U.S. cybersecurity agencies warn that threat actors are using AI-generated scripts to exploit Siemens S7 Series programmable logic controllers (PLCs) in U.S. critical infrastructure. PLCs are industrial computers used to automate and control machinery and physical processes in factories and other critical infrastructure. The NSA, CISA, FBI, Department of Energy, and Environmental Protection Agency issued the joint advisory Wednesday, saying the attacks are ongoing. "This advisory relates to an active threat to Siemens S7 Series programmable logic controllers (PLCs)," reads the advisory. "However, ongoing PLC targeting activity is broader than Siemens PLCs. All PLC owners and operators should apply relevant mitigations to reduce the risk to their devices and systems." The critical infrastructure sectors most targeted include Critical Manufacturing, Energy, Water and Wastewater Systems, Chemical, Food and Agriculture, and Commercial Facilities. The agencies also note that Siemens S7 PLCs are used in the Defense Industrial Base, which could also be targeted.
Most email defenses still do the job they did a decade ago. Scan the message, look for something malicious, block it. That worked when the danger sat in the payload, a bad link or an attachment. It stopped working when the danger moved into the message's intent, and it is failing now that the sender is no longer a person. From Bad Content to Bad Intent to AI on Both Sides Phishing 1.0 was bad
Researchers tested 31 million patterns to disrupt surveillance AI, with promising results but significant gaps between simulation and real-world use. The Kansas City-based cybersecurity researcher Bill Swearingen spent the past year doing something that sounds almost too simple to work: printing patterns, watching cameras fail to detect them, and repeating. TechCrunch reports that after roughly 31 million tests, he can now generate patterns on demand that block license plate readers and surveillance cameras from recognizing whatever the pattern covers, whether that’s a person or a vehicle. The project is called noRecognition, and the core idea isn’t stealth in the traditional sense. The camera still records everything just fine. What breaks is the detection layer sitting on top of the footage, the software that flags license plates, tracks faces, or spots “activity of interest” across thousands of hours of video. Swearingen’s patterns don’t hide you from the lens; they make the algorithm looking through that lens shrug and move on. Swearingen, co-founder of the SecKC meetup, said his project started for personal reasons. He became concerned about the growing number of surveillance cameras in his town and the possibility of being tracked while attending a protest. What started as a simple experiment later became a reinforcement learning system. He taught the model to create patterns, learn from failures and keep improving. Over time, it learned how to avoid detection by several camera systems. Every time a pattern failed and got detected, the system adjusted and tried again, eventually learning to defeat multiple detection algorithms simultaneously rather than just one at a time. The research dashboard behind the project, published at sandbox.norecognition.org, goes considerably deeper into the numbers than the headline claim suggests, and it’s refreshingly upfront about what’s proven versus what isn’t. The team states its overall objective plainly as “one pattern that defeats every detector,” and by their own account that goal remains only partially met. Their strongest validated result against a detector extracted directly from a real deployed surveillance camera sits at 61.7% non-detection across held-out test subjects, a solid number, but nowhere near total, and still a digital simulation rather than a real-world fabric test. That distinction matters more than it might seem. Most of the dashboard’s headline figures are explicitly labeled as digital, simulated results, meaning the pattern was tested against a virtual camera and printed ink model rather than an actual garment photographed by an actual camera in the field. The gap between “works in simulation” and “works when Donut Media wraps a real 2009 Toyota Yaris in it,” which is the physical test Swearingen ran live at DEF CON, is exactly the gap this kind of research has to close before anyone should treat it as a reliable, everyday privacy tool. “On Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen ran his first real-world test. With help from Donut Media, the test involved covering a 2009 Toyota Yaris with one of Swearingen’s newest patterns to see if the car would be invisible to detection by a Flock camera.” reports TechCrunch. “We proved it was effective,” said Swearingen, though the wheels were a challenge. The video of the demo will be out in the next few weeks, said Donut Media.” That DEF CON demo is where things got concrete. Swearingen covered a car in one of his newest patterns and tested it against a Flock Safety camera, the kind widely deployed for automated license plate reading across the US. He said the test proved effective, though the vehicle’s wheels turned out to be a persistent weak point, curved surfaces apparently don’t cooperate with flat printed patterns the way a car door does. Source Tech Crunch – A photo of a 2009 Toyota Yaris at the Def Con conference in Las Vegas, covered in a pattern made by Bill Swearingen, as part of a test to see if it can defeat surveillance camera detection. Image Credits:Bill Swearingen / Donut Media Swearingen is not publishing his best patterns because he does not want camera makers to easily find and block them. Instead, he is using crowdfunding to develop and sell printed products such as T-shirts and hoodies, with vehicle wraps possibly coming later. It is still unclear whether the project will become a practical privacy tool for everyday users or remain mainly a DEF CON demonstration. Its real effectiveness will depend on how well the patterns work on real clothing, in different weather and camera conditions. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Surveillance camera)
