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The invisible passenger in your car

While monitoring Android threats in June 2026, we discovered a new piece of Android malware. What struck us as unusual was that it installed like an ordinary user app yet made no attempt to disguise itself as legitimate software: it had no user interface at all. This led us to suspect the app might be reaching users’ devices without their knowledge. Further investigation confirmed that hypothesis and allowed us to reconstruct the entire infection chain.

Key findings:

  • We identified new Android malware: a multi-stage downloader whose ultimate purpose is ad fraud and creation of a proxy botnet.
  • The malware spread through the built-in updaters of Android-based automotive head unit firmware. This is the first documented case of malware found on a car head unit with an infection chain specific to that type of device.
  • We attribute this activity, with high confidence, to the MoYu Group, an actor linked to the BADBOX botnet.

Kaspersky solutions detect the threats described below under the following detection names:

  • HEUR:Trojan-Dropper.AndroidOS.Agent.vu
  • HEUR:Trojan-Downloader.AndroidOS.Agent.ov
  • HEUR:Trojan-Proxy.AndroidOS.Zhima.*
  • HEUR:Trojan.AndroidOS.Vo1d.*

Head unit firmware overview

A head unit is a system that combines multimedia functions with partial control over certain vehicle functions. Head units may come as part of a car’s factory equipment or as an aftermarket upgrade. The main attack vectors for these systems are compromise via physical access and vulnerabilities in the head unit’s OS or components, both of which we’ve covered previously.

In some cases, head units run on Android, primarily because it’s convenient for manufacturers: Android’s source code already accounts for use cases within automotive head units. Android also allows manufacturers to add their own system applications during the build process, which they can use for a range of purposes: customizing the UI, adding system components tailored to the vendor’s needs, and more.

Most apps developed for Android devices can also run on an Android-based head unit, and that is true for malware as well. That said, it’s hard to imagine certain categories of smartphone-targeted malware being used to attack a head unit. Banking Trojans are a good example: since mobile banking is used almost exclusively on smartphones, infecting a head unit with a banking Trojan would be a waste of the attacker’s resources.

It’s worth noting that head units often include SIM card slots and can connect to the internet, enabling features like navigation and software updates. Since a head unit typically holds nothing of value to an attacker, one of the more likely attack scenarios using “classic” Android malware is infecting the device to recruit it into a botnet – similar to attacks on IoT devices.

During our research, we found exactly that kind of malware. The design of firmware for DoFun head units enabled attackers to distribute malware. We notified the vendor about the distribution scheme, and they subsequently reported fixing the security issues.

Below is the entire infection chain:

Head unit infection scheme

Head unit infection scheme

Let’s look at exactly how these head units became infected.

The TWCore app

TWCore is a legitimate system application responsible for collecting analytics data and updating the head unit software. Let’s take a closer look at how the update function works.

The process is fairly simple. An MQTT message broker hosted on the subdomain cardoor[.]cn sends a message containing information about the APK files that need to be downloaded and installed on the head unit. Notably, the object describing this message includes an installNotExists field, a Boolean flag that can be set to true or false. This flag allows TWCore to install apps that weren’t originally present on the device.

TWCore only checks whether an app is already installed on the device when installNotExists = false

TWCore only checks whether an app is already installed on the device when installNotExists = false

The APK file is downloaded to <TWCore external cache dir>/push/apk/ for installation.

The path TWCore uses to download APK files

The path TWCore uses to download APK files

Our telemetry revealed previously unknown malware at these file paths. On top of that, our data indicates that in every observed case, the malware was installed by an app with the package name com.tw.core, which matches the TWCore package name.

Next, we’ll break down the malware installed by TWCore: the JarService dropper.

Stage 1: the JarService dropper

As mentioned earlier, JarService is a small dropper app with no UI of any kind. It decrypts data stored as encrypted blocks within the Trojan’s code. Each block is XOR-encrypted with a single-byte key that shifts linearly from block to block. The decrypted data contains serialized information about the payload version and entry point, along with the malware’s own code for further loading.

Decrypting and deserializing information about the stage 2 payload

Decrypting and deserializing information about the stage 2 payload

In the version of JarService we analyzed, the entry point for the next-stage payload was the wa method of the com.c.j.qbh class.

