Unit 42 details how attackers exploit enterprise collaboration tools for identity phishing and credential theft. Discover key defense strategies. The post Identity Abuse Through Trusted Communication Channels appeared first on Unit 42.
U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds an MLflow vulnerability to its Known Exploited Vulnerabilities catalog. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added a Progress LoadMaster vulnerability, tracked as CVE-2026-64849 (CVSS score of 9.3), to its Known Exploited Vulnerabilities (KEV) catalog. CVE-2026-64849 is a critical server-side request forgery (SSRF) vulnerability in MLflow, a platform for managing machine-learning workflows. The issue affects MLflow versions before 3.15.0 and a remote attacker can exploit the issue without authentication. The vulnerability allows attackers to make requests from an exposed MLflow server to internal services, including cloud metadata endpoints, potentially exposing temporary cloud credentials. Attackers are actively exploiting CVE-2026-64849 to access cloud metadata services and steal credentials and secrets. Cybersecurity firm watchTowr also observed widespread scanning for exposed MLflow instances just hours after the CVE was assigned on August 17, 2026. “watchTowr Intel is observing in-the-wild exploitation of a critical unauthenticated Server-Side Request Forgery vulnerability in MLflow (CVE-2026-64849), the open-source platform for managing the machine learning and AI development lifecycle, with over 60 million monthly downloads.” watchTowr said in a post on LinkedIn. “Attackers are exploiting the vulnerability to reach cloud metadata services directly, and exfiltrating cloud credentials and secrets. Within hours of the CVE being assigned, Attacker Eye, our global honeypot network, detected attackers indiscriminately scanning for exposed MLflow systems online, capturing attempts against cloud-hosted instances.” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CISA)
U.S. healthcare IT company CareCloud disclosed that the data breach incident it suffered earlier this year has impacted more than 3.7 million individuals. The healthcare technology organization is publicly traded and provides electronic health records, medical billing, practice management, and revenue-cycle services. The company disclosed the incident in March via a filing with the U.S. Securities and Exchange Commission (SEC), noting that the attack caused an 8-hour network disruption on its platform and cut access to one of its databases. At the time, the firm said the compromised environment contained patient data, indicating the risk of sensitive medical information being stolen. Following the incident, CareCloud launched an investigation to determine its scope and how many people were potentially impacted.
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.
Huntress observed a 155x increase in password spraying attacks in H1 2026, including a campaign that generated more than 81 million login attempts in two weeks. The attacks exploited legacy authentication and gaps in MFA policies that left some login flows unprotected.
Cybersecurity researchers have disclosed details of a remote Spectre attack against Cloudflare Workers that leaked a JSON Web Token (JWT) from a co-located Worker in the production environment at up to 12 bits per second, 360 times the rate of an earlier attack demonstrated in 2021. The end-to-end experiment used an attacker Worker and a victim Worker controlled by the researchers,
Cybersecurity researchers at Hunt.io have disclosed details of a campaign that they say compromised more than 14,530 Dahua devices between June 17 and July 22, 2026, using credential attacks, two authentication-bypass flaws, and a peer-to-peer (P2P) relay technique. The activity, codenamed Operation CameraSwarm, was reconstructed from a 407 MB exposed working directory containing 2,616 files
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
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.
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)
Confirm this action.
Leaving now will discard your changes.