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ToddyCat: your hidden email assistant. Part 2

Introduction

We continue to share details on the malicious techniques and toolsets used by the ToddyCat APT group. In the first part of this report, we examined the group’s attacks aimed at stealing data from browsers, as well as from local and cloud email services. The methods used in that campaign indicated that ToddyCat was attempting to access corporate correspondence while evading monitoring tools. However, all of the group’s methods we described previously are effectively detected by EPP and EDR solutions.

The attackers continued their search for ways to bypass security solutions and developed a new tool to gain access to a victim’s cloud account via the Google API. Armed with this tool, the group automated all stages of the attack and managed to remain undetected by monitoring systems.

In this part of the report, we break down the mechanics of this new attack and analyze the tool that was used to automate it. We’ll also discuss how to detect and defend against this threat.

Umbrij

In this campaign, the attackers focused their attention on corporate email communications hosted on Gmail, targeting access compromise via APIs. Because the Google API relies on the OAuth 2.0 protocol for authorization, applications can use an OAuth token to access requested email resources. To acquire this token, the threat actors developed a tool called Umbrij and used it to connect to the browser’s management console in headless mode via a remote debugging port. Through a series of requests, they obtained an OAuth authorization code, which they subsequently exchanged for an access token to reach the target resources via the API. We have dubbed this technique Shadow Token via Remote Debug (STRD).

This attack is viable on Chromium-based browsers. If the user has not logged out of their Gmail account, the browser maintains an active session. The attackers exploit this: they launch the browser, connect via the remote debugging port to take control, and send a request to the Gmail service to grant access to the Google account resources within the context of the user’s saved session.

During our investigation of this attack, we discovered several versions of the Umbrij tool. These versions included a variety of helper functions designed for debugging, as well as for searching and selecting user accounts within the browser, among other tasks.

Kaspersky solutions detect this tool with the following verdicts: HEUR:Trojan-PSW.MSIL.Umbrij.gen, HEUR:Trojan.MSIL.Agent.gen, HEUR:Trojan-PSW.MSIL.Agent.gen.

Execution

The Umbrij tool was discovered during a proactive threat hunting operation: a scheduled task, KasperskyEndpointSecurityEDRAvp, was running on a user host, launching a digitally signed file. Kaspersky solutions do not create scheduled tasks with that name; the attackers were attempting to masquerade their malicious activity as a legitimate process.

The signed file then used the DLL sideloading technique to load the malicious tool.

Umbrij execution events within Kaspersky Managed Detection and Response

Umbrij execution events within Kaspersky Managed Detection and Response

Throughout our observation period, we identified the following legitimate files vulnerable to the DLL sideloading technique that were used to launch Umbrij:

  1. BDSubWiz.exe: a component of the Submission Wizard in Bitdefender ConnectAgent, which is used to support connection features and interaction with other Bitdefender services or agents. This file insecurely loads a file named log.dll.
  2. VSTestVideoRecorder.exe: a component of the video-recording tool used for testing with Visual Studio (VS Test). This executable insecurely loads a file named Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll.
  3. GoogleDesktop.exe: the discontinued Google Desktop Search application for indexing files and performing quick searches on a local Windows computer. This executable insecurely loads a file named GoogleServices.dll.

These files were used to load different versions of Umbrij; the same legitimate file could be leveraged to launch more than one variant. In total, we discovered three versions of Umbrij, which we refer to as a, b, and c for convenience.

The tool itself is a DLL written in .NET and obfuscated with ConfuserEx, an open-source obfuscator for .NET applications.

Example of an obfuscated code snippet

Example of an obfuscated code snippet

Umbrij is managed with the help of parameters passed through a command line at startup, although it is occasionally executed without any parameters. Below are examples of the command lines observed in attacks against users:

"c:\Users\Public\BDSubWiz.exe" -regex <name> -deepsearch
c:\windows\vss\bds.exe

However, these are not the only parameters the tool can accept and process. During the analysis of its executable code, we discovered additional parameters that vary depending on the version of Umbrij. See the table below for the parameters and their descriptions.

Version Command Description
a -regex <string> Used in conjunction with the -deepsearch parameter. Specifies a substring to search for within the user_name field of the user profile file, which typically contains the email address. The tool will utilize the user profile that matches this specified substring
a -user <username> Specifies the system username under which the tool will run
a -runas-currentuser Configures Umbrij to run within the execution context of the current user
a -deepsearch Enforces additional checks on the user_name field in the user profile: verifying that it is not empty and that it contains the substring specified in the -regex parameter
a, b, c -path <path> Specifies the full path to the directory containing the browser’s executable file
a, b, c -browser <both|msedge|chrome> Specifies which browser the tool should target: Google Chrome, Microsoft Edge, or both
a, b, c -debugport <port> Specifies the remote debugging port number
a, b, c -sync When this parameter is specified in the URL, the value 1095133494869 replaces 279448736670 in the permission request
b -domainAd Specifies the domain name if the user account is a domain account
b -savepdf Instructs Umbrij to save a screenshot of the user profile as a PDF file
c -lport Same as debugport

Environment preparation

At startup, the tool evaluates several prerequisites required to carry out the attack and performs preparatory actions to subsequently compromise the Gmail account.