Stage 2: the loader

This stage’s payload is a malicious loader. Its code contains encrypted strings that are later used as class names to execute the stage 3 payload using the reflection mechanism. The loader sends implant information to one of the attackers’ servers via a POST request. Example of a request to the C2 server:

{
    "userId": "REDACTED",
    "dexVersion": "1.7",
    "dexType": 1,
    "channelId": "2039",
    "packageName": "com.tw.jar1",
    "appVersion": 12,
    "appName": "JarService"
}

In response to the POST request, the C2 server returns a link for downloading the stage 3 payload. An example of a C2 response is shown below.

{
    "code": 200,
    "data": {
        "dexUrl": "hxxp://144.217.243[.]201/vr34der34/dex3.68.png",
        "dexVersion": 3.680,
        "status": 0
    }
}

The Trojan uses the link in the dexUrl field of the data object to download serialized data for loading the next stage. This data begins with a single-byte integer, a key used to decrypt the strings in the loader’s code. Immediately following this number is a four-byte floating-point value used to XOR-decrypt the stage 3 payload, which itself is located after these keys.

Decrypting the stage 3 payload

Decrypting the stage 3 payload

In the decrypted payload, the entry point is the init method of the com.ast.sdk.BillingMain class, shown in the screenshot below.

Entry point of the stage 3 payload

Entry point of the stage 3 payload

While analyzing this stage, we noticed that the download link for the next-stage payload includes a version number. We decided to try other version numbers to retrieve different payload versions, and ultimately obtained seven distinct variants, which we list under “Indicators of Compromise” at the end of this report. The earliest version, numbered 3.57, uses a different decoding algorithm than the one described above. This may indicate that an earlier version of the infection chain used a different loader between JarService and the stage 3 payload.

Stage 3: clicker / reverse proxy loader

In this stage, the malware sends a POST request to /cpc/api/task every 90 minutes by default, containing information about the infected device (display resolution, device model, the SSID of the connected Wi-Fi network, MAC address, and so on) along with the Trojan’s configuration version. If the configuration is outdated, the C2 server returns an updated configuration containing new C2 addresses and new paths for sending HTTP requests. An example of a response is shown below. Note that at the time of our research, the most up-to-date configuration version was 3.82.

{
    "code": 100,
    "data": {
        "configVersion": 3.820,
        "hosts": ["hxxp://t2.kshahnd[.]sbs", "hxxp://t2.mdsjhd[.]sbs", "hxxp://t2.nmnsny[.]sbs", "hxxps://t2.nmnsny[.]sbs"],
        "interval": 5500000,
        "reportApi": "/cpc/api/report",
        "tagName": "config",
        "taskApi": "/cpc/api/task",
        "updates": ["hxxp://a2.kshahnd[.]sbs", "hxxp://a2.mdsjhd[.]sbs", "hxxp://a2.nmnsny[.]sbs", "hxxps://a2.nmnsny[.]sbs"],
        "vn": 1.010
    }
}

If the configuration version doesn’t need updating, the C2 server instead returns integer command identifiers, which the attackers refer to as productId. The Trojan maps each identifier to command information, which it stores as a serialized JSON object using the SharedPreferences API. Each identifier also has its own version, expressed as a UNIX timestamp. If the C2 response includes an unknown productId or one whose version is outdated, the malware sends a GET request to the attackers’ server at /cpc/api/xml to retrieve the command contents for all such identifiers. The C2 server responds with command information for each unknown identifier. An example of a response is shown below.