Two critical vulnerabilities impacting MLflow, an open-source artificial intelligence (AI) platform, and FUXA, an open-source, web-based SCADA / HMI software built for operational technology (OT) and industrial automation, are witnessing malicious scanning and exploitation efforts. According to independent reports from watchTowr and VulnCheck, the vulnerabilities in question are as follows -
A new Android NFC relay malware called WindRelay is being used alongside the SpyNote remote administration tool (RAT) to steal card data and send it to attackers in real time. In an incident investigated by the cybersecurity company Group-IB, a fraudster impersonated a bank employee and called the victim under the pretense of a problem with their payment card. During the call, the threat actor instructed the victim to sideload the SpyNote RAT disguised as a legitimate app and grant it Accessibility Service permissions, giving the attacker remote access to the Android device. To add credibility, the attacker personalized the malicious app label with the victim's name. After gaining remote access to the device through SpyNote, the attacker installed WindRelay without further interaction with the victim and used the banking app to take out a loan in the victim’s name.
Google says Chrome's anti-abuse systems reduced unwanted notifications on Android by more than 7 billion per day during the first quarter of 2026. In a new blog post, Google argues that notification abuse has increasingly been used to distribute scams, malware, phishing attempts, and fraudulent payment requests. To reduce the abuse, Google developed a "Swiss cheese" defense model, where several overlapping systems try to stop abuse at different stages. "Our goal is to ensure that if abuse slips through one layer, another is there to catch it," Google explained. "This approach allows us to halt abuse at the source, preventing deceptive content from reaching users while maintaining a healthy balance between utility and security." Chrome already removes notification permissions from inactive websites, as well as sites that repeatedly trigger suspicious-notification warnings.
A Malicious SIM Card Can Run Attacker Code Inside the Modems Behind Cellular IoT Devices thehackernews.com
A malicious SIM card can order the device it sits in to run commands of the attacker's choosing. On the cellular modules built into electric-vehicle chargers, industrial routers, and car telematics units, that is enough to take the whole device over. Researchers at the University of Birmingham and the security firm Fuzzware tested 26 phones and cellular modules for the capability, found it
North Korea's state hackers are no longer content to type prompts into public chatbots. One of the country's main espionage groups has begun running artificial intelligence (AI) offline on its own servers, connecting document-search tools to files in its possession, and collecting the software parts needed to build AI into its malware. South Korean security firm Genians says it uncovered the The incident highlights how adversaries continue to evolve their tradecraft, combining increasingly accessible tooling with targeted social engineering to slip past traditional perimeter defenses. Finally, maintain offline, tested backups and a clear communication plan so that business continuity decisions are made ahead of time rather than under pressure.
Cybersecurity researchers have called attention to an active "widespread email-driven phishing campaign" that employs adversary-in-the-middle (AitM) techniques to take control of Microsoft 365 accounts with an aim to identify key personnel involved in financial workflows and gather related email. "The campaign uses residential proxies to disguise malicious sign-ins as ordinary consumer traffic," Arctic Wolf Labs said. "Automated activity maintains compromised sessions at approximately eight-hour intervals." The activity is assessed to impact organizations across healthcare, education, manufacturing, government, and professional services sectors located in the U.S., Canada, and Europe. It shares tactical overlaps with Payroll Pirate attacks tracked by Microsoft under the moniker Storm-2755. Payroll Pirates is the designation assigned to a broader financially motivated threat cluster that involves hijacking the accounts of employees to reroute salary payments to attacker-controlled accounts. Some aspects of these campaigns have been documented since early 2025, with Microsoft tracking a related threat as Storm-2657.
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