First, Umbrij verifies the availability of the port that will be designated for browser debugging. To accomplish this, the tool utilizes a function named ChekPortAvailable() (original spelling retained), which accepts the target port number as a parameter. It then retrieves information about active connections on the host using the .NET GetActiveTcpConnections() function from the System.Net.NetworkInformation namespace. The tool iterates through each connection in a loop, comparing the port number to the one it is checking.

The ChekPortAvailable function used to verify open ports

The ChekPortAvailable function used to verify open ports

After this, the tool retrieves the user context. It searches the system for the explorer.exe process and duplicates its token, retaining all of its privileges (T1134.003 Access Token Manipulation: Make and Impersonate Token). This is the exact same mechanism used by another tool in the group’s arsenal, TomBerBil, which we covered previously.

The ImpersonateWithProcess function used to retrieve user context

The ImpersonateWithProcess function used to retrieve user context

By default, Umbrij duplicates the token of the first explorer.exe process it encounters. If multiple users are logged in to the system, the -user <username> switch can be used to specify the name of the target user whose token to duplicate. If the -runas-currentuser switch is specified, the tool will execute within the context of the current user without duplicating any tokens.

Next, Umbrij constructs the path to the browser application folder within the user’s local application data repository. To do this, it uses the Environment.SpecialFolder.LocalApplicationData command to retrieve the repository directory from the environment variable and appends the directory of the target browser. The tool then searches for the Local State file in the following folders:

  • %LOCALAPPDATA%\Google\Chrome\User Data\Local State
  • %LOCALAPPDATA%\Microsoft\Edge\User Data\Local State

See below for an example of the Local State file structure.

Structure of the Local State JSON file

Structure of the Local State JSON file

Within this file, the tool searches for the info_cache array, which stores information about browser user profiles. Umbrij enumerates all user profiles and looks for those containing a user_name field that includes an email address. The presence of an email address indicates that the user is authenticated to a Google service. While the tool can interact with every profile it finds, if the -regex <string> parameter is passed through a command line, it searches for the specified substring within the email addresses being enumerated and proceeds exclusively with those matches.

Next, Umbrij creates the following directories for Google Chrome and Microsoft Edge, respectively:

  • %LOCALAPPDATA%\Google\Chrome\BackupFiles\
  • %LOCALAPPDATA%\Microsoft\Edge\BackupFiles\

The tool copies the following user files and folders of each target user profile into these directories:

  • IndexedDB: a folder containing a relational database used for client-side storage of structured data
  • Local Storage: a component of the browser’s web storage that provides a key-value mechanism for storing data on the client side
  • Network: a folder where the browser stores files related to network requests and caching, such as the network cache and session files
  • Login Data: a file that stores saved passwords for various websites and applications
  • Login Data For Account: a file that stores credentials associated with a Google account or other synchronized accounts within the browser
  • Preferences: a file containing profile-level browser settings
  • Secure Preferences: a file that stores protected configurations, such as security and synchronization data
  • Web Data: a file that stores auto-fill data

If these files are locked by other processes, the tool includes a dedicated function to force-copy them.

The ForceCopyFolder function used to copy files locked by other processes

The ForceCopyFolder function used to copy files locked by other processes

As the next step, the tool searches the “Program Files” and “Program Files (x86)” directories for the browser installation folder. Once it locates the executable file and successfully copies all required files, it is ready to proceed with acquiring the authorization code.

Acquiring the authorization code

In the next phase of execution, Umbrij launches Google Chrome, Microsoft Edge, or both browsers sequentially, depending on the parameters passed in the command line. It then passes arguments to the browser based on the following template:

"\"{1}\" --user-data-dir=\"{0}\" --remote-debugging-port={2}  --profile-directory=\"Default\" --headless https://www.google.com/"

It populates the template with the following values:

  • {0}: the path to \BackupFiles\, where the user profile files were copied
  • {1}: the path to the browser executable file
  • {2}: the remote debugging port number

The table below describes the parameters used in this browser launch template:

Parameter Description
–user-data-dir Specifies the path to the root directory that will store the shared browser data and user profiles
–remote-debugging-port Opens a port for remote browser debugging over the DevTools protocol. This switch is commonly used for automated testing with frameworks like Selenium
–profile-directory Specifies the name of the specific profile folder within the user-data-dir
–headless Launches the browser in headless mode, that is, without a graphical user interface

The browser process runs in headless mode while utilizing the copied user profile. Consequently, all active user cookies are applied, which means sites with saved credentials will skip authentication prompts. Furthermore, the browser will log history to a new folder, keeping it completely hidden from the user’s primary account view.

Through this method, the threat actors gain access to the user’s authenticated sessions — specifically their Google account — along with the ability to erase any trace of their activity within the browser.

Code snippet showing Umbrij connecting to the browser via the debugging port

Code snippet showing Umbrij connecting to the browser via the debugging port

Next, the tool uses the Puppeteer Sharp library, a .NET version of Puppeteer, to connect to the remote debugging port. Puppeteer provides a high-level API to control Chrome or Chromium browsers over the DevTools protocol. Its primary use is for automated testing.