{
    "code": 200,
    "data": [{
        "productId": 979,
        "script": "{\n  \"loadType\": 1,\n  \"reload\": true,\n  \"method\": \"start\",\n  \"url2\": \"hxxp://144.217.243[.]201/vr34der34/sh65.io\",\n  \"md52\": \"de77c3303e93c9450424759f1741441c\",\n  \"name\": \"zhima\",\n  \"className\": \"com.miyc.transfer.Client\",\n  \"thread\": true,\n  \"tagName\": \"loadlib2\",\n  \"params\": [\n    {\n      \"type\": \"Context\"\n    },\n    {\n      \"type\": \"String\",\n      \"value\": \"107.151.248[.]132\"\n    },\n    {\n      \"type\": \"String\",\n      \"value\": \"1002\"\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 1337\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 7777\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 8888\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 15000\n    }\n  ],\n  \"url\": \"hxxp://144.217.243[.]201/vr34der34/sh65.io\",\n  \"md5\": \"de77c3303e93c9450424759f1741441c\"\n}",
        "version": 1778650942
    }, {
        "productId": 1019,
        "script": "{\n  \"loadType\": 1,\n  \"reload\": true,\n  \"method\": \"start\",\n  \"url2\": \"hxxp://144.217.243[.]201/vr34der34/sh65.io\",\n  \"md52\": \"de77c3303e93c9450424759f1741441c\",\n  \"name\": \"zhima\",\n  \"className\": \"com.miyc.transfer.Client\",\n  \"thread\": true,\n  \"tagName\": \"loadlib2\",\n  \"params\": [\n    {\n      \"type\": \"Context\"\n    },\n    {\n      \"type\": \"String\",\n      \"value\": \"128.14.210[.]58\"\n    },\n    {\n      \"type\": \"String\",\n      \"value\": \"1002\"\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 9999\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 7777\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 8888\n    },\n    {\n      \"type\": \"int\",\n      \"value\": 15000\n    }\n  ],\n  \"url\": \"hxxp://144.217.243[.]201/vr34der34/sh65.io\",\n  \"md5\": \"de77c3303e93c9450424759f1741441c\"\n}",
        "version": 1766001509
    }, {
        "productId": 3505,
        "script": "{\n\"tagName\":\"http\",\n\"url\":\"hxxps://api.kookjar[.]com/sayhi?channel=daihai&uuid={get_uuid_10}\"\n}",
        "version": 1776656317
    }],
    "msg": ""
}

The command information includes a tagName field, which is the command name. The code maps each name to the corresponding class responsible for executing it.

List of executable commands

List of executable commands

At the time of our research, the attackers had implemented nine commands. The table below lists command names, brief descriptions, and arguments. The functionality of these commands suggests that the malware can be used to display ads, commit ad fraud (serving as a clicker), and download additional malicious code.

Command name Description Arguments
return Return a value from SharedPreferences. key: the key whose value should be returned
copy Set the contents of the clipboard. text: the key whose value from SharedPreferences is returned as the clipboard contents
url: a link for downloading gzip-compressed data (optional); this data is then concatenated with the value of the text key, with      (5 spaces) used as a separator
http Make a POST/GET HTTP request to a specified resource and, if instructed, save the response in SharedPreferences under a specified key. url: the resource address
method: the HTTP method name (optional)
startLabel: a marker for the start of the data to save from the resource (optional)
endLabel: a marker for the end of the data to save from the resource (optional)
valueLabel: the key under which to save the value (optional)
header: a dictionary of headers for the HTTP request (optional)
content: the content of the POST request (optional)
web Open a link in the WebView and execute arbitrary JavaScript code within it. url: the link to open in the WebView
js: base64-encoded JavaScript code to execute in the WebView; used when the url parameter is empty or absent
corejs: JavaScript code to execute when the resource loads in the WebView (optional)
param: a string dictionary of parameters for launching the WebView
client: if this key is present, WebViewClient is used to handle redirects manually
time: task timeout
loadlib Not fully implemented at the time of publishing this report.
loadlib2 Download and execute arbitrary code. url: the address to download the payload from
name: the name of the module being downloaded
md5: the MD5 hash of the payload
clear: a comma-separated list of payload names to delete (optional)
params: an array of parameters to launch the payload with
className: the class name of the payload entry point
method: the name of the virtual method at the payload entry point
cmethod: the name of the static method used to instantiate the entry-point class (optional)
thread: a flag; the payload runs in a separate thread if this flag is not set
reload: a flag that, when set, restarts already loaded modules
loadlib3 Not fully implemented at the time of publishing this report.
deeplink Open a resource in the browser. url: a link to the resource
traceroute Check resource availability via an ICMP ping. host: comma-separated list of resources to check

However, attackers use only a relatively small subset of these commands in real-world attacks. As shown in the example C2 response above, at the time of publishing this report the attackers were using the loadlib2 and http commands. The payload downloaded via the loadlib2 command is a reverse proxy module named “zhima”, which researchers from the Nokia Deepfield Emergency Response Team independently discovered in TV set-top boxes around the same time as we did and also described in their report. This confirms that the attackers’ ultimate goal is building a proxy botnet.

