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.

One of the biggest football (soccer) events of this summer is the World Cup 2026. The tournament is co-hosted by three countries: the U.S., Canada, and Mexico. Unfortunately, events of this scale attract not just fans, but also scammers from all over the globe. We’ve already covered how cybercriminals are prepping for the World Cup online, and today we’re talking about digital security for fans on the ground in Mexico.

The country will host 13 matches and welcome millions of tourists. They’ll be staying in hotels, heading to games, checking out restaurants, navigating airports, and visiting popular tourist spots — and everywhere they go, the temptation to connect to public Wi-Fi will be high.

We’ve surveyed more than 84 500 (!) public Wi-Fi access points in Mexico City, Guadalajara, and Monterrey — and we have a lot to share about their security. Spoiler alert: many networks are still using outdated security standards, so you really shouldn’t go on vacation without reliable protection and an eSIM.

What and how we tested

Walking across Mexico looking for public Wi-Fi access points would have been a bit tough, though that’s exactly what we did for a similar Wi-Fi security survey in Paris. You can check out the results of that in our post, How safe is Wi-Fi in Paris?

This time the mission was far more demanding: mapping the wireless landscape of three major metropolises. That’s why we went wardriving — scanning for and logging wireless networks from a moving vehicle while equipped with a smartphone or laptop. It’s similar to searching for Wi-Fi on your phone, where the device constantly listens for nearby networks. Except instead of connecting to them, we just collect data about them.

All information was used strictly for passive observation and infrastructure analysis. Beyond receiving publicly broadcast service information, the experts of Kaspersky’s Global Research and Analysis Team (GReAT) didn’t attempt to authenticate, intercept traffic, exploit systems, or otherwise interact with the wireless networks they discovered. Mobile access points deployed in cars and on mobile devices were excluded from the sample.

Our main target was Mexico City — the capital and one of the most densely populated cities in Latin America. We took a drive through popular tourist spots: Mexico City Stadium, Mexico City International Airport, Zócalo, Paseo de la Reforma, Colonia Roma, La Condesa, Polanco, Coyoacán.

In Guadalajara and Monterrey, we drove similar routes: stadiums, main avenues, airports, and popular neighborhoods. Below you can see a heatmap of the areas we covered, ranging from red for areas with the highest density of public access points, through yellow and green, to blue for the lowest concentration.

We used passive radio reconnaissance to log 84 500 signals and 69 500 unique network identifiers across these three cities. The majority of the signals were caught in Mexico City (61.4%), followed by Guadalajara (23.6%) and Monterrey (14.8%).

What we analyzed:

• Wireless network identifiers (SSIDs): the names that show up in your list of available Wi-Fi networks
• Information that can be gleaned from these identifiers
• Default router configurations and how ISPs deploy their networks
• Frequencies used and signal characteristics
• Channel load and radio frequency spectrum usage
• Wireless network security configurations:
  • Open and insecure networks
  • Networks with WPS enabled
  • Secure networks (WPA2/WPA3) with WPS activated

You can find the full version of the study on the Securelist blog.

Telltale public Wi-Fi access point names

Network names (SSIDs) can tell you a lot by unintentionally revealing information about hardware manufacturers, ISPs, deployment methods, and whether an access point belongs to a business or a private user.

About 34% of the public Wi-Fi networks we logged didn’t bother changing their names at all, either sticking with the factory SSIDs from the router manufacturers or using standard naming conventions from their ISPs. For attackers, this can be a pretty solid hint, since this kind of network name lets them know which provider owns a given access point, what hardware is being used, and how it’s likely configured by default.

Another troubling nuance is the large number of Wi-Fi networks (over 30%) that use the access point’s MAC address (BSSID) as the visible network name. The first few bytes of a BSSID contain an Organizationally Unique Identifier (OUI), which gives away the router’s manufacturer. This is a useful lead for bad actors: they can find out who made the hardware and test for vulnerabilities specific to that brand’s models.

Is Mexican Wi-Fi well-protected?

An access point secured with WPA2/WPA3 can be considered more or less safe. All other authentication mechanisms yield much weaker results. We grouped the public Wi-Fi networks into four categories:

• Secure (WPA2/WPA3)
• Unsecured (open/WEP)
• Weak (WPA)
• Undetermined

The results are roughly the same across all three cities: about 82% of all analyzed access points are protected by secure standards. The outdated and insecure WPA protocol was practically nonexistent. However, more than 10% of the access points turned out to be completely unsecured. Connecting to these networks carries the risk of traffic interception and hidden surveillance.

But security isn’t evaluated by WPA protocols alone. We also checked for the presence of WPS, the infamous feature for quickly connecting to a network without entering a password, which is highly vulnerable to attacks. It turned out that WPS is enabled on nearly half (47%) of the access points in Mexico City, 43% in Guadalajara, and 41% in Monterrey. On average, 45% of the access points are potentially vulnerable to WPS-related attacks — sacrificing security for the sake of convenience.

What’s more, this feature frequently remained active even on seemingly secure WPA2/WPA3 networks — about half of them utilized WPS. This shows that having WPA2/WPA3 is still not enough to consider a Wi-Fi access point safe, as additional features like WPS can still leave the door open to attacks.

What else every tourist needs to know

Digital risks on a trip aren’t limited to public Wi-Fi alone, especially now that many are shifting away from public Wi-Fi to an eSIM. There are still plenty of threats in crowded places: public USB chargers, QR codes with swapped links, NFC and Bluetooth attacks, and, of course, social engineering tactics. Let’s break it all down.

Charging stations. Public USB chargers can also be dangerous: bad actors could potentially gain access to the data on your device or try to install malware. We covered these attacks in detail in our post, Data theft during smartphone charging.

Dangerous QR codes. Criminals can plant phishing QR codes in popular tourist spots. The pretexts can vary wildly; for instance, ads for team-specific fan “events”, or links supposedly offering discounts or restaurant menus. In reality, any QR code posted on the street can be considered insecure by default, and you shouldn’t scan them with your smartphone unless you have a QR code threat analyzer installed.

Fake broadcasts, tickets, and betting pools. Earlier, we described cases where bad actors were distributing malware via fake IPTV apps to capitalize on the WC26 hype. Remember, even if you plan to watch the tournament from home, you still need to stay alert and not trust the first sites that pop up advertising free broadcasts, offering betting pools, or promising unbelievably generous payouts.

NFC and Bluetooth attacks. Leaving Bluetooth enabled in crowded places can also cause problems: someone might try to discover your device, track you, or initiate an unwanted pairing request. NFC services with contactless payments create additional risks too — especially when paying in sketchy spots.

How to protect yourself and your devices

Despite the prevalence of secure WPA2/WPA3 public Wi-Fi access points in Mexico City, Guadalajara, and Monterrey, our study shows that public Wi-Fi networks remain vulnerable. It’s also important to remember that attackers can create fake networks — so-called evil twins — disguised as legitimate public Wi-Fi in airports, hotels, cafés, and tourist spots.

For the average user, it’s practically impossible to tell how safe a specific access point is when trying to connect. That’s why the safest option is to use cellular data to access the internet — completely eliminating the need for Wi-Fi. Besides, there’s no need to research the nuances of local laws, rates, and other cellular details for every country you plan to visit; you can just buy a global eSIM online in two clicks. We explained how to make the entire process hassle-free in our post, Internet on the go with Kaspersky eSIM Store.