The Puppeteer module GitHub page

The Puppeteer module GitHub page

If the connection to the remote debugging port is successful, Umbrij sends a GET request to direct the browser to the following URL:

https[:]//accounts[.]google[.]com/o/oauth2/v2/auth/identifier?response_type=code&client_id=279448736670.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcalendar%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fcalendar.readonly%20https%3A%2F%2Fwww.google.com%2Fm8%2Ffeeds%2F%20https%3A%2F%2Fwww.google.com%2Fm8%2Ffeeds%2F%20https%3A%2F%2Fmail.google.com%2F%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fgmail.insert%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fgmail.labels%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fadmin.directory.user%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Ftasks%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fadmin.directory.group.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fapps.groups.migration%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.profile&flowName=GeneralOAuthFlow

The value specified in the client_id field belongs to Google Workspace Migration for Microsoft Outlook (GWMMO). This is Google’s official tool for importing email, calendar events, and contacts from Microsoft Exchange accounts or local PST files into a Google Workspace account.

Umbrij also includes the ability to switch the client_id value from 279448736670 to 1095133494869 by using the -sync parameter. This second identifier belongs to another application: Google Workspace Sync for Microsoft Outlook (GWSMO), which allows users to sync email, calendars, and other data from the cloud account directly into Microsoft Outlook.

Code snippet where the client_id replacement occurs

Code snippet where the client_id replacement occurs

The remaining parameters used in the request differ from those typically utilized by the legitimate applications. See the table below for a comparison of these parameters:

GET request parameter URL used by Umbrij Original URL
flowName=GeneralOAuthFlow Present Absent
code_challenge (PKCE) Absent Present (method=S256)
state Absent Present
login_hint Absent Present
redirect_uri http://localhost http://localhost:61619/callback

As seen from the list above, Umbrij omits several parameters characteristic of the legitimate applications. For instance, Umbrij drops the code_challenge parameter, normally used for data protection when retrieving an authorization code. Additionally, the tool modifies the redirection address: while the legitimate application specifies a dedicated port and a callback path, the tool simply points to localhost.

The authorization code request specifies the set of permissions for Google services required by the application. This list also differs significantly between requests issued by the legitimate application and those generated by Umbrij. The table below details the variations in the requested scopes:

Service parameter URL used by Umbrij Original URL
https://www.google.com/m8/feeds/ Present (specified twice) Absent
https://www.googleapis.com/auth/contacts Absent Present
https://www.googleapis.com/auth/admin.directory.resource.calendar.readonly Absent Present
https://www.googleapis.com/auth/peopleapi.readonly Absent Present

After the browser navigates to the URL provided by Umbrij, the Google account selection page opens.

Account selection

Account selection

Because the attackers copied the victim’s profile folder and are operating within their specific environment, the account selection options will include the currently signed-in user’s authenticated session. Umbrij identifies the corresponding element within the page’s HTML source code.

Searching for HTML code elements on the page

Searching for HTML code elements on the page

The tool uses JavaScript to emulate a mouse click on the elements, allowing it to proceed to the next step.

Simulating a mouse click on a page element

Simulating a mouse click on a page element

The subsequent step opens a page displaying the list of requested permissions.

Confirming the list of requested access permissions

Confirming the list of requested access permissions

As shown in the screenshot, Umbrij requests full access to email, cloud storage, and contacts. Just like in the previous step, it uses JavaScript to click the “Allow” button, which completes the authentication process.

The browser is then redirected to the local address that was specified in the redirect_uri parameter of the initial request. The tool intentionally omits a port and a path to a specific page in the redirect_uri because the true objective of this action is simply to capture the code parameter from the context of the GET request. This parameter contains the OAuth authorization code. To retrieve it, Umbrij extracts the substring located between the code= and &scope parameters.

Extracting the authorization code from the GET request

Extracting the authorization code from the GET request

Results

Umbrij, like most other tools in ToddyCat’s arsenal, logs its actions in detail and saves them to a file. It also saves the retrieved authorization code to this log file, which the operator subsequently exfiltrates from the compromised host.

Below is an example of a log file generated by version a of the tool.

------------------------------
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[*] switch to sync mode.
[!] port 11111 is available!
[*] Impersonate <username> success!
[*] browser switch to chrome .
Parsing C:\Users\<username>\AppData\Local\Google\Chrome\User Data\Local State ...
[*] detected profile: Profile 4 ==> <email>@gmail.com
[*] ready auth for <email>@gmail.com.
[*] Browser Exe path C:\Program Files\Google\Chrome\Application\chrome.exe.
[!] CreateProcessAsUserW...
[*] Browser created with pid 3108
[???] <email>@gmail.com
[pup] mail : <email>@gmail.com
[pup] account choice click !
[pup] Allow click !
[<email>@gmail.com] 4%2F0AcvDMrDtzQaC-TT8<hash>uMhg 
[*] RevertToSelf succeed!
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The log indicates that the sync mode is selected (meaning the Google Workspace Sync for Microsoft Outlook application is used) and the debugging port is set to 11111. After locating the user profile and copying its folder, Umbrij launches Google Chrome. After this, the tool emulates clicks on the appropriate buttons to confirm permissions, ultimately outputting the final result of the operation: the stolen OAuth authorization code.

Since all requests occur within a background browser instance, the tool includes a feature to generate a PDF snapshot of the web page where the permission confirmation process halted in the event of an error.