While investigating this stage of the attack chain, we noticed that the zhima download link also included a version number. As with the previous stage, we tried other possible version numbers and found eight variants of the zhima module, the earliest of which was version 57. The complete list of identified zhima modules is provided under “Indicators of Compromise” below.

Attribution

While analyzing the complete infection chain, we noticed that the stage 2 loader created a thread with the meaningful name mosdk-host-loader. We decided to investigate what mosdk referred to in that name. This led us to a malicious app installed on various TV set-top boxes with the package name com.abc.nexus (3AD4BF5A86D26FFBF09CAE42AF330A98). It consists of several components (including a dropper similar to JarService), each used by the attackers to covertly monetize the device’s computing power. Each malicious component in the app corresponds to its own service, and the service containing the launch code for the JarService-like dropper is named AdmoyuService. In light of this and the name of the malicious thread found in the payload code, we concluded that moyu in the service name referred to MoYu Group, one of the actors linked to the BADBOX malware platform, which had been described by researchers at HUMAN. This assessment is further supported by extensive overlap between the malware’s network infrastructure and that of MoYu Group, which was independently identified by researchers from the Nokia Deepfield Emergency Response Team around the same time as our own research. Based on these similar naming patterns and prominent infrastructure overlap between the activity of MoYu Group and the attacks described in this report, we attribute it to the same actor with high confidence.

While investigating the malware downloaded by TWCore, we noticed that the domain admin.uipoxy[.]com resolved to the IP address 128.14.210[.]58, one of the C2 servers for the zhima reverse proxy module. It appears that the URL hxxp://admin.uipoxy[.]com/proxy/u/login hosts the zhima admin panel. Interestingly, this panel allows anyone to register as long as they have a valid invite code.

The malware operator registration page

The malware operator registration page

During registration, users are prompted to review the terms of use and privacy policy. Both documents are hosted on links under the pxyedge[.]com domain, which belongs to PXYEDGE, a vendor specializing in the sale of residential proxies.

On the registration page hosted at admin.uipoxy[.]com, we also found the string copyright © 2020 proxyforu[.]com all rights reserved, which linked to hxxps://proxyforu[.]com, the website of ProxyForU, another vendor of residential proxy services.

We found several similarities in the authentication APIs across all of these sites:

  • The sign-in page was hosted on an admin.* subdomain.
  • The sign-in page was located at /proxy/u/login.
  • The signup page was located at /proxy/register?channelKey=<invitation code>.

Based on this, we believe these services are connected to MoYu Group.

Conclusion

Despite efforts by cybersecurity professionals and law enforcement to shut down the BADBOX botnet, individual actors linked to it continue their malicious activity, infecting devices worldwide. Delivery methods for this kind of malware vary widely, from downloads via pre-installed backdoors to infected builds of IPTV apps. The case examined here demonstrates an even more sophisticated delivery method: distribution through the legitimate update functionality of a system application. Attackers are also actively expanding into new platforms. This malware is the first known malicious app targeting head units, which means these platforms now require protection against malware as well.

Indicators of compromise

Stage 1: JarService

ba27951b4ee1c341f4415d033369ecd3
d63bacd6d6709dd68a10ef9d374c7835
6c2e34b30da42085240ede53ab6107d4
8b5e513144a6138a966ea59e68bf9da2
e119845877089d6f4b0a70dc7388f316