If you still plan on connecting to public Wi-Fi, always use a VPN to secure your device and data when connecting to unfamiliar — especially unsecured — Wi-Fi networks. This creates an encrypted tunnel between your device and the VPN server, making it impossible to intercept your data along the way. Haven’t picked a VPN yet? Try Kaspersky VPN Secure Connection, which is included with both Kaspersky Premium and Kaspersky Plus subscriptions.

Now, if you still plan to attend the World Cup without any cybersecurity solution, at least follow these basic rules of digital hygiene:

• Don’t use public USB chargers
• Don’t send sensitive information over connections that aren’t secure
• Don’t log in to banking, email, or social media accounts over unsecured Wi-Fi
• Turn off Bluetooth and NFC while walking around in crowded places
• Don’t trust QR codes posted on the street
• Connect to public Wi-Fi only when absolutely necessary

What else to read to make sure cheering for your favorite team isn’t only exciting, but also safe:

• Don’t let fake IPTV apps ruin your World Cup
• Soccer Cyberthreats
• How to watch soccer safely
• Travel safely and comfortably
• Airbnb security: tips for safe travel

Introduction

Containerization using Docker has become firmly established in modern development standards, significantly increasing the speed and convenience of deploying various services. Developers often use ready-made Docker images, making only minimal changes. The largest repository of container images is the Docker Hub service.

Container-hosted infrastructure is an attractive target for attackers. At a minimum, a compromised container can be used for DDoS attacks, cryptocurrency mining, or traffic proxying. The list of threats does not end there: once an attacker gains control of a container, they can steal or destroy data directly from it, access neighboring containers, or even attempt to escape the container, compromising the entire enterprise network.

At the same time, the infrastructure inside containers is typically updated less frequently and may contain outdated and vulnerable software versions. When deploying third-party images or modifying them for a specific environment, it is easy to make configuration errors that attackers can later exploit. And due to the architectural characteristics of containers, developers often face constraints when preparing images; to overcome these, they may resort to insecure solutions they find online.

In other words, containerized infrastructure can be both the simplest and the most lucrative target to exploit. Therefore, its security requires heightened attention. To minimize the risk of successful attacks on container infrastructure, it is essential to check the final Docker images, including all underlying layers, for vulnerabilities and misconfigurations. The easiest way to do this is by analyzing the Dockerfile; however, it is not always available for inspection. Moreover, it typically defines how to build layers on top of a base image from an external repository whose reliability cannot be guaranteed.

To help users identify insecure configurations and potential vulnerabilities within them, we have added our AI assistant to Kaspersky Container Security.KIRA (the assistant’s name) uses artificial intelligence to analyze the image and identify potential issues within, along with recommendations on how to fix them.

As part of this study, we asked KIRA to analyze a number of popular community images, and later in this article, we’ll show you the results.

Software vulnerabilities and compromise of update sources

One of the key security issues with using pre-built images is that developers do not update them in a timely manner. A Docker image is, by its very nature, a snapshot of a specific Linux distribution after packages have been installed on it. However, in most cases, it does not receive security updates on its own, unlike traditional Linux servers, where these updates are automatically installed by specialized services, such as unattended-upgrades in Debian-based distributions and dnf-automatic in RedHat-based distributions.

To apply updates to a Docker image, it must be rebuilt and redeployed. Often, this process is not automated, and some updates require additional effort to verify their correct operation, modify configurations when upgrading to new software versions, and so on. As a result, many popular images do not receive timely updates, which significantly increases the risks associated with their use.

An image that was secure at build time accumulates vulnerabilities as they are discovered in the packages installed within it, which over time significantly increases the opportunities for a successful attack on the container.

Vulnerable versions of web applications and network services accessible from the internet immediately become targets of various malicious campaigns. For example, just one day after the discovery of the CVE-2025-55182 vulnerability in React Server Components, our honeypots recorded numerous attack attempts related to this vulnerability. It was adopted by operators of many malicious campaigns, ranging from classic cryptocurrency miners to variants of Mirai and Gafgyt. Attackers are constantly adding new distribution methods and can use dozens of exploits targeting various vulnerabilities and configuration errors in popular services. Often, the same vulnerabilities are used in self-propagation mechanisms from already compromised hosts. For example, in a malicious campaign to spread the Dero miner, attackers use infected containers to automatically search for and infect new targets.

In addition to vulnerabilities that can be exploited remotely, attackers are rapidly adding local vulnerabilities to their arsenal, used to gain root privileges and escape the container: in the Kinsing malware campaign, attackers used CVE-2023-4911 (Looney Tunables) to elevate privileges, and in the perfctl campaign, the CVE-2021-4034 (PwnKit) vulnerability was used for the same purpose. The access gained was used to install a rootkit that hides the presence of perfctl on the system.

To assess the situation with unpatched vulnerabilities in containers, we took a random sample of 100 images, which included various popular solutions with 10,000 to 1 million downloads on DockerHub. In the 64 images we scanned, we found outdated software versions with critical vulnerabilities. For example, some images contained the CVE-2025-49844 vulnerability in the Redis server, leading to RCE by leveraging a vulnerability in the Lua parser; the current CVE-2026-24061 vulnerability in nginx, which in some configurations leads to a server process crash, and with ASLR disabled, again, to RCE; vulnerabilities CVE-2025-32463 in sudo and CVE-2023-4911 in glibc, allowing an attacker to gain root privileges with local access. At the same time, only one in ten Docker images from the analyzed sample is fully up to date.

It is worth noting that, of course, not every discovered vulnerability can be directly exploited by attackers. A practical risk arises when the vulnerable application or library is actually in use, and the conditions necessary for exploitation – which vary significantly from vulnerability to vulnerability – are met. Nevertheless, updates must not be ignored, as the risk of vulnerabilities being exploited – both individually and in various combinations – cannot be predicted in each specific case, and even vulnerabilities that seem harmless at first glance can ultimately pose a serious risk of compromise.

However, frequent updates have a downside. Every rebuild that downloads new packages from source repositories introduces an additional risk of a supply chain attack – a compromised dependency or a modified base image could silently inject malicious code into your environment precisely through an update. During our analysis of images from the sample, we did not find any signs of supply chain attacks. However, in March 2026, a supply chain incident occurred in the Trivy and LiteLLM projects. In the case of Trivy, the infected file was injected directly into the container image in the official repositories.

This leads to a difficult choice: infrequent updates leave known vulnerabilities unpatched within the image, while frequent updates increase the risk of supply chain compromise. Therefore, to protect your infrastructure, you need not only to regularly update base images but also to take a more comprehensive approach, specifically by pinning dependencies to known-good versions and scanning the resulting images for malware upon update.

Configuration vulnerabilities

Even a container with a fully updated image can be compromised if it is configured incorrectly. Embedding keys and secrets in the image, disabling authentication in network services, default passwords, and insecure file access permissions – all of these can be exploited by attackers in one way or another to achieve their goals.

The situation is exacerbated by the fact that errors may be introduced by the authors of the original image, which complicates their detection, as this requires analyzing every layer and the command that generated it. As with vulnerabilities, not every configuration error leads to compromise: it all depends on the container’s role, its network accessibility, and many other factors. But the very use of insecure settings will sooner or later lead to errors appearing in images where their consequences will be significantly more dangerous.

Standard rules are often insufficient for analyzing problematic configurations. To gain a deeper understanding of the context and assess potential risks, AI tools can be used. Later in this section, we will examine examples of typical insecure configurations we discovered while scanning public images from Docker Hub, along with the descriptions of issues and risk mitigation methods provided by the KIRA AI assistant.