Saving a web page as a PDF file in the case of an error

Saving a web page as a PDF file in the case of an error

Additionally, the tool can create a PDF file for the user profile in Google Chrome and Microsoft Edge by navigating to the following internal addresses:

  • edge://profile-internals
  • chrome://profile-internals
Example contents of a generated PDF file
Example contents of a generated PDF file

Example contents of a generated PDF file

The acquired authorization code is then exchanged for an OAuth access token. The threat actors use that token to connect to the Gmail account through the API, thus compromising corporate email communications. The diagram below illustrates the complete attack workflow.

Umbrij workflow diagram

Umbrij workflow diagram

Detection

DLL sideloading

First and foremost, defenders should monitor library loading events (DLL loads) associated with the known applications vulnerable to DLL sideloading that are exploited by this tool: Bitdefender ConnectAgent, Visual Studio, and Google Desktop Search.

title: Possible Dll Hijacking Of Microsoft VisualStudio QualityTools dll
id: 246f1409-2993-46f6-9b77-e447a327df5d
status: experimental
description: Detects possible DLL hijacking of Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll by looking for suspicious image loads, loading this DLL from unexpected locations
author: kaspersky
date: 2025-08-11
tags:
    - attack.defense-evasion
    - attack.t1574.001
logsource:
    product: windows
    category: image_load
detection:
    selection:
        ImageLoaded|endswith: 'Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll'
    filter:
        ImageLoaded|contains: '\IDE\Extensions\TestPlatform\Extensions\'
    condition: selection
falsepositives:  Legitimate activity
level: high

Browser launch

Launching a browser with a remote debugging port specified is a highly unusual event on standard user hosts that are not running web application development or automated testing workflows. Consequently, monitoring for these specific command-line arguments can serve as a reliable indicator of this attack.

title: Launching Chrome With Debug Parameters
id: f072803f-3cf4-4537-82e6-e8b3a201d99f
status: stable
description: Detects the execution of Chromium based browsers launched with incognito mode and remote debugging enabled
author: kaspersky
date: 2025-12-11
tags:
    - attack.lateral_movement
    - attack.defense_evasion
    - attack.t1550.001
logsource:
    category: process_creation
    product: windows

detection:
    selection:
        CommandLine|contains|all:
            - '--remote-debugging-port'
            - '--headless'
    condition: selection
falsepositives: Opening a browser as part of web application testing. Legitimate activity
level: high

Revoking third-party access

To review the authorization codes granted to applications, navigate to the Google Account settings under the Third-party apps & services section, or access the following URL directly:

https://myaccount.google.com/connections

This page displays a comprehensive list of applications and services that currently have permission to access the account.

List of apps connected to the Google account

List of apps connected to the Google account

If the Google Workspace Migration for Microsoft Outlook or Google Workspace Sync for Microsoft Outlook applications appear in this list but are not actually used within your organization, revoke their access immediately. This will invalidate all potentially compromised OAuth tokens associated with them.

Risk mitigation

Launching a browser with a remote debugging port enabled is inherently suspicious for users who do not engage in web development. For these employees, you can completely disable Chromium-based browser developer tools.

This can be achieved by configuring the DeveloperToolsAvailability policy. To enforce this, set the registry value to 0x00000002 for the following Windows Registry key and restart the browser:

HKLM\Software\Policies\Google\Chrome\DeveloperToolsAvailability

To verify that the policy has been successfully applied, navigate to the browser’s internal policies page at chrome://policy:

Note that while disabling developer tools can successfully disrupt the automated retrieval of the OAuth authorization code, it will not help, however, if the adversary decides to leverage the browser’s graphical user interface (GUI) — though this manual approach is significantly less likely due to the friction it introduces for the attackers. Therefore, as a risk mitigation measure, users should be instructed to explicitly log out of their Google accounts as soon as their sessions are complete.

Takeaways

The ToddyCat APT group continues to search for ways of compromising corporate email communications. We have been tracking the group for a long time and we have observed continuous updates to its arsenal in an attempt to bypass security defenses, even as their core techniques remain consistent. For instance, the group has long relied on DLL sideloading to stealthily drop malicious utilities and scheduled tasks. However, their new tool, Umbrij, automates the attackers’ attempts to gain access to organizational email accounts. This automation not only helps increase the scale and frequency of their attacks but also demonstrates ToddyCat’s strong motivation and advanced technical skills.

To defend against these threats, corporate security teams must monitor for suspicious library loading events initiated by legitimate files, watch for instances of browsers launching in developer mode, and conduct regular audits of third-party applications and services with access permissions to Google accounts. Furthermore, deploying a robust, comprehensive security solution — such as Kaspersky Next — is critical to detect this type of malicious host-based activity in a timely manner.

Indicators of compromise

Additional information about this threat is available to customers of the Kaspersky Threat Intelligence Reporting service. Contact: intelreports@kaspersky.com.