Stage 2: loader

e9f3a0dab6949ce2cddab9e0aa80ae1a

Stage 3: loader/clicker

0fbaa7092204f4b1494e0b840b014774
1dcf031c40ce456b6a36a00b0acf3d11
44b6b213a6a3f299eaf88e078de95ecb
67dc78e544ebce16b85dc7c195dfbc58
9642ae619b3165d23c6349002d1abe24
b067d5b0dbecbd6498bcdfba45dba77e
f0e3f7eba2cde91e2dedb921bab47422

zhima module

412e9243f2981bbea3894254d105b3b8
71ab5517f71866279d0d87d37f2ae320
89ef78f716a75964539f2db6520be362
a4223ce4288a230d1e6c3ff2c7639045
bd4d81cd27125ad3d9a114922d468499
c6bfb1643ac7474ed8a7b4f96a187fdb
de77c3303e93c9450424759f1741441c
f8cf8c23ff597700d471fb7767df8bac

Domains and IP addresses

xmsae[.]sbs
ishano456[.]sbs
xshaon123[.]sbs
kshahnd[.]sbs
mdsjhd[.]sbs
nmnsny[.]sbs
kookjar[.]com
ty54fgd435[.]my
ue886578433[.]online
ty4523[.]space
144.217.243[.]201
107.151.248[.]132
128.14.210[.]58

Addresses used to download JarService

hxxp://ovcloudcontrol.cdn.cardoor[.]cn/upgrade/2026-06-08/bd80bd3c3d0e4bf6b5b4a825650d01f5.apk
hxxp://ovcloudcontrol.cdn.cardoor[.]cn/upgrade/2025-06-10/fe71af9ecf174de48d2b2ccc2c15fb04.apk
hxxp://ovcloudcontrol.cdn.cardoor[.]cn/upgrade/2024-11-07/fa831c3c23824b99871163387bcda7ad.apk

Hashes of TWCore (the legitimate software used to distribute JarService)

2a64c3efc11bf224aa54f24e876446c9
7a4d3ba2dacccfdda55859a5dfee2671
ea24487996eb70c1780922fb3063bcc5

Almost Half of Malware Samples Communicate Direct to IP

Nearly half of C2 malware bypasses DNS by connecting directly to IP addresses. Zero trust IP enforcement secures networks against these threats.

The post Almost Half of Malware Samples Communicate Direct to IP appeared first on Unit 42.

IT threat evolution in Q1 2026. Non-mobile statistics

IT threat evolution in Q1 2026. Non-mobile statistics
IT threat evolution in Q1 2026. Mobile statistics

The statistics in this report are based on detection verdicts returned by Kaspersky products unless otherwise stated. The information was provided by Kaspersky users who consented to sharing statistical data.

Quarterly figures

In Q1 2026:

  • Kaspersky products blocked more than 343 million attacks that originated with various online resources.
  • Web Anti-Virus responded to 50 million unique links.
  • File Anti-Virus blocked nearly 15 million malicious and potentially unwanted objects.
  • 2938 new ransomware variants were detected.
  • More than 77,000 users experienced ransomware attacks.
  • 14% of all ransomware victims whose data was published on threat actors’ data leak sites (DLS) were victims of Clop.
  • More than 260,000 users were targeted by miners.

Ransomware

Quarterly trends and highlights

Law enforcement success

In January 2026, it was reported that the FBI had seized the domains of the RAMP cybercrime forum, a major platform used extensively by ransomware developers to advertise their RaaS programs and to recruit affiliates. There has been no official statement from the FBI, nor is it clear if RAMP servers were seized. In a post on an external website, a RAMP moderator mentioned law enforcement agencies gaining control over the forum. The takedown disrupted a key element of the RaaS ecosystem, creating ripple effects for ransomware operators, affiliates, and initial access brokers.

A man suspected of links to the Phobos group was apprehended in Poland. He was charged with the creation, acquisition, and distribution of software designed for unlawfully obtaining information, including data that facilitates unauthorized access to information stored within a computer system.

In March, a Phobos ransomware administrator pleaded guilty to the creation and distribution of the Trojan, which had been used in international attacks dating back to at least November 2020.

In March, the U.S. Department of Justice charged a man who had acted as a negotiator for ransomware groups. The company he worked for specializes in cyberincident investigations. The prosecution alleges the suspect colluded with the BlackCat threat actor to share privileged insights into the ongoing progress of negotiations. Additionally, the suspect is alleged to have had a prior direct role in BlackCat attacks, serving as an affiliate for the RaaS operation.