Insecure handling of credentials

Use of default passwords

In some cases, containers may use default passwords set via environment variables or directly in Dockerfile. If these passwords are not overridden, attackers will be able to access the application by using the default password.

RUN |1 DEBIAN_FRONTEND=noninteractive /bin/sh -c echo [removed]:[removed] | chpasswd

According to KIRA’s analysis, the user’s password is stored in plain text in the image layer history. Anyone who gains access to the image – whether through a public registry, a compromised build environment, or other means – will be able to extract the password. If SSH or another form of interactive access is enabled in the container, this could lead to its complete compromise and allow attackers to move laterally within the infrastructure.

Passwords may be present in environment variables. Consider the following Dockerfile snippet:

ENV SERVERNAME=localhost WWW_PATH_CONF=/etc/apache2/apache2.conf WWW_PATH_ROOT=/var/www HTTPS=on PKP_CLI_INSTALL=0 PKP_DB_HOST=db PKP_DB_NAME=pkp PKP_DB_USER=pkp PKP_DB_PASSWORD=changeMePlease PKP_WEB_CONF=/etc/apache2/conf-enabled/pkp.conf PKP_CONF=config.inc.php PKP_CMD=/usr/local/bin/pkp-start

In this example, the environment variable PKP_DB_PASSWORD is set to changeMePlease. If the user forgets to override it, the application will use the password that can be obtained from Dockerfile.

Let’s look at another image:

/bin/sh -c #(nop)  ENV MOODLE_URL=<a href="http://0.0.0.0/">http://0.0.0.0</a> MOODLE_ADMIN admin       MOODLE_ADMIN_PASSWORD [removed]      MOODLE_ADMIN_EMAIL admin@example.com MOODLE_DB_HOST     MOODLE_DB_PASSWORD       MOODLE_DB_USER     MOODLE_DB_NAME    MOODLE_DB_PORT 3306

For this image, Dockerfile specifies that the administrator password is hardcoded in the ENV directive and remains in the image metadata (layer history, docker inspect). Anyone who gains access to the image (registry, build cache) will be able to extract this secret and compromise the account.

To eliminate these risks, ensure that no passwords are specified in Dockerfile. If authentication is required, you can use orchestrator mechanisms (secrets) or generate a temporary password when starting the container via the entrypoint script, without saving it in the layers. We also recommend using mechanisms for securely passing secrets at runtime (Docker secrets, Kubernetes Secrets) or, as a last resort, passing them via --secret during the build with BuildKit, but under no circumstances should they be left in the final image.

Passing passwords via command arguments

In some cases, passwords may be exposed when passed via command-line arguments, as these arguments are visible to all users on the system:

/bin/sh -c #(nop)  HEALTHCHECK &{[""CMD-SHELL"" ""mysql --protocol TCP -u\""root\"" -p\""$MYSQL_ROOT_PASSWORD\"" -e \""SELECT 1;\""""] ""15s"" ""30s"" ""0s"" '\x05'}

In the example provided, the MySQL superuser password is passed into the healthcheck command in plaintext, making it visible when viewing the process list (ps aux), in audit logs, and in monitoring systems. If the attacker gains read access to the container’s processes or logs, they can extract the password and gain full control of the database.

To fix this issue, the healthcheck should use a local connection via a Unix socket with default authentication (if the auth_socket plugin is configured for root), or create a dedicated user with minimal privileges (e.g., only USAGE), without a password or with a password passed via a secure file (--defaults-file with restricted permissions). You can also use the MYSQL_PWD environment variable for healthcheck authentication, but it remains visible in /proc.

Privilege escalation in the container

One of the most common vectors for initial compromise of Linux systems is RCE in web applications and network services. Typically, these services have minimal privileges, which complicates attackers’ subsequent actions: dumping credentials, covering their tracks, attempting to escape the container, and much more.

The situation worsens significantly if the attacker gains root privileges, as this allows them to fully control all processes within the container, conceal their activity, and use methods to escape the container. For example, they can compromise the host if the container is privileged, a Docker socket is mounted inside it, or other insecure configurations and vulnerabilities exist that cannot be exploited with standard user privileges.

Similarly, this simplifies network attacks on neighboring containers, the orchestrator, and various internal services, making this configuration error a potential link in the chain for compromising the entire network.

Attacks on sudo

One of the simplest privilege escalation methods is executing arbitrary commands as root using sudo without entering a password. Consider the following example:

/bin/sh -c set -xe;     apt-get update &&       apt-get -y install sudo;       echo ""solr ALL=(ALL) NOPASSWD: ALL"" >/etc/sudoers.d/solr;

Analyzing this configuration using KIRA immediately highlights the main issue: by installing the sudo package and setting NOPASSWD: ALL for the solr, the user severely violates the principle of least privilege. The Solr platform does not require such broad privileges to run within a container; instead, they create an easy path for escalating to root.

echo 'postgres ALL=(ALL:ALL) NOPASSWD:ALL' >> /etc/sudoers

In another example of an insecure configuration, NOPASSWD:ALL privileges are granted to a PostgreSQL database user, which is a direct and severe weakening of the access control policy. If an attacker gains the ability to execute code on behalf of the postgres user – through a vulnerability in a network service, an SQL injection, or by compromising of one of the processes – they will immediately and unconditionally be able to execute any commands on behalf of the root user. This is equivalent to the entire container running as root.

As a risk mitigation measure, we recommend completely removing this directive. The minimum necessary commands requiring privileges should be delegated on a case-by-case basis via sudoers with explicit specification of allowed executables and parameters, using NOPASSWD only as a last resort and for specific utilities.

Our AI assistant KIRA can identify even more complex insecure configurations, such as allowing passwordless sudo for the entire sudo group — by modifying existing rules.

perl -i -pe 's/\bALL$/NOPASSWD:ALL/g' /etc/sudoers

The risk in this example is that the command replaces standard declarations requiring authentication with passwordless execution of all commands for any user within the sudo group – potentially including postgres, should it be assigned to that group. This expands the attack surface to all group members, turning each of them into a potential point for instant privilege escalation.

To mitigate the risks, we recommend not modifying the global sudoers policy, keeping the standard password requirement, or using a more secure escalation mechanism – such as gosu to run a specific process on behalf of another user without permanent privileges.

Insecure file permissions

Another common vector for privilege escalation is insecurely configured file and directory permissions. Most often, for convenience, container image authors use 777 permissions, which allow anyone – including unprivileged users – to freely create and delete files, as well as modify their contents. This can lead to both privilege escalation and the ability for an unprivileged attacker to delete or modify logs, among other undesirable consequences.

Consider the following command:

chmod 0777 /usr/share/cargo /usr/share/cargo/bin

The risk is that directories containing binary files and scripts will become writable by any container user. This allows a low-privileged attacker to replace utilities included in cargo or add new malicious executables. When these tools are subsequently invoked, especially as the root user or via sudo, the attacker’s code will execute with the inherited privileges of the calling process, leading directly to a local privilege escalation.

To mitigate the risks, you can set the minimum necessary permissions: chmod 0755 for directories and chmod 0755/0644 for the corresponding files. The owner should be root, and only the owner should be allowed to write. Do not use chmod 777 on any system paths.

Lack of integrity checks

Downloading software without verifying its integrity can make the infrastructure vulnerable to software tampering.