Malicious files
1AB58838E5790EFB22F2D35AB98C0B7D              Umbrij ver. a
A7D7D6C4C3F227F7117261C63B9E23A9              Umbrij ver. a
3D3A621F852C42D97FD7260681E42508              Umbrij ver. a
3432DD9AC0DF80EF86EB80BD080F839B             Umbrij ver. a
22AAEB4946BA6D2F2E27FEB7DBB295DE             Umbrij ver. b
F61FBFB7AA1CD5DC8F70B055B51563E2              Umbrij ver. b
F169D6D172DFB775895A5E2B1540C854              Umbrij ver. c

Legitimate files leveraged for DLL sideloading

MD5 File name Name of DLL being loaded
9F5F2F0FB0A7F5AA9F16B9A7B6DAD89F GoogleDesktop.exe GoogleServices.DLL
28CB7B261F4EB97E8A4B3B0D32F8DEF1 BDSubWiz.exe log.dll
BAE82A15D1DBFB024617B9B56A8E5F66 VSTestVideoRecorder.exe Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll

Paths to DLL sideloading files

Path to the file that loads the DLL Path to the DLL being loaded
C:\Users\<user>\AppData\Local\Temp\BDS.exe C:\Users\<user>\AppData\Local\Temp\log.dll
C:\Users\Public\BDS.exe C:\Users\Public\log.dll
c:\users\public\bdsubwiz.exe C:\Users\Public\log.dll
C:\Windows\Temp\BDS.exe C:\Windows\Temp\log.dll
c:\windows\vss\bds.exe C:\Windows\Vss\log.dll
c:\windows\temp\GoogleDesktop.exe c:\windows\temp\GoogleServices.DLL
c:\windows\temp\VSTestVideoRecorder.exe c:\windows\temp\Microsoft.VisualStudio.QualityTools.VideoRecorderEngine.dll

Wardriving assessment across Mexico: Preparing for the 2026 World Cup

Introduction

Mexico is one of the host countries for the 2026 FIFA World Cup, with matches to be played in three major cities: Mexico City, Monterrey, and Guadalajara. These locations are expected to see a large influx of international visitors, increasing the potential security risks. Many of those risks arise from users connecting to public wireless networks.

To better understand the wireless environments that visitors may encounter, we at Kaspersky GReAT conducted a wardriving assessment in the three host cities. The aim of the study was to analyze characteristics, deployment patterns, security configurations and potential exposure risks of public Wi-Fi infrastructure in urban wireless environments.

The information collected during the assessment was used exclusively for passive observation and infrastructure analysis. No attempts were made to authenticate, intercept communications, exploit systems or interact with the detected wireless networks beyond the publicly broadcast management information.

During processing of the collected data, one step involved filtering out networks belonging to cars or cell phones categorized as mobile hotspots because they do not represent networks that can be considered part of the assessment.

Research scope

The cities included in the study have high population density and extensive wireless infrastructure deployments. We chose areas with the most prominent wireless network activity and highly concentrated public access points. We carried out wardriving research in Monterrey back in 2008, but the city’s hotspot landscape has changed since then.

We chose the following analysis areas for each of the cities:

  1. Mexico City: México City Stadium, Mexico City International Airport, Zócalo, Paseo de la Reforma, Colonia Roma, La Condesa, Polanco, and Coyoacán.
  2. Guadalajara: Guadalajara Stadium, Guadalajara International Airport, the city center, Zapopan, Providencia, Avenida Chapultepec, Colonia Americana, Tlaquepaque, and the area around Andares.
  3. Monterrey: Monterrey Stadium, Monterrey International Airport, Fundidora Park, Cintermex Monterrey, the downtown area, Barrio Antiguo, MacroPlaza, and the San Pedro financial district.

The wireless information was collected using passive wireless reconnaissance techniques. The collected information included:

  • SSID analysis and information exposure, including BSSID-derived SSIDs
  • Default router configurations and ISP deployments
  • Frequency and signal characteristics
  • Channel congestion and spectrum usage
  • Wireless security configurations, including:
    • Open and insecure wireless networks
    • WPS-enabled networks
    • Secure networks (WPA2/WPA3) with WPS enabled

We performed a wireless infrastructure analysis in Mexico City, Guadalajara, and Monterrey. We drove through the areas surrounding the World Cup stadiums, tourist zones, and other places where fan concentrations are likely to be largest. Our goal was to evaluate the security status, deployment characteristics and operational exposure of detected wireless networks.

In total, we recorded 84,588 signals with 69,473 unique Service Set Identifiers (SSIDs) in busy locations and World Cup zones across the three cities. Mexico City accounted for 61.4% of the signals, Guadalajara for 23.6%, and Monterrey for 14.8%. Approximately 82% of the signals had a single SSID (81.9%, 81.34%, and 84% respectively). Notably, they all operate under the IEEE 802.11 standard protocol.

Particular attention was given to identifying standard deployment patterns, legacy configurations, default vendor settings and information disclosure through publicly broadcast wireless identifiers.

The following sections present the results that were obtained by analyzing wireless infrastructure across the three locations.

Our findings

SSID analysis and information exposure

SSID analysis was conducted to evaluate naming conventions, deployment standardization and potential information exposure.

Only a few networks (0.0047%) have an invisible SSID, meaning the names of these networks are not broadcast. Some users prefer to hide the SSID for various reasons, such as the network’s purpose, the profile of its users, internal policies, etc. In contrast, the rest of the networks maintained active SSID broadcasting.

SSID structures may unintentionally disclose operational details about internet service providers (ISPs), device manufacturers, deployment practices, organizational ownership or user identity. The repeated presence of default SSID naming patterns across the analyzed locations indicates a significant degree of infrastructure homogeneity and reuse of default wireless configurations. It may also facilitate passive infrastructure profiling by revealing standard characteristics in use.

Approximately 34% of the detected networks retained the default SSID naming conventions provided by the manufacturer or ISP, while 66% used customized identifiers.

