A new Android malware named Manic targeting users in multiple European countries has a fallback mechanism for exfiltrating data through nearby infected devices. The malware has been active since at least February and combines spyware, banking fraud, and remote control capabilities. It targets at least 169 banking, government/eID, payment, crypto wallet, messaging, and authenticator/2FA apps, with users in Ukraine being the primary focus. Mobile security company ThreatFabric analyzed the Manic malware and found that it uses transparent overlays on the numeric keypads of legitimate applications to capture victims' taps and reproduce them through Android Accessibility, allowing the legitimate applications to continue functioning normally. After obtaining Accessibility and notification access permissions, the malware can capture the lock PIN/password, intercept notifications and SMS messages, collect files and location data, monitor the screen, and provide remote control to operators via WebRTC sessions.
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.
In a large-scale campaign that researchers dubbed CameraSwarm, hackers compromised more than 14,500 Dahua IP cameras mostly in Ukraine and Russia. The operation ran for at least 35 days between June 17 and July 22, compromising devices by exploiting vulnerabilities, brute-forcing logins, and using offline recovery codes from serial numbers for cloud-registered cameras. Researchers at threat intelligence company Hunt.io discovered the campaign after finding a working directory on an HTTP server that the operator left unprotected. Hunt.io recovered 407 MB of data comprising 2,616 files across 234 directories, including source code, logs, credentials, captured camera images, shell history, and exploitation results, which helped them map an impressive operation. According to their findings, the 35-day CameraSwarm campaign compromised 14,530 Dahua IP cameras using three attack methods in parallel:
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.
Microsoft tracked over 30 MacSync Stealer domains by focusing on behavioral patterns, revealing a campaign targeting passwords, keys, wallets and other data. Domain blocking is a losing game when the thing you’re blocking can register a new domain faster than you can add it to a list. That’s the exact problem Microsoft Defender Experts ran into while tracking MacSync Stealer, a macOS-focused information stealer that RST Cloud first flagged for swapping out its command-and-control infrastructure almost immediately after getting publicly outed. Microsoft detailed how its experts stopped chasing individual domains and started tracking the behaviors that stayed constant underneath them. Instead of tracking individual domains, Microsoft looked at recurring request patterns, HTTP headers and other behaviors. This allowed its researchers to link more than 30 domains to the same campaign and determine that the infrastructure was doing more than just sending commands to infected Macs. It was also being used to collect, stage and exfiltrate stolen data. “MacSync Stealer is a macOS-focused information stealer that relies on changing infrastructure to deliver payloads, communicate with compromised devices, and exfiltrate data. Earlier reporting by RST Cloud identified the threat through a limited set of domains and documented rapid command-and-control (C2) replacement after public disclosure.” reads the report published by Microsoft. “Microsoft Defender Experts expanded that view by correlating recurring endpoints and network behaviors across the activity. This behavior-led approach connected more than 30 domains and showed that the infrastructure supported more than C2 communication, extending into active collection, staging, and exfiltration.” The infection chain starts with a trick rather than an exploit. Victims get social-engineered through a technique known as ClickFix, tricked into pasting or running commands directly in macOS Terminal, and once that shell session fires, curl pulls down attacker-controlled payload content from a path formatted as /curl/[token]. Then, native macOS tools decode and unpack the payload, and an AppleScript-driven layer takes over, blending Unix commands like sh, cp, rm, and killall with osascript calls that make the whole chain look more like ordinary system scripting than malware. Once active, the stealer focuses on valuable data. The malicious code looks for macOS Keychain data, saved browser passwords and cookies, SSH keys, AWS credentials, Kubernetes configurations and files in common user folders. It also searches for Ledger and Trezor wallet data, showing that the malware targets users with valuable credentials and assets rather than simply collecting random browser history. What actually confirms exfiltration, rather than just suspicious traffic, is the upload mechanism itself. Collected data gets staged under temporary paths, compressed into an archive, split into chunks, and pushed out through HTTP PUT requests carrying parameters like upload_id, chunk_index, and total_chunks. “The staged archive was uploaded through rotating infrastructure using curl and HTTP PUT requests. Observed requests included –data-binary, API-key headers, macOS User-Agent string, upload_id values, chunk_index values, and total_chunks parameters.” states Microsoft. “These upload traits confirmed active data exfiltration and provided durable hunting pivots even when domains rotated. “ The researchers pointed out that the exfiltration method stays recognizable even when the destination keeps changing. RST Cloud’s follow-up work backs up how consistent this infrastructure actually is under the surface. Using the same recurring URI patterns, RST Cloud surfaced eleven additional candidate domains and found a static API-key value shared across four confirmed command-and-control domains, even while the build token attached to each deployment kept rotating. A shared static key sitting inside otherwise rotating infrastructure is exactly the kind of detail that makes automated evasion look less impressive up close. The attack wraps up with cleanup, deleting temporary archives, staging folders, and lock files after the upload completes. Microsoft notes this reduces what’s left sitting on disk, but it doesn’t erase the behavioral sequence itself. “After exfiltration, the malware removed temporary archives, staging folders, lock files, and other artifacts. Although this cleanup reduced on-disk evidence, the sequence of archive creation, chunked upload, and deletion can still provide a useful behavioral correlation for defenders.” concludes Microsoft. For anyone defending Mac fleets, the practical takeaway here isn’t a list of domains to block, since that list will be stale within days. It’s building detection around the recurring shape of the attack itself: shell sessions spawning curl with those specific flag patterns, osascript chaining rapidly into network activity, and archives appearing under /tmp/sync* right before outbound PUT traffic starts. Chase the pattern, not the address, because the address was never going to sit still long enough to matter. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, malware)
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)
U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds a Ray-Project Ray vulnerability to its Known Exploited Vulnerabilities catalog. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added a Progress LoadMaster vulnerability, tracked as CVE-2025-62593 (CVSS score of 9.4), to its Known Exploited Vulnerabilities (KEV) catalog. CVE-2025-62593 is a critical remote code execution (RCE) vulnerability in Ray, an AI compute engine. Versions before 2.52.0 insufficiently protected the Ray dashboard/API against browser-based attacks. Its defense relied on checking whether the HTTP User-Agent header started with “Mozilla”, but browsers can modify this header. By combining this weakness with DNS rebinding, an attacker could potentially execute arbitrary code on a developer’s machine simply by getting them to visit a malicious website or view a malicious advertisement while running Ray. The vulnerability affects Firefox and Safari. Ray 2.52.0 fixes the issue. “This vulnerability is due to an insufficient guard against browser-based attacks, as the current defense uses the User-Agent header starting with the string “Mozilla” as a defense mechanism. This defense is insufficient as the fetch specification allows the User-Agent header to be modified.” reads the advisory. “Combined with a DNS rebinding attack against the browser, and this vulnerability is exploitable against a developer running Ray who inadvertently visits a malicious website, or is served a malicious advertisement (malvertising).” “An attacker exploited a code injection vulnerability in Ray AI Compute Engine via a DNS rebinding attack, leading to remote code execution. This allowed the attacker to escalate privileges within the system, move laterally across the network, establish command and control channels, exfiltrate sensitive data, and ultimately disrupt operations.” reads the analysis published by Aviatrix. According to Binding Operational Directive (BOD) 22-01: Reducing the Significant Risk of Known Exploited Vulnerabilities, FCEB agencies have to address the identified vulnerabilities by the due date to protect their networks against attacks exploiting the flaws in the catalog. Experts also recommend that private organizations review the Catalog and address the vulnerabilities in their infrastructure. CISA orders federal agencies to fix the vulnerability by the end of this week, on August 20, 2026. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CISA)
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