In a separate development this March, a U.S. court sentenced an initial access broker associated with the Yanluowang ransomware group to 81 months of imprisonment. According to the U.S. Department of Justice, the convict facilitated dozens of ransomware attacks across the United States, resulting in over $9 million in actual loss and more than $24 million in intended loss.

Vulnerabilities and attacks

The Interlock group has been heavily exploiting the CVE-2026-20131 zero-day vulnerability in Cisco Secure FMC firewall management software since at least January 26, 2026. The vulnerability enabled arbitrary Java code execution with root privileges on the affected device. This campaign demonstrates the ongoing reliance on zero-day vulnerabilities for initial access, a focus on network appliances as high-value entry points, and the rapid weaponization of new vulnerabilities within the ransomware ecosystem.

The most prolific groups

This section highlights the most prolific ransomware gangs by number of victims added to each group’s DLS. This quarter, the Clop ransomware (14.42%) returned to the top of the rankings, displacing Qilin (12.34%), which had held the leading position in the previous reporting period. Following closely is a new threat actor, The Gentlemen (9.25%). Emerging no later than July 2025, the group had already surpassed the activity levels of mainstays such as Akira (7.25%) and INC Ransom (6.13%).

Number of each group’s victims according to its DLS as a percentage of all groups’ victims published on all the DLSs under review during the reporting period (download)

Number of new variants

In Q1 2026, Kaspersky solutions detected six new ransomware families and 2938 new modifications. Volumes have returned to Q3 2025 levels following a surge in Q4 2025.

Number of new ransomware modifications, Q1 2025 — Q1 2026 (download)

Number of users attacked by ransomware Trojans

Throughout Q1, our solutions protected 77,319 unique users from ransomware. Ransomware activity was highest in March, with 35,056 unique users encountering such attacks during the month.

Number of unique users attacked by ransomware Trojans, Q1 2026 (download)

Attack geography

TOP 10 countries and territories attacked by ransomware Trojans

Country/territory* %**
1 Pakistan 0.79
2 South Korea 0.64
3 China 0.52
4 Tajikistan 0.40
5 Libya 0.38
6 Turkmenistan 0.36
7 Iraq 0.35
8 Bangladesh 0.33
9 Rwanda 0.30
10 Cameroon 0.28

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by ransomware Trojans as a percentage of all unique users of Kaspersky products in the country/territory.

TOP 10 most common families of ransomware Trojans

Name Verdict %*
1 (generic verdict) Trojan-Ransom.Win32.Gen 33.90
2 (generic verdict) Trojan-Ransom.Win32.Crypren 6.38
3 WannaCry Trojan-Ransom.Win32.Wanna 5.87
4 (generic verdict) Trojan-Ransom.Win32.Encoder 4.68
5 (generic verdict) Trojan-Ransom.Win32.Agent 3.80
6 LockBit Trojan-Ransom.Win32.Lockbit 2.80
7 (generic verdict) Trojan-Ransom.Win32.Phny 1.99
8 (generic verdict) Trojan-Ransom.MSIL.Agent 1.96
9 (generic verdict) Trojan-Ransom.Python.Agent 1.93
10 (generic verdict) Trojan-Ransom.Win32.Crypmod 1.89

* Unique Kaspersky users attacked by the specific ransomware Trojan family as a percentage of all unique users attacked by this type of threat.

Miners

Number of new variants

In Q1 2026, Kaspersky solutions detected 3485 new modifications of miners.

Number of new miner modifications, Q1 2026 (download)

Number of users attacked by miners

In Q1, we detected attacks using miner programs on the computers of 260,588 unique Kaspersky users worldwide.

Number of unique users attacked by miners, Q1 2026 (download)

Attack geography

TOP 10 countries and territories attacked by miners

Country/territory* %**
1 Senegal 3.19
2 Turkmenistan 3.06
3 Mali 2.63
4 Tanzania 1.62
5 Bangladesh 1.06
6 Ethiopia 0.95
7 Panama 0.88
8 Afghanistan 0.79
9 Kazakhstan 0.77
10 Bolivia 0.75

* Excluded are countries and territories with relatively few (under 50,000) Kaspersky users.
** Unique users whose computers were attacked by miners as a percentage of all unique users of Kaspersky products in the country/territory.

