Malware is abusing car infotainment updates to install proxy software, turning Android head units into nodes for the BADBOX network. Kaspersky researchers found something in June 2026 that made them stop and look twice: an Android app with no interface at all, installed like any ordinary app but making zero effort to disguise itself as legitimate. Their report documents the first known malware infection targeting car head units, the Android-based infotainment and control systems built into many modern vehicles, spread through the vehicle’s own official update mechanism. Head units run Android for practical reasons, since manufacturers can build on existing source code and add their own custom system apps during production. That flexibility cuts both ways. Because head units are just Android devices under the hood, most apps built for smartphones can technically run on them too, including malware, even though something like a banking trojan would be wasted effort on a device nobody uses for mobile banking. Kaspersky researchers found something unusual in June 2026: an Android app with no visible interface that was installed like a normal app but did not try to look legitimate. Their report describes what they say is the first known malware infection targeting car head units, Android-based systems used for infotainment and vehicle controls, delivered through the car’s official update system. “We identified new Android malware: a multi-stage downloader whose ultimate purpose is ad fraud and creation of a proxy botnet.” reads the report published by Kaspersky. “The malware spread through the built-in updaters of Android-based automotive head unit firmware. This is the first documented case of malware found on a car head unit with an infection chain specific to that type of device.” Car head units often use Android because it is flexible and lets manufacturers add their own software. But this also creates a security risk. Since these systems are basically Android devices, many apps designed for smartphones can also run on them, including malware. The infection chain here starts inside TWCore, a completely legitimate system app responsible for analytics and firmware updates on DoFun head units. An MQTT message broker sends TWCore instructions about which APK files to download and install, and a specific configuration flag called installNotExists controls whether the app checks if something is already installed before pushing it. “TWCore only checks whether an app is already installed on the device when installNotExists = false The APK file is downloaded to <TWCore external cache dir>/push/apk/ for installation.” which means setting that flag the other way turns a routine update channel into a silent installation pipeline for anything the attackers want to push. What gets installed through that channel is a small dropper called JarService, carrying zero user interface and doing nothing but decrypting and loading the next stage. That stage is a loader that phones home to a command server, reports basic device details, and receives a download link for the actual payload, a third-stage module researchers found could be pulled in at least seven different versions simply by trying different version numbers in the download URL. That third stage turns out to be a clicker and reverse proxy component, checking in with its server every 90 minutes by default and waiting for new instructions. “In this stage, the malware sends a POST request to /cpc/api/task every 90 minutes by default, containing information about the infected device (display resolution, device model, the SSID of the connected Wi-Fi network, MAC address, and so on) along with the Trojan’s configuration version.” continues the report. “If the configuration is outdated, the C2 server returns an updated configuration containing new C2 addresses and new paths for sending HTTP requests.” The command system behind the malware is more powerful than you might expect from something running silently on a car’s dashboard. Researchers found nine commands, including ones that can change the clipboard, send HTTP requests, load web pages, and run JavaScript. In practice, attackers mainly used two: one to download and run new code, and another to send web requests. The extra module they deployed, called “zhima,” was a reverse proxy. This means the main goal was not to attack or control the car. Instead, the attackers wanted to use infected cars’ internet connections as part of a larger proxy network. Tracing the malware’s naming conventions and infrastructure led researchers to attribute the campaign, with high confidence, to MoYu Group, an actor connected to the BADBOX botnet that’s been documented previously by other researchers. A thread inside the second-stage loader carried the internal name “mosdk-host-loader,” which pointed toward malware previously found on TV set-top boxes tied to the same group, and overlapping network infrastructure independently spotted by a separate research team around the same time backed up the connection. The command system behind the malware is more powerful than you might expect from something running silently on a car’s dashboard. Researchers found nine commands, including ones that can change the clipboard, send HTTP requests, load web pages, and run JavaScript. In practice, attackers mainly used two: one to download and run new code, and another to send web requests. The extra module they deployed, called “zhima,” was a reverse proxy. This means the main goal was not to attack or control the car. Instead, the attackers wanted to use infected cars’ internet connections as part of a larger proxy network. There is also a strange detail in the network’s infrastructure. The proxy service’s admin panel allows people to create accounts if they have a valid invite code. Its copyright text and login page also look similar to those used by at least two commercial residential-proxy services. This suggests the network may be linked to a business-like proxy service, while also using infected cars to provide internet connections. BADBOX has survived several takedown attempts by researchers and law enforcement. This campaign shows why: the people behind it keep changing how they spread the malware and are moving into new types of devices. Cars are now joining smart TVs and set-top boxes as devices that can be quietly added to a botnet. As cars become more like computers, they also need the same kind of security protection. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Android Car)
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.
A new Android threat codenamed Manic has been observed actively targeting Ukrainian banks, government and identity services, and messaging applications, as well as Russian and European financial institutions, global fintech and cryptocurrency services, and military-focused communications. "Manic sits at the intersection of Android banking malware and mobile spyware, combining financial-fraud
Cybersecurity researchers have shed light on an updated version of ToxicPanda (aka TgToxic) that comes with "significant enhancements," including a set of 167 remote commands and expands its targeting footprint globally. Zimperium zLabs, in a Wednesday report, said the Android malware also features a PIN harvesting workflow targeting more than 140 banking and cryptocurrency applications.
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.
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