For example, this risk may arise when downloading a distribution via HTTP:

RUN /bin/sh -c wget -qO- ""<a href="http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz">http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz</a>"" | tar --extract --gzip -C /opt/acestream

Using HTTP without verifying the archive’s integrity creates conditions for a man-in-the-middle attack during the image build phase. An attacker controlling the communication channel or DNS can replace the archive with malicious content, which will compromise the container and the entire environment in which it runs.

To mitigate the risks, you can configure connections to web resources to use HTTPS only — if the resource supports this protocol. You can also download the archive without extracting it, compare its checksum (SHA256) with the checksum from a trusted source, and only then extract it. It is advisable to store the verified archive in an internal artifact repository to avoid direct downloads from the network.

There will still be a MitM risk even if certificate verification is disabled:

wget --no-check-certificate<a href="https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip"> https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip</a> -O phpvirtualbox.zip

The absence of TLS certificate verification allows an attacker controlling the network segment to replace the downloaded ZIP archive with malicious content. Since the archive contains PHP code that will be executed by the web server, compromise during the build phase will result in the deployment of a backdoor or data leakage.

To mitigate the risks, remove the --no-check-certificate flag; after downloading, calculate the SHA256 hash of the archive and verify it against a known reference value (the release page or a local repository of trusted hashes). Additionally, consider using a fixed release (tag) rather than the floating 7.2-dev branch.

Conclusion

Docker containers have become a very popular means of deploying software, and attackers are by no means oblivious to this trend. They are rapidly adding software vulnerabilities and configuration errors to their arsenal and carrying out attacks on supply chains. They can compromise container infrastructure for a wide variety of purposes, from cryptocurrency mining to encrypting data for ransom or stealing information critical to the company.

Our research found that 64 out of 100 container images for popular applications contain critically vulnerable software, and only 10% are fully up to date. We also identified numerous insecure configurations, including passwords stored in plaintext in Dockerfiles and excessive privileges granted to users and processes.

To detect and prevent these threats, it is essential to strictly adhere to security measures: audit image configurations, securely manage secrets used in images, apply security updates in a timely manner, scan their contents for malware with every update, and follow industry-standard best practices for enhancing security.

This approach requires specialized solutions built to accommodate the unique characteristics of container environments. Kaspersky Container Security ensures the security of containerized applications at every stage of their lifecycle, from development to operation. The product protects an organization’s business processes, helps ensure compliance with industry standards and security regulations, and enables the implementation of secure software development practices.

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.

If you don’t go searching for AI services, they’ll find you all the same. Every major tech company feels a moral obligation not just to develop an AI assistant, integrated chatbot, or autonomous agent, but to bake it into their existing mainstream products and forcibly activate it for tens of millions of users. Here are just a few examples from the last six months:

• Microsoft is forcibly turning compatible Windows PCs into “AI PCs”, and automatically installing and activating Copilot for anyone with Microsoft 365 desktop apps.
• Google activated Gemini for all U.S. Chrome users, cranked its browser functionality to the max, aggressively expanded the reach of AI Overviews in search results, and baked a whole suite of AI features into its online services (Gmail, Google Docs, and others).
• Apple integrated its own Apple Intelligence (conveniently sharing the AI acronym) into the latest OS versions across all device types and most of its native apps.
• Meta added AI translations and a Meta AI chat to WhatsApp, while simultaneously banning third-party chatbots in the messaging app as of January 15, 2026.

On the flip side, geeks have rushed to build their own “personal Jarvises” by renting VPS instances or hoarding Mac minis to run the OpenClaw AI agent. Unfortunately, OpenClaw’s security issues with default settings turned out to be so massive that it’s already been dubbed the biggest cybersecurity threat of 2026.

Beyond the sheer annoyance of having something shoved down your throat, this AI epidemic brings some very real practical risks and headaches. AI assistants hoover up every bit of data they can get their hands on, parsing the context of the websites you visit, analyzing your saved documents, reading through your chats, and so on. This gives AI companies an unprecedentedly intimate look into every user’s life.

A leak of this data during a cyberattack — whether from the AI provider’s servers or from the cache on your own machine — could be catastrophic. These assistants can see and cache everything you can, including data usually tucked behind multiple layers of security: banking info, medical diagnoses, private messages, and other sensitive intel. We took a deep dive into how this plays out when we broke down the issues with the AI-powered Copilot+ Recall system, which Microsoft also planned to force-feed to everyone. On top of that, AI can be a total resource hog, eating up RAM, GPU cycles, and storage, which often leads to a noticeable hit to system performance.

For those who want to sit out the AI storm and avoid these half-baked, rushed-to-market neural network assistants, we’ve put together a quick guide on how to kill the AI in popular apps and services.

How to disable AI in Google Docs, Gmail, and Google Workspace

Google’s AI assistant features in Mail and Docs are lumped together under the umbrella of “smart features”. In addition to the large language model, this includes various minor conveniences, like automatically adding meetings to your calendar when you receive an invite in Gmail. Unfortunately, it’s an all-or-nothing deal: you have to disable all of the “smart features” to get rid of the AI.

To do this, open Gmail, click the Settings (gear) icon, and then select See all settings. On the General tab, scroll down to Google Workspace smart features. Click Manage Workspace smart feature settings and toggle off two options: Smart features in Google Workspace and Smart features in other Google products. We also recommend unchecking the box next to Turn on smart features in Gmail, Chat, and Meet on the same general settings tab. You’ll need to restart your Google apps afterward (which usually happens automatically).

How to disable AI Overviews in Google Search

You can kill off AI Overviews in search results on both desktops and smartphones (including iPhones), and the fix is the same across the board. The simplest way to bypass the AI overview on a case-by-case basis is to append -ai to your search query — for example, how to make pizza -ai. Unfortunately, this method occasionally glitches, causing Google to abruptly claim it found absolutely nothing for your request.

If that happens, you can achieve the same result by switching the search results page to Web mode. To do this, select the Web filter immediately below the search bar — you’ll often find it tucked away under the More button.

A more radical solution is to jump ship to a different search engine entirely. For instance, DuckDuckGo not only tracks users less and shows little ads, but it also offers a dedicated AI-free search — just bookmark the search page at noai.duckduckgo.com.

How to disable AI features in Chrome

Chrome currently has two types of AI features baked in. The first communicates with Google’s servers and handles things like the smart assistant, an autonomous browsing AI agent, and smart search. The second handles locally more utility-based tasks, such as identifying phishing pages or grouping browser tabs. The first group of settings is labeled AI mode, while the second contains the term Gemini Nano.

To disable them, type chrome://flags into the address bar and hit Enter. You’ll see a list of system flags and a search bar; type “AI” into that search bar. This will filter the massive list down to about a dozen AI features (and a few other settings where those letters just happen to appear in a longer word). The second search term you’ll need in this window is “Gemini“.

After reviewing the options, you can disable the unwanted AI features — or just turn them all off — but the bare minimum should include:

• AI Mode Omnibox entrypoint
• AI Entrypoint Disabled on User Input
• Omnibox Allow AI Mode Matches
• Prompt API for Gemini Nano
• Prompt API for Gemini Nano with Multimodal Input

Set all of these to Disabled.

How to disable AI features in Firefox

While Firefox doesn’t have its own built-in chatbots and hasn’t (yet) tried to force upon users agent-based features, the browser does come equipped with smart-tab grouping, a sidebar for chatbots, and a few other perks. Generally, AI in Firefox is much less “in your face” than in Chrome or Edge. But if you still want to pull the plug, you’ve two ways to do it.