Attacks on macOS

In Q1 2026, Google uncovered a new cryptocurrency theft campaign. The scammers directed victims to a fraudulent video call, prompting them to execute malicious scripts under the guise of technical support fixes for connection problems.

In March, researchers with GTIG and iVerify reported the discovery of an in-the-wild exploit chain targeting both iOS and macOS devices. The exploit kit was apparently marketed on the dark web, providing threat actors with a suite of spyware capabilities alongside specialized cryptocurrency exfiltration modules. The exploit was delivered via drive-by downloads when victims visited various compromised websites. Our analysis confirmed that the toolkit included an updated version of a component previously identified in the Operation Triangulation attack chain.

Devices running macOS were similarly impacted by the high-profile supply chain attack targeting the Axios npm package, a widely used HTTP client for JavaScript. The installation of the infected package led to the deployment of a backdoor on macOS devices.

TOP 20 threats to macOS

Unique users* who encountered this malware as a percentage of all attacked users of Kaspersky security solutions for macOS (download)

* Data for the previous quarter may differ slightly from previously published data due to some verdicts being retrospectively revised.

The share of PasivRobber spyware attacks is beginning to decline, giving way to more traditional adware and Monitor-class software capable of tracking user activity. The popular Amos stealer also maintains its presence within the TOP 20.

Geography of threats to macOS

TOP 10 countries and territories by share of attacked users

Country/territory %* Q4 2025 %* Q1 2026
China 1.28 1.97
France 1.18 1.07
Brazil 1.13 0.98
Mexico 0.72 0.52
Germany 0.71 0.45
The Netherlands 0.62 0.75
Hong Kong 0.49 0.53
India 0.42 0.48
Russian Federation 0.34 0.37
Thailand 0.24 0.27

* Unique users who encountered threats to macOS as a percentage of all unique Kaspersky users in the country/territory.

IoT threat statistics

This section presents statistics on attacks targeting Kaspersky IoT honeypots. The geographic data on attack sources is based on the IP addresses of attacking devices.

In Q1 2026, the share of devices attacking Kaspersky honeypots via the SSH protocol saw a significant increase compared to the previous reporting period.

Distribution of attacked services by number of unique IP addresses of attacking devices (download)

The distribution of attacks between Telnet and SSH maintained the ratio observed in Q4 2025.

Distribution of attackers’ sessions in Kaspersky honeypots (download)

TOP 10 threats delivered to IoT devices

Share of each threat delivered to an infected device as a result of a successful attack, out of the total number of threats delivered (download)

The primary shifts in the IoT threat distribution are linked to the activity of various Mirai botnet variants, although members of this family continue to account for the majority of the list. Furthermore, a new variant, Mirai.kl, surfaced in the rankings. We also observed a significant decline in NyaDrop botnet activity during Q1.

Attacks on IoT honeypots

The United States, the Netherlands, and Germany accounted for the highest proportions of SSH-based attacks during this period.

Country/territory Q4 2025 Q1 2026
United States 16.10% 23.74%
The Netherlands 15.78% 17.57%
Germany 12.07% 10.34%
Panama 7.72% 6.34%
India 5.32% 6.05%
Romania 4.05% 5.82%
Australia 1.62% 4.61%
Vietnam 4.21% 3.50%
Russian Federation 3.79% 2.35%
Sweden 2.25% 2.09%

China continues to account for the largest proportion of Telnet attacks, though there was a marked increase in activity originating from Pakistan.

Country/territory Q4 2025 Q1 2026
China 53.64% 39.54%
Pakistan 14.27% 27.31%
Russian Federation 8.20% 8.25%
Indonesia 8.58% 6.71%
India 4.85% 4.66%
Brazil 0.06% 3.30%
Argentina 0.02% 2.51%
Nigeria 1.22% 1.38%
Thailand 0.01% 0.55%
Sweden 0.54% 0.55%

Attacks via web resources

The statistics in this section are based on detection verdicts by Web Anti-Virus, which protects users when suspicious objects are downloaded from malicious or infected web pages. These malicious pages are purposefully created by cybercriminals. Websites that host user-generated content, such as message boards, as well as compromised legitimate sites, can become infected.