Distribution of SSID naming conventions (download)

Several recurring SSID naming conventions associated with ISP-provided deployments were identified in the three cities. The most frequently observed patterns include identifiers such as “Club_Totalplay_WiFi”, “izzi WiFi”, and “Megacable WiFi”, which suggests extensive standardization of wireless infrastructure deployment. Additionally, we observed distinctive location-specific SSIDs in each area of analysis, such as “XXXX-Internet para Todos-CDMX” or “RED JALISCO”.

Most frequently observed SSID patterns (download)

Sequential SSID naming structures were also identified during the analysis. Patterns such as “INFINITUMXX” and “IZZI-XX” suggest automated ISP deployment and large-scale deployment strategies.

We identified 33 unique sequential naming structures among the 137 sequential SSIDs in total, representing approximately 0.16% of the detected wireless networks.

The following graph shows the top five sequential SSID patterns found in the largest number of networks:

Five most frequently observed sequential patterns (download)

Several customized SSIDs contained personal or organizational identifiers, including family names, professions, addresses or internal department references. Although personalized SSIDs may simplify local network identification for users, they may also expose sensitive information that could be useful for social engineering, physical targeting, or organizational profiling.

BSSID-derived SSID

During the analysis, multiple networks were identified that used the physical MAC address of a Wi-Fi access point (BSSID) as the visible SSID. This practice exposes hardware-level information that could facilitate vendor fingerprinting and targeted reconnaissance activities.

The organizationally unique identifier (OUI) contained in the first bytes of the BSSID identifies the equipment manufacturer. Threat actors can correlate exposed manufacturers with device-specific vulnerabilities.

BSSID-derived SSID by city (download)

Notably, we found that more than 30% of networks in all three cities reuse the MAC address as the SSID.

Default router configurations and ISP deployments

We performed wireless infrastructure profiling to identify the most common wireless equipment manufacturers and ISP deployments across the three locations.

Large-scale ISP deployments frequently use standardized wireless configurations and vendor-specific hardware platforms. Identifying dominant manufacturers and ISP naming conventions can provide insight into infrastructure and deployment practices facilitating the mapping of standardized attack surfaces.

The following figure shows the distribution of the most commonly used manufacturers.

Most frequently observed wireless equipment manufacturers (download)

The manufacturer analysis revealed a strong concentration of wireless infrastructure among a limited number of vendors. Across the three locations, Huawei Technologies, MediaTek-based devices, and other manufacturers’ equipment that is distributed through ISP channels represented a significant portion of the detected deployments. Mexico City had the most diverse infrastructure, while Monterrey and Guadalajara had a greater concentration of wireless equipment known as SOHO (small office/home office) or residential-grade hardware. The widespread presence of standard vendor platforms may facilitate infrastructure fingerprinting and large-scale targeting of known device-specific vulnerabilities.

Most frequently observed wireless equipment manufacturers across the three cities (download)

ISP deployments frequently exhibited standardized configuration patterns and recurring manufacturer identifiers. Our ISP deployment analysis revealed a high concentration of access points associated with major residential internet providers. Deployments associated with Infinitum, Totalplay and Izzi represented a substantial portion of the detected wireless infrastructure across all locations. These findings suggest a high degree of deployment standardization across networks associated with major residential internet providers. This observation was supported by the repeated presence of ISP-associated SSIDs such as “Infinitum”, “Totalplay”, and “Izzi”, combined with manufacturer identifiers frequently associated with consumer equipment, including Huawei, ZTE and other residential wireless equipment vendors.

It is important to note that, for this analysis, ISPs were primarily inferred from SSID naming conventions and manufacturer fingerprint data. A significant portion of the detected wireless networks fell into the “UNKNOWN/CUSTOM” category. This classification includes custom hotspots and networks whose naming conventions did not expose identifiable ISP-associated patterns. The findings suggest that many users and organizations (as we saw previously, approximately 66%) use custom network names, limiting direct provider attribution.

The following figure illustrates the distribution of ISP-associated wireless deployments in general.

Most frequently observed ISPs (download)

To better understand this distribution, we took the most frequently observed ISPs by city.

Most frequently observed ISPs across the three cities (download)

Frequency and signal characteristics

We also analyzed wireless signal characteristics to evaluate coverage quality, signal strength, and frequency band utilization in the three cities. In dense urban environments, signal quality and frequency spectrum distribution can affect wireless reliability, client connectivity, roaming performance, and overall network efficiency.

Signal quality analysis revealed that a substantial portion of the detected access points operated under weak or very weak signal conditions. Monterrey had the highest percentage of very weak signals, with approximately 50% of detected deployments. Similar patterns were observed in Guadalajara and Mexico City, suggesting high-density wireless environments with overlapping coverage areas. Only a limited percentage of networks were classified within the very good or excellent signal categories across the three locations.

Signal quality distribution by city (download)

Signal stability analysis revealed that most detected wireless deployments exhibited stable beacon transmission behavior. More than 96% of the detected access points across all locations were classified as stable, while only a small percentage exhibited unstable or indeterminate signal behavior.

These findings imply that the majority of the wireless infrastructure observed during the assessment corresponded to permanently deployed access points rather than transient or intermittent wireless devices.

