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
Cybersecurity researchers have flagged a global cybercrime operation that abuses thousands of hacked WordPress websites as infrastructure to disseminate malware, commandeer infected hosts, store stolen documents, screenshots, and activity logs created to track the status of the activity. "The operation doesn't rely on a single piece of malware, but on a whole toolkit of criminal software
The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Tuesday added four critical vulnerabilities to its Known Exploited Vulnerabilities (KEV) catalog, stating they are being exploited in the wild. The shortcomings added to the KEV catalog are listed below - CVE-2026-65400 (CVSS score: 9.8) - An improper authentication vulnerability impacting Apple macOS that could allow an
Microsoft Defender Experts have linked more than 30 web domains to MacSync Stealer, a macOS-focused information stealer, after correlating recurring endpoint and network behaviors across changing infrastructure, tracing the malware from payload retrieval through data collection, staging, and exfiltration. The tech giant said it required multiple endpoint and network behaviors to align before
A JavaServer Pages (JSP) web shell deployed following the exploitation of a critical security flaw in PTC Windchill and FlexPLM servers is specifically designed for the enterprise Product Lifecycle Management (PLM) software, according to new findings from ReliaQuest. The cybersecurity company characterized the web shell as a fully equipped extortion platform capable of mapping sensitive vault
Comcast is promoting WiFi-based motion detection as a part of its new Xfinity Shield home protection platform, allowing routers and wireless devices to detect people moving through a home without cameras or motion sensors. This feature was announced as part of a new Xfinity Shield product offering on Tuesday, a new application suite that combines cybersecurity, physical home monitoring, and family safety features through Xfinity WiFi and the Xfinity app. Part of this new offering is WiFi Shield, which is included at no additional cost for Xfinity Internet customers with compatible gateways. It combines Xfinity's CyberSecure network protection, Family Settings, and WiFi Motion, with Home Watch, Away Watch, and Dark Watch modes used to control when notifications are generated. Comcast is also launching Shield Select for $15 per month, which adds an indoor camera, door/window sensor, cloud video storage, and 24/7 urgent response functionality.
We provide guidance for preparing for and mitigating large-scale credential attacks, focusing on recent campaigns targeting security vendors' devices. The post Threat Brief: Mitigating Large-Scale Credential Attacks (Updated August 18) appeared first on Unit 42.
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)
Security controls can block a familiar attack method while missing quieter ways to achieve the same objective. Picus Security's Blue Report 2026 shows how prevention rates can vary dramatically by technique and why behavioral testing is needed to uncover those gaps.
Microsoft says some users are experiencing issues searching in Microsoft 365 apps, including Outlook on the web, Outlook desktop, SharePoint Online, and OneDrive. According to an incident report seen by BleepingComputer and tracked under MO1456424 in the Microsoft 365 Admin Center, the root cause is what Microsoft describes as a recent deployment that causes resource utilization problems. "Impact is specific to some users served through the affected infrastructure who are attempting to search for content in SharePoint Online, OneDrive, Outlook on the web, or Outlook desktop," Microsoft said. "Our investigation identified that a recent deployment introduced a resource utilization inefficiency issue, leading to impact." Microsoft says it has already developed a fix and deployed it to reduce resource pressure and restore service for all affected Microsoft 365 users.
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