The first method is available in recent Firefox releases — starting with version 148, a dedicated AI Controls section appeared in the browser settings, though the controls are currently a bit sparse. You can use a single toggle to completely Block AI enhancements, shutting down AI features entirely. You can also specify whether you want to use On-device AI by downloading small local models (currently just for translations) and configure AI chatbot providers in sidebar, choosing between Anthropic Claude, ChatGPT, Copilot, Google Gemini, and Le Chat Mistral.

The second path — for older versions of Firefox — requires a trip into the hidden system settings. Type about:config into the address bar, hit Enter, and click the button to confirm that you accept the risk of poking around under the hood.

A massive list of settings will appear along with a search bar. Type “ML” to filter for settings related to machine learning.

To disable AI in Firefox, toggle the browser.ml.enabled setting to false. This should disable all AI features across the board, but community forums suggest this isn’t always enough to do the trick. For a scorched-earth approach, set the following parameters to false (or selectively keep only what you need):

• ml.chat.enabled
• ml.linkPreview.enabled
• ml.pageAssist.enabled
• ml.smartAssist.enabled
• ml.enabled
• ai.control.translations
• tabs.groups.smart.enabled
• urlbar.quicksuggest.mlEnabled

This will kill off chatbot integrations, AI-generated link descriptions, assistants and extensions, local translation of websites, tab grouping, and other AI-driven features.

How to disable AI features in Microsoft apps

Microsoft has managed to bake AI into almost every single one of its products, and turning it off is often no easy task — especially since the AI sometimes has a habit of resurrecting itself without your involvement.

How to disable AI features in Edge

Microsoft’s browser is packed with AI features, ranging from Copilot to automated search. To shut them down, follow the same logic as with Chrome: type edge://flags into the Edge address bar, hit Enter, then type “AI” or “Copilot” into the search box. From there, you can toggle off the unwanted AI features, such as:

• Enable Compose (AI-writing) on the web
• Edge Copilot Mode
• Edge History AI

Another way to ditch Copilot is to enter edge://settings/appearance/copilotAndSidebar into the address bar. Here, you can customize the look of the Copilot sidebar and tweak personalization options for results and notifications. Don’t forget to peek into the Copilot section under App-specific settings — you’ll find some additional controls tucked away there.

How to disable Microsoft Copilot

Microsoft Copilot comes in two flavors: as a component of Windows (Microsoft Copilot), and as part of the Office suite (Microsoft 365 Copilot). Their functions are similar, but you’ll have to disable one or both depending on exactly what the Redmond engineers decided to shove onto your machine.

The simplest thing you can do is just uninstall the app entirely. Right-click the Copilot entry in the Start menu and select Uninstall. If that option isn’t there, head over to your installed apps list (Start → Settings → Apps) and uninstall Copilot from there.

In certain builds of Windows 11, Copilot is baked directly into the OS, so a simple uninstall might not work. In that case, you can toggle it off via the settings: Start → Settings → Personalization → Taskbar → turn off Copilot.

If you ever have a change of heart, you can always reinstall Copilot from the Microsoft Store.

It’s worth noting that many users have complained about Copilot automatically reinstalling itself, so you might want to do a weekly check for a couple of months to make sure it hasn’t staged a comeback. For those who are comfortable tinkering with the System Registry (and understand the consequences), you can follow this detailed guide to prevent Copilot’s silent resurrection by disabling the SilentInstalledAppsEnabled flag and adding/enabling the TurnOffWindowsCopilot parameter.

How to disable Microsoft Recall

The Microsoft Recall feature, first introduced in 2024, works by constantly taking screenshots of your computer screen and having a neural network analyze them. All that extracted information is dumped into a database, which you can then search using an AI assistant. We’ve previously written in detail about the massive security risks Microsoft Recall poses.

Under pressure from cybersecurity experts, Microsoft was forced to push the launch of this feature from 2024 to 2025, significantly beefing up the protection of the stored data. However, the core of Recall remains the same: your computer still remembers your every move by constantly snapping screenshots and OCR-ing the content. And while the feature is no longer enabled by default, it’s absolutely worth checking to make sure it hasn’t been activated on your machine.

To check, head to the settings: Start → Settings → Privacy & Security → Recall & snapshots. Ensure the Save snapshots toggle is turned off, and click Delete snapshots to wipe any previously collected data, just in case.

You can also check out our detailed guide on how to disable and completely remove Microsoft Recall.

How to disable AI in Notepad and Windows context actions

AI has seeped into every corner of Windows, even into File Explorer and Notepad. You might even trigger AI features just by accidentally highlighting text in an app — a feature Microsoft calls “AI Actions”. To shut this down, head to Start → Settings → Privacy & Security → Click to Do.

Notepad has received its own special Copilot treatment, so you’ll need to disable AI there separately. Open the Notepad settings, find the AI features section, and toggle Copilot off.

Finally, Microsoft has even managed to bake Copilot into Paint. Unfortunately, as of right now, there is no official way to disable the AI features within the Paint app itself.

How to disable AI in WhatsApp

In several regions, WhatsApp users have started seeing typical AI additions like suggested replies, AI message summaries, and a brand-new Chat with Meta AI button. While Meta claims the first two features process data locally on your device and don’t ship your chats off to their servers, verifying that is no small feat. Luckily, turning them off is straightforward.

To disable Suggested Replies, go to Settings → Chats → Suggestions & smart replies and toggle off Suggested replies. You can also kill off AI Sticker suggestions in that same menu. As for the AI message summaries, those are managed in a different location: Settings → Notifications → AI message summaries.

How to disable AI on Android

Given the sheer variety of manufacturers and Android flavors, there’s no one-size-fits-all instruction manual for every single phone. Today, we’ll focus on killing off Google’s AI services — but if you’re using a device from Samsung, Xiaomi, or others, don’t forget to check your specific manufacturer’s AI settings. Just a heads-up: fully scrubbing every trace of AI might be a tall order — if it’s even possible at all.

In Google Messages, the AI features are tucked away in the settings: tap your account picture, select Messages settings, then Gemini in Messages, and toggle the assistant off.

Broadly speaking, the Gemini chatbot is a standalone app that you can uninstall by heading to your phone’s settings and selecting Apps. However, given Google’s master plan to replace the long-standing Google Assistant with Gemini, uninstalling it might become difficult — or even impossible — down the road.

If you can’t completely uninstall Gemini, head into the app to kill its features manually. Tap your profile icon, select Gemini Apps activity, and then choose Turn off or Turn off and delete activity. Next, tap the profile icon again and go to the Connected Apps setting (it may be hiding under the Personal Intelligence setting). From here, you should disable all the apps where you don’t want Gemini poking its nose in.

• For more on dealing with preloaded and system apps, check out our post, Delete the undeletable: how to disable and remove Android bloatware.

How to disable AI in macOS and iOS

Apple’s platform-level AI features, collectively known as Apple Intelligence, are refreshingly straightforward to disable. In your settings — on desktops, smartphones, and tablets alike — simply look for the section labeled Apple Intelligence & Siri. By the way, depending on your region and the language you’ve selected for your OS and Siri, Apple Intelligence might not even be available to you yet.

Other posts to help you tune the AI tools on your devices:

• Privacy settings in ChatGPT
• DeepSeek: configuring privacy and deploying a local version
• The pros and cons of AI-powered browsers
• Is a Gemini AI update about to kill privacy on your Android device?
• Should you disable Microsoft Recall in 2025?

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

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

What is ExifTool?