Signal stability status (download)

Frequency band analysis revealed the strong prevalence of 2.4 GHz wireless deployments across the three locations. More than 95% of the detected wireless networks operated within the 2.4 GHz spectrum, while only a small percentage of deployments were classified under the unknown or non-standard frequency categories. This uneven distribution reflects the continued prevalence of legacy-compatible wireless infrastructure and SOHO deployments.

Frequency band utilization (download)

These findings are consistent with dense urban wireless environments with large numbers of access points in restricted spectrum allocations.

Channel congestion and spectrum usage

Next, we analyzed wireless channel utilization to evaluate frequency spectrum congestion and channel allocation patterns across the three cities. Our analysis focused on the 2.4 GHz spectrum, where channel overlap and high access point density commonly produce interference and degraded wireless performance. In densely populated wireless environments, an excessive concentration of access points on a limited number of channels can lead to co-channel interference, packet collisions, reduced throughput, and degraded network stability.

Spectrum congestion analysis revealed that the 2.4 GHz band consistently experienced elevated congestion levels across the three cities. The detailed results showed a strong concentration of deployments on channels 11, 6 and 1, which are traditionally recommended as non-overlapping channels within the 2.4 GHz spectrum. Channel 11 was the most utilized channel, accounting for 25.2% of the detected access points, followed by channel 6 with 22.5% and channel 1 with 19.5%. This distribution indicates that most wireless deployments adhere to standard channel allocation practices for 2.4 GHz Wi-Fi environments.

The following figure illustrates the overall distribution of the most frequently utilized wireless channels.

Most utilized wireless channels (download)

To further assess wireless spectrum saturation, the detected access points were grouped according to channel congestion levels: VERY_HIGH, HIGH, UNKNOWN, MEDIUM, LOW and NONE.

Mexico City had the highest proportion of heavily congested wireless channels, with approximately 7% of detected access points operating under HIGH congestion conditions. Guadalajara followed with nearly 5% of deployments categorized as HIGH congestion, while Monterrey had the lowest percentage at approximately 3.29%.

These findings suggest that wireless spectrum saturation increases proportionally with urban infrastructure density and access point concentration. Despite the presence of congested deployments, most detected access points were categorized as LOW or MEDIUM congestion, suggesting severe spectrum saturation was localized rather than uniformly distributed.

Channel congestion by city (download)

A thorough analysis of individual channel utilization revealed that channels 11, 6 and 1 consistently experienced the highest congestion levels across the three cities, which correlates with our previous findings. These channels accounted for the majority of VERY_HIGH congestion classifications, particularly within the 2.4 GHz band.

In Mexico City, channel 11 alone accounted for more than 25% of detected deployments and consistently exhibited VERY_HIGH congestion levels.

This behavior reflects the limited availability of non-overlapping channels within the 2.4 GHz spectrum and the widespread reliance on default wireless configurations.

Most congested channels by city (download)

Overall, the channel utilization analysis showed that wireless deployments are concentrated heavily within the traditional, non-overlapping 2.4 GHz channels. While this strategy reduces adjacent-channel interference, excessive access point density on the same channels can still produce significant co-channel contention and poor wireless performance in high-density urban environments.

Wireless security configurations

The next thing we evaluated was the security posture of the detected wireless networks. We analyzed the wireless security configurations advertised by access points in each of the locations.

Overall security configuration distribution

The analysis revealed that WPA2 was the dominant wireless authentication mechanism across the three cities. Mexico City had the highest WPA2 adoption rate at 81.19%, followed by Monterrey at 79.19% and Guadalajara at 77.59%.

The study found that every 6th open access point (17%) was unsafe, namely 16.5% in Mexico City, 18.5% in Guadalajara, and 17.2% in Monterrey. Open wireless deployments were consistently present across all locations, ranging between 10% and 12% of detected access points. These findings show that despite the widespread deployment of modern wireless security standards, encryption adoption remains incomplete.

Distribution of wireless authentication mechanisms across the three locations (download)

To simplify the interpretation of wireless security posture, we grouped detected networks into four categories:

  • Secure (WPA2/WPA3)
  • Insecure (Open/WEP)
  • Weak (WPA)
  • Unknown

Across the three locations, secure networks comprised most of detected deployments, accounting for approximately 82% of all access points. However, insecure open networks still account for between 10% and 12% of detected wireless infrastructure, consistent with our previous findings. It is important to mention that networks within the unknown category are not considered secure.

Mexico City had the highest percentage of secure deployments at 83.54%, while Guadalajara had the highest percentage of insecure open networks at 12.46%. Although Monterrey had the lowest percentage of insecure networks, open deployments still accounted for more than 10% of the detected access points.

Wireless security posture grouping across the three locations (download)

Although modern WPA2/WPA3 encryption standards dominate current wireless deployments, the continued presence of open and legacy WPA deployments indicates that insecure wireless configurations remain relevant from an operational standpoint. These networks may expose users to passive traffic interception, unauthorized monitoring, rogue access point attacks, and credential harvesting techniques.

WPS-enabled networks

We also analyzed Wi-Fi Protected Setup (WPS) in all the locations to evaluate additional attack surfaces. WPS is a standard feature on wireless routers that enables devices such as printers, repeaters or mobile phones to connect to a secure Wi-Fi network without manually entering a long password, typically through a PIN-based enrolled mechanism. Although WPA2 and WPA3 provide strong encryption mechanisms, the presence of WPS can introduce security weaknesses due to inherently vulnerable PIN-based enrollment methods.