TOP 10 countries and territories that served as sources of web-based attacks

The following statistics show the distribution by country/territory of the sources of internet attacks blocked by Kaspersky products on user computers (web pages redirecting to exploits, sites containing exploits and other malicious programs, botnet C&C centers, and so on). One or more web-based attacks could originate from each unique host.

To determine the geographic source of web attacks, we matched the domain name with the real IP address where the domain is hosted, then identified the geographic location of that IP address (GeoIP).

In Q1 2026, Kaspersky solutions blocked 343,823,407 attacks launched from internet resources worldwide. Web Anti-Virus was triggered by 49,983,611 unique URLs.

Web-based attacks by country/territory, Q1 2026 (download)

Countries and territories where users faced the greatest risk of online infection

To assess the risk of malware infection via the internet for users’ computers in different countries and territories, we calculated the share of Kaspersky users in each location on whose computers Web Anti-Virus was triggered during the reporting period. The resulting data provides an indication of the aggressiveness of the environment in which computers operate in different countries and territories.

This ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out Web Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

Country/territory* %**
1 Venezuela 9.33
2 Hungary 8.16
3 Italy 7.58
4 Tajikistan 7.48
5 India 7.21
6 Greece 7.13
7 Portugal 7.10
8 France 7.05
9 Belgium 6.83
10 Slovakia 6.80
11 Vietnam 6.62
12 Bosnia and Herzegovina 6.57
13 Canada 6.56
14 Serbia 6.50
15 Tunisia 6.36
16 Qatar 6.01
17 Spain 5.95
18 Germany 5.95
19 Sri Lanka 5.89
20 Brazil 5.88

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users targeted by web-based Malware attacks as a percentage of all unique users of Kaspersky products in the country/territory.

On average during the quarter, 4.73% of users’ computers worldwide were subjected to at least one Malware web attack.

Local threats

Statistics on local infections of user computers are an important indicator. They include objects that penetrated the target computer by infecting files or removable media, or initially made their way onto the computer in non-open form. Examples of the latter are programs in complex installers and encrypted files.

Data in this section is based on analyzing statistics produced by anti-virus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media. The statistics are based on detection verdicts from the On-Access Scan (OAS) and On-Demand Scan (ODS) modules of File Anti-Virus and include detections of malicious programs located on user computers or removable media connected to the computers, such as flash drives, camera memory cards, phones, or external hard drives.

In Q1 2026, our File Anti-Virus detected 15,831,319 malicious and potentially unwanted objects.

Countries and territories where users faced the highest risk of local infection

For each country and territory, we calculated the percentage of Kaspersky users whose computers had the File Anti-Virus triggered at least once during the reporting period. This statistic reflects the level of personal computer infection in different countries and territories around the world.

Note that this ranked list includes only attacks by malicious objects classified as Malware. Our calculations leave out File Anti-Virus detections of potentially dangerous or unwanted programs, such as RiskTool or adware.

Country/territory* %**
1 Turkmenistan 47.96
2 Tajikistan 31.48
3 Cuba 31.03
4 Yemen 29.59
5 Afghanistan 28.47
6 Burundi 26.93
7 Uzbekistan 24.81
8 Syria 23.08
9 Nicaragua 21.97
10 Cameroon 21.60
11 China 21.09
12 Mozambique 21.02
13 Algeria 20.64
14 Democratic Republic of the Congo 20.63
15 Bangladesh 20.44
16 Mali 20.35
17 Republic of the Congo 20.23
18 Madagascar 20.00
19 Belarus 19.78
20 Tanzania 19.52

* Excluded are countries and territories with relatively few (under 10,000) Kaspersky users.
** Unique users on whose computers local Malware threats were blocked, as a percentage of all unique users of Kaspersky products in the country/territory.

On average worldwide, Malware local threats were detected at least once on 11.55% of users’ computers during Q1.

Russia scored 11.92% in these rankings.

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