90-day transit by July 2nd, 2035. The second would depart Earth on July 15th, 2035, and return to Earth after a 104-day transit on December 5th, 2037. As Kingdon explained, the former trajectory is the most likely to succeed: "The optimal trajectory is the 2033 trajectory -- it has the lowest fuel requirements for the fastest transit time. A note that may not be obvious to the layreader is that Starship can very easily reach Mars in ~3 months -- in fact, it can in any launch window, over a fairly wide range of trajectories. However, Starship may impact the Martian atmosphere too fast (although we do not know, and likely SpaceX don't either actually how fast Starship can hit the Martian atmosphere and survive). The trajectories discussed are ones that I am confident Starship will survive." The paper describing the work has been published in the journal Scientific Reports. Read more of this story at Slashdot.