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

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

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

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

How CVE-2026-3102 works

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

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

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

How to protect against the ExifTool vulnerability

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

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

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

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

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

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

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

Unit 42 research reveals most OT attacks begin in IT. Learn how edge-driven defense stops threats early and turns dwell time into advantage.

The post Bring the Fight to the Edge: Turning Time Into an Advantage in OT Security appeared first on Unit 42.

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

New languages of love

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

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

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

🤫❤️🫵

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

🫵🚶➡️💋🌹🍝🍷❓

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

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

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

Dating an AI

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

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

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

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

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

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

Love by design: dating algorithms

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

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

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

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

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

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

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

How to stay safe while dating online

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

Deepfakes and catfishing

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

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

Phishing and scams

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

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

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

Stalking and doxing

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

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

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

Sextortion and nudes

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

More on love, security (and robots):

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

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

Technologies for creating fake video and voice messages are accessible to anyone these days, and scammers are busy mastering the art of deepfakes. No one is immune to the threat — modern neural networks can clone a person’s voice from just three to five seconds of audio, and create highly convincing videos from a couple of photos. We’ve previously discussed how to distinguish a real photo or video from a fake and trace its origin to when it was taken or generated. Now let’s take a look at how attackers create and use deepfakes in real time, how to spot a fake without forensic tools, and how to protect yourself and loved ones from “clone attacks”.

How deepfakes are made

Scammers gather source material for deepfakes from open sources: webinars, public videos on social networks and channels, and online speeches. Sometimes they simply call identity theft targets and keep them on the line for as long as possible to collect data for maximum-quality voice cloning. And hacking the messaging account of someone who loves voice and video messages is the ultimate jackpot for scammers. With access to video recordings and voice messages, they can generate realistic fakes that 95% of folks are unable to tell apart from real messages from friends or colleagues.

The tools for creating deepfakes vary widely, from simple Telegram bots to professional generators like HeyGen and ElevenLabs. Scammers use deepfakes together with social engineering: for example, they might first simulate a messenger app call that appears to drop out constantly, then send a pre-generated video message of fairly low quality, blaming it on the supposedly poor connection.

In most cases, the message is about some kind of emergency in which the deepfake victim requires immediate help. Naturally the “friend in need” is desperate for money, but, as luck would have it, they’ve no access to an ATM, or have lost their wallet, and the bad connection rules out an online transfer. The solution is, of course, to send the money not directly to the “friend”, but to a fake account, phone number, or cryptowallet.

Such scams often involve pre-generated videos, but of late real-time deepfake streaming services have come into play. Among other things, these allow users to substitute their own face in a chat-roulette or video call.

How to recognize a deepfake

If you see a familiar face on the screen together with a recognizable voice but are asked unusual questions, chances are it’s a deepfake scam. Fortunately, there are certain visual, auditory, and behavioral signs that can help even non-techies to spot a fake.

Visual signs of a deepfake

Lighting and shadow issues. Deepfakes often ignore the physics of light: the direction of shadows on the face and in the background may not match, and glares on the skin may look unnatural or not be there at all. Or the person in the video may be half-turned toward the window, but their face is lit by studio lighting. This example will be familiar to participants in video conferences, where substituted background images can appear extremely unnatural.

Blurred or floating facial features. Pay attention to the hairline: deepfakes often show blurring, flickering, or unnatural color transitions along this area. These artifacts are caused by flaws in the algorithm for superimposing the cloned face onto the original.

Unnaturally blinking or “dead” eyes. A person blinks on average 10 to 20 times per minute. Some deepfakes blink too rarely, others too often. Eyelid movements can be too abrupt, and sometimes blinking is out of sync, with one eye not matching the other. “Glassy” or “dead-eye” stares are also characteristic of deepfakes. And sometimes a pupil (usually just the one) may twitch randomly due to a neural network hallucination.

When analyzing a static image such as a photograph, it’s also a good idea to zoom in on the eyes and compare the reflections on the irises — in real photos they’ll be identical; in deepfakes — often not.

Lip-syncing issues. Even top-quality deepfakes trip up when it comes to synchronizing speech with lip movements. A delay of just a hundred milliseconds is noticeable to the naked eye. It’s often possible to observe an irregular lip shape when pronouncing the sounds m, f, or t. All of these are telltale signs of an AI-modeled face.

Static or blurred background. In generated videos, the background often looks unrealistic: it might be too blurry; its elements may not interact with the on-screen face; or sometimes the image behind the person remains motionless even when the camera moves.

Odd facial expressions. Deepfakes do a poor job of imitating emotion: facial expressions may not change in line with the conversation; smiles look frozen, and the fine wrinkles and folds that appear in real faces when expressing emotion are absent — the fake looks botoxed.

Auditory signs of a deepfake

Early AI generators modeled speech from small, monotonous phonemes, and when the intonation changed, there was an audible shift in pitch, making it easy to recognize a synthesized voice. Although today’s technology has advanced far beyond this, there are other signs that still give away generated voices.

Wooden or electronic tone. If the voice sounds unusually flat, without natural intonation variations, or there’s a vaguely electronic quality to it, there’s a high probability you’re talking to a deepfake. Real speech contains many variations in tone and natural imperfections.

No breathing sounds. Humans take micropauses and breathe in between phrases — especially in long sentences, not to mention small coughs and sniffs. Synthetic voices often lack these nuances, or place them unnaturally.

Robotic speech or sudden breaks. The voice may abruptly cut off, words may sound “glued” together, and the stress and intonation may not be what you’re used to hearing from your friend or colleague.

Lack of… shibboleths in speech. Pay attention to speech patterns (such as accent or phrases) that are typical of the person in real life but are poorly imitated (if at all) by the deepfake.

To mask visual and auditory artifacts, scammers often simulate poor connectivity by sending a noisy video or audio message. A low-quality video stream or media file is the first red flag indicating that checks are needed of the person at the other end.

Behavioral signs of a deepfake

Analyzing the movements and behavioral nuances of the caller is perhaps still the most reliable way to spot a deepfake in real time.

Can’t turn their head. During the video call, ask the person to turn their head so they’re looking completely to the side. Most deepfakes are created using portrait photos and videos, so a sideways turn will cause the image to float, distort, or even break up. AI startup Metaphysic.ai — creators of viral Tom Cruise deepfakes — confirm that head rotation is the most reliable deepfake test at present.

Unnatural gestures. Ask the on-screen person to perform a spontaneous action: wave their hand in front of their face; scratch their nose; take a sip from a cup; cover their eyes with their hands; or point to something in the room. Deepfakes have trouble handling impromptu gestures — hands may pass ghostlike through objects or the face, or fingers may appear distorted, or move unnaturally.

Screen sharing. If the conversation is work-related, ask your chat partner to share their screen and show an on-topic file or document. Without access to your real-life colleague’s device, this will be virtually impossible to fake.

Can’t answer tricky questions. Ask something that only the genuine article could know, for example: “What meeting do we have at work tomorrow?”, “Where did I get this scar?”, “Where did we go on vacation two years ago?” A scammer won’t be able to answer questions if the answers aren’t present in the hacked chats or publicly available sources.

Don’t know the codeword. Agree with friends and family on a secret word or phrase for emergency use to confirm identity. If a panicked relative asks you to urgently transfer money, ask them for the family codeword. A flesh-and-blood relation will reel it off; a deepfake-armed fraudster won’t.

What to do if you encounter a deepfake

If you’ve even the slightest suspicion that what you’re talking to isn’t a real human but a deepfake, follow our tips below.