By combining detections from the three locations, we found that 55% of all detected access points did not advertise WPS capabilities, leaving 45% of deployments vulnerable to WPS-based abuse. These results suggest that, despite the adoption of modern encryption standards, a significant portion of wireless infrastructure continues to expose legacy convenience features.

During the analysis, we found that Mexico City had the highest proportion of WPS-enabled networks, with 46.61% of the detected access points advertising WPS capabilities. Guadalajara was second with 43.45%, while Monterrey had the lowest proportion at 40.93%.

The percentage of detected access points advertising WPS capabilities across the three locations (download)

Almost half of the detected wireless networks in each city continued to advertise WPS, indicating that WPS prevalence is consistently high across the three cities.

Secure networks with WPS enabled

In many cases, networks classified as secure because of WPA2/WPA3 encryption still had WPS functionality enabled, which effectively increased the available attack surface.

To further assess the relationship between encryption strength and WPS exposure, we conducted a secondary analysis of secure networks (WPA2/WPA3) only. The results showed that around half of all secure deployments still exposed WPS, with the following breakdown for each city:

  • Mexico City: 53.7%
  • Guadalajara: 50.9%
  • Monterrey: 47.5%

The proportion of secure networks with WPS enabled across the three locations (download)

These findings indicate that encryption strength alone is not enough to evaluate wireless security posture because additional protocol features, such as WPS, may still expose exploitable attack vectors.

Additional security considerations

Overall, travelers operating within dense public environments are exposed not only to insecure wireless infrastructure but also to various risks associated with digital interactions. These risks include many threats, from public USB charging systems and phishing QR codes to proximity-based protocols and exposure to shared public devices, such as interactive totems or kiosks. One particular point that should be taken into account in light of our research is the issue of rogue wireless deployments.

Rogue access points are not necessarily malicious; they may be set up accidentally by misconfiguring router settings. An entry point for potential compromise might be caused by various misconfigurations, from a weak password to an insecure protocol. However, attackers deploy such unauthorized hotspots with malicious intent to infiltrate a network. Threat actors may deploy rogue access points posing as legitimate public wireless networks in airports, hotels, cafés and tourist areas. These deployments are called “evil twins” and can trick users into connecting to attacker-controlled infrastructure capable of intercepting traffic, harvesting credentials, or performing man-in-the-middle attacks. Further risk lies in the potential compromise of local network devices or even malware distribution. Such threats complement our findings, underscoring the importance of implementing traffic encryption, using a security solution and exercising extreme caution while browsing via public networks.

Conclusion

The wardriving assessment conducted in Mexico City, Guadalajara, and Monterrey revealed that modern wireless infrastructure continues to present multiple forms of operational exposure despite the widespread adoption of WPA2 and WPA3 security standards. The analysis demonstrated that wireless environments are highly standardized in all the locations, with recurring ISP deployments, default SSID naming conventions, homogeneous manufacturer distribution, and predictable channel allocation practices observed in all three cities.

Although most of the detected networks were classified as secure under WPA2/WPA3 authentication mechanisms, a significant proportion were exposing additional attack surfaces through enabled WPS functionality, default configurations, sequential SSID structures, and infrastructure metadata disclosure. This demonstrates that encryption strength alone is insufficient for evaluating the overall security posture of wireless infrastructure. Additionally, the prevalence of open networks and legacy wireless configurations indicates that insecure deployments are still operationally relevant in all the locations.

The results also showed that wireless infrastructure is heavily concentrated within the 2.4 GHz spectrum, particularly around channels 11, 6, and 1. This leads to elevated congestion and increased co-channel interference in densely populated urban environments.

SSID analysis further revealed that publicly broadcast wireless identifiers frequently expose valuable operational information about ISPs, equipment manufacturers, deployment templates, organizational ownership, and user-defined naming practices. The identification of default ISP naming conventions, sequential SSID structures, and BSSID-derived SSIDs demonstrated that many deployments prioritize operational convenience and simplicity over exposure minimization and privacy.

The scope of the threats stemming from vulnerable wireless configurations poses serious digital exposure risks for users. The widespread presence of standard deployments, predictable SSID naming and publicly exposed infrastructure identifiers can facilitate passive reconnaissance, infrastructure fingerprinting and opportunistic targeting.

Recommendations

To minimize the risks of wireless-based exposure and the attack surface related to hotspot infrastructure, we recommend taking the following measures:

  • Disable WPS functionality on wireless routers whenever possible, particularly within WPA2/WPA3 deployments.
  • Avoid using default SSID naming conventions that disclose ISP providers, router manufacturers, or deployment templates.
  • Refrain from using personal, organizational, or location-based identifiers in wireless network names.
  • Avoid configuring SSID using BSSID or naming conventions derived from MAC addresses, as these may expose hardware fingerprinting information.
  • Promote migration toward modern WPA3-capable infrastructure while removing legacy wireless protocols when operationally feasible.
  • Reduce wireless congestion by optimizing channel allocation strategies and minimizing excessive dependence on the 2.4 GHz spectrum.
  • Encourage adoption of 5 GHz and newer wireless technologies to reduce interference and improve spectrum efficiency.

The findings presented in this assessment emphasize the importance of combining strong wireless encryption standards, secure deployment practices, exposure minimization strategies, and user awareness to enhance the overall security posture of wireless environments.

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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