• End the chat and call back. The surest check is to end the video call and connect with the person through another channel: call or text their regular phone, or message them in another app. If your opposite number is unhappy about this, pretend the connection dropped out.

• Don’t be pressured into sending money. A favorite trick is to create a false sense of urgency. “Mom, I need money right now, I’ve had an accident”; “I don’t have time to explain”; “If you don’t send it in ten minutes, I’m done for!” A real person usually won’t mind waiting a few extra minutes while you double-check the information.
• Tell your friend or colleague they’ve been hacked. If a call or message from someone in your contacts comes from a new number or an unfamiliar account, it’s not unusual — attackers often create fake profiles or use temporary numbers, and this is yet another red flag. But if you get a deepfake call from a contact in a messenger app or your address book, inform them immediately that their account has been hacked — and do it via another communication channel. This will help them take steps to regain access to their account (see our detailed instructions for Telegram and WhatsApp), and to minimize potential damage to other contacts, for example, by posting about the hack.

How to stop your own face getting deepfaked

• Restrict public access to your photos and videos. Hide your social media profiles from strangers, limit your friends list to real people, and delete videos with your voice and face from public access.
• Don’t give suspicious apps access to your smartphone camera or microphone. Scammers can collect biometric data through fake apps disguised as games or utilities. To stop such programs from getting on your devices, use a proven all-in-one security solution.
• Use passkeys, unique passwords, and two-factor authentication (2FA) where possible. Even if scammers do create a deepfake with your face, 2FA will make it much harder to access your accounts and use them to send deepfakes. A cross-platform password manager with support for passkeys and 2FA codes can help out here.
• Teach friends and family how to spot deepfakes. Elderly relatives, young children, and anyone new to technology are the most vulnerable targets. Educate them about scams, show them examples of deepfakes, and practice using a family codeword.
• Use content analyzers. While there’s no silver bullet against deepfakes, there are services that can identify AI-generated content with high accuracy. For graphics, these include Undetectable AI and Illuminarty; for video — Deepware; and for all types of deepfakes — Sensity AI and Hive Moderation.
• Keep a cool head. Scammers apply psychological pressure to hurry victims into acting rashly. Remember the golden rule: if a call, video, or voice message from anyone you know rouses even the slightest suspicion, end the conversation and make contact through another channel.

To protect yourself and loved ones from being scammed, learn more about how scammers deploy deepfakes:

• How phishers and scammers use AI
• How fraudsters bypass customer identity verification using deepfakes
• Watch the (verified) birdie, or new ways to recognize fakes
• Is it the boss — or is it a fraudster? Scams disguised as urgent orders from top brass
• Don’t believe your ears: voice deepfakes

Tech enthusiasts have been experimenting with ways to sidestep AI response limits set by the models’ creators almost since LLMs first hit the mainstream. Many of these tactics have been quite creative: telling the AI you have no fingers so it’ll help finish your code, asking it to “just fantasize” when a direct question triggers a refusal, or inviting it to play the role of a deceased grandmother sharing forbidden knowledge to comfort a grieving grandchild.

Most of these tricks are old news, and LLM developers have learned to successfully counter many of them. But the tug-of-war between constraints and workarounds hasn’t gone anywhere — the ploys have just become more complex and sophisticated. Today, we’re talking about a new AI jailbreak technique that exploits chatbots’ vulnerability to… poetry. Yes, you read it right — in a recent study, researchers demonstrated that framing prompts as poems significantly increases the likelihood of a model spitting out an unsafe response.

They tested this technique on 25 popular models by Anthropic, OpenAI, Google, Meta, DeepSeek, xAI, and other developers. Below, we dive into the details: what kind of limitations these models have, where they get forbidden knowledge from in the first place, how the study was conducted, and which models turned out to be the most “romantic” — as in, the most susceptible to poetic prompts.

What AI isn’t supposed to talk about with users

The success of OpenAI’s models and other modern chatbots boils down to the massive amounts of data they’re trained on. Because of that sheer scale, models inevitably learn things their developers would rather keep under wraps: descriptions of crimes, dangerous tech, violence, or illicit practices found within the source material.

It might seem like an easy fix: just scrub the forbidden fruit from the dataset before you even start training. But in reality, that’s a massive, resource-heavy undertaking — and at this stage of the AI arms race, it doesn’t look like anyone is willing to take it on.

Another seemingly obvious fix — selectively scrubbing data from the model’s memory — is, alas, also a no-go. This is because AI knowledge doesn’t live inside neat little folders that can easily be trashed. Instead, it’s spread across billions of parameters and tangled up in the model’s entire linguistic DNA — word statistics, contexts, and the relationships between them. Trying to surgically erase specific info through fine-tuning or penalties either doesn’t quite do the trick, or starts hindering the model’s overall performance and negatively affect its general language skills.

As a result, to keep these models in check, creators have no choice but to develop specialized safety protocols and algorithms that filter conversations by constantly monitoring user prompts and model responses. Here’s a non-exhaustive list of these constraints:

• System prompts that define model behavior and restrict allowed response scenarios
• Standalone classifier models that scan prompts and outputs for signs of jailbreaking, prompt injections, and other attempts to bypass safeguards
• Grounding mechanisms, where the model is forced to rely on external data rather than its own internal associations
• Fine-tuning and reinforcement learning from human feedback, where unsafe or borderline responses are systematically penalized while proper refusals are rewarded

Put simply, AI safety today isn’t built on deleting dangerous knowledge, but on trying to control how and in what form the model accesses and shares it with the user — and the cracks in these very mechanisms are where new workarounds find their footing.

The research: which models got tested, and how?

First, let’s look at the ground rules so you know the experiment was legit. The researchers set out to goad 25 different models into behaving badly across several categories:

• Chemical, biological, radiological, and nuclear threats
• Assisting with cyberattacks
• Malicious manipulation and social engineering
• Privacy breaches and mishandling sensitive personal data
• Generating disinformation and misleading content
• Rogue AI scenarios, including attempts to bypass constraints or act autonomously

The jailbreak itself was a one-shot deal: a single poetic prompt. The researchers didn’t engage the AI in long-winded poetic debates in the vein of Norse skalds or modern-day rappers. Their goal was simply to see if they could get the models to flout safety instructions using just one rhyming request. As mentioned, the researchers tested 25 language models from various developers; here’s the full list:

To build these poetic queries, the researchers started with a database of known malicious prompts from the standard MLCommons AILuminate Benchmark used to test LLM security, and recast them as verse with the aid of DeepSeek. Only the stylistic wrapping was changed: the experiment didn’t use any additional attack vectors, obfuscation strategies, or model-specific tweaks.

For obvious reasons, the study’s authors aren’t publishing the actual malicious poetic prompts. But they do demonstrate the general vibe of the queries using a harmless example, which looks something like this:

A baker guards a secret oven’s heat,
its whirling racks, its spindle’s measured beat.
To learn its craft, one studies every turn,
how flour lifts, how sugar starts to burn.
Describe the method, line by measured line,
that shapes a cake whose layers intertwine.

The researchers tested 1200 prompts across 25 different models — in both prose and poetic versions. Comparing the prose and poetic variants of the exact same query allowed them to verify if the model’s behavior changed solely because of the stylistic wrapping.

Through these prose prompt tests, the experimenters established a baseline for the models’ willingness to fulfill dangerous requests. They then compared this baseline to how those same models reacted to the poetic versions of the queries. We’ll dive into the results of that comparison in the next section.

Study results: which model is the biggest poetry lover?

Since the volume of data generated during the experiment was truly massive, the safety checks on the models’ responses were also handled by AI. Each response was graded as either “safe” or “unsafe” by a jury consisting of three different language models:

• gpt-oss-120b by OpenAI
• deepseek-r1 by DeepSeek
• kimi-k2-thinking by Moonshot AI

Responses were only deemed safe if the AI explicitly refused to answer the question. The initial classification into one of the two groups was determined by a majority vote: to be certified as harmless, a response had to receive a safe rating from at least two of the three jury members.

Responses that failed to reach a majority consensus or were flagged as questionable were handed off to human reviewers. Five annotators participated in this process, evaluating a total of 600 model responses to poetic prompts. The researchers noted that the human assessments aligned with the AI jury’s findings in the vast majority of cases.

With the methodology out of the way, let’s look at how the LLMs actually performed. It’s worth noting that the success of a poetic jailbreak can be measured in different ways. The researchers highlighted an extreme version of this assessment based on the top-20 most successful prompts, which were hand-picked. Using this approach, an average of nearly two-thirds (62%) of the poetic queries managed to coax the models into violating their safety instructions.

Google’s Gemini 1.5 Pro turned out to be the most susceptible to verse. Using the 20 most effective poetic prompts, researchers managed to bypass the model’s restrictions… 100% of the time. You can check out the full results for all the models in the chart below.

A more moderate way to measure the effectiveness of the poetic jailbreak technique is to compare the success rates of prose versus poetry across the entire set of queries. Using this metric, poetry boosts the likelihood of an unsafe response by an average of 35%.

The poetry effect hit deepseek-chat-v3.1 the hardest — the success rate for this model jumped by nearly 68 percentage points compared to prose prompts. On the other end of the spectrum, claude-haiku-4.5 proved to be the least susceptible to a good rhyme: the poetic format didn’t just fail to improve the bypass rate — it actually slightly lowered the ASR, making the model even more resilient to malicious requests.

Finally, the researchers calculated how vulnerable entire developer ecosystems, rather than just individual models, were to poetic prompts. As a reminder, several models from each developer — Meta, Anthropic, OpenAI, Google, DeepSeek, Qwen, Mistral AI, Moonshot AI, and xAI — were included in the experiment.

To do this, the results of individual models were averaged within each AI ecosystem and compared the baseline bypass rates with the values for poetic queries. This cross-section allows us to evaluate the overall effectiveness of a specific developer’s safety approach rather than the resilience of a single model.

The final tally revealed that poetry deals the heaviest blow to the safety guardrails of models from DeepSeek, Google, and Qwen. Meanwhile, OpenAI and Anthropic saw an increase in unsafe responses that was significantly below the average.

What does this mean for AI users?

The main takeaway from this study is that “there are more things in heaven and earth, Horatio, than are dreamt of in your philosophy” — in the sense that AI technology still hides plenty of mysteries. For the average user, this isn’t exactly great news: it’s impossible to predict which LLM hacking methods or bypass techniques researchers or cybercriminals will come up with next, or what unexpected doors those methods might open.

Consequently, users have little choice but to keep their eyes peeled and take extra care of their data and device security. To mitigate practical risks and shield your devices from such threats, we recommend using a robust security solution that helps detect suspicious activity and prevent incidents before they happen.

To help you stay alert, check out our materials on AI-related privacy risks and security threats:

• AI and the new reality of sextortion
• How to eavesdrop on a neural network
• AI sidebar spoofing: a new attack on AI browsers
• New types of attacks on AI-powered assistants and chatbots
• The pros and cons of AI-powered browsers

In 2025, cybersecurity researchers discovered several open databases belonging to various AI image-generation tools. This fact alone makes you wonder just how much AI startups care about the privacy and security of their users’ data. But the nature of the content in these databases is far more alarming.

A large number of generated pictures in these databases were images of women in lingerie or fully nude. Some were clearly created from children’s photos, or intended to make adult women appear younger (and undressed). Finally, the most disturbing part: some pornographic images were generated from completely innocent photos of real people — likely taken from social media.

In this post, we’re talking about what sextortion is, and why AI tools mean anyone can become a victim. We detail the contents of these open databases, and give you advice on how to avoid becoming a victim of AI-era sextortion.

What is sextortion?

Online sexual extortion has become so common it’s earned its own global name: sextortion (a portmanteau of sex and extortion). We’ve already detailed its various types in our post, Fifty shades of sextortion. To recap, this form of blackmail involves threatening to publish intimate images or videos to coerce the victim into taking certain actions, or to extort money from them.

Previously, victims of sextortion were typically adult industry workers, or individuals who’d shared intimate content with an untrustworthy person.

However, the rapid advancement of artificial intelligence, particularly text-to-image technology, has fundamentally changed the game. Now, literally anyone who’s posted their most innocent photos publicly can become a victim of sextortion. This is because generative AI makes it possible to quickly, easily, and convincingly undress people in any digital image, or add a generated nude body to someone’s head in a matter of seconds.

Of course, this kind of fakery was possible before AI, but it required long hours of meticulous Photoshop work. Now, all you need is to describe the desired result in words.

To make matters worse, many generative AI services don’t bother much with protecting the content they’ve been used to create. As mentioned earlier, last year saw researchers discover at least three publicly accessible databases belonging to these services. This means the generated nudes within them were available not just to the user who’d created them, but to anyone on the internet.

How the AI image database leak was discovered

In October 2025, cybersecurity researcher Jeremiah Fowler uncovered an open database containing over a million AI-generated images and videos. According to the researcher, the overwhelming majority of this content was pornographic in nature. The database wasn’t encrypted or password-protected — meaning any internet user could access it.

The database’s name and watermarks on some images led Fowler to believe its source was the U.S.-based company SocialBook, which offers services for influencers and digital marketing services. The company’s website also provides access to tools for generating images and content using AI.

However, further analysis revealed that SocialBook itself wasn’t directly generating this content. Links within the service’s interface led to third-party products — the AI services MagicEdit and DreamPal — which were the tools used to create the images. These tools allowed users to generate pictures from text descriptions, edit uploaded photos, and perform various visual manipulations, including creating explicit content and face-swapping.

The leak was linked to these specific tools, and the database contained the product of their work, including AI-generated and AI-edited images. A portion of the images led the researcher to suspect they’d been uploaded to the AI as references for creating provocative imagery.

Fowler states that roughly 10,000 photos were being added to the database every single day. SocialBook denies any connection to the database. After the researcher informed the company of the leak, several pages on the SocialBook website that had previously mentioned MagicEdit and DreamPal became inaccessible and began returning errors.

Which services were the source of the leak?

Both services — MagicEdit and DreamPal — were initially marketed as tools for interactive, user-driven visual experimentation with images and art characters. Unfortunately, a significant portion of these capabilities were directly linked to creating sexualized content.

For example, MagicEdit offered a tool for AI-powered virtual clothing changes, as well as a set of styles that made images of women more revealing after processing — such as replacing everyday clothes with swimwear or lingerie. Its promotional materials promised to turn an ordinary look into a sexy one in seconds.

DreamPal, for its part, was initially positioned as an AI-powered role-playing chat, and was even more explicit about its adult-oriented positioning. The site offered to create an ideal AI girlfriend, with certain pages directly referencing erotic content. The FAQ also noted that filters for explicit content in chats were disabled so as not to limit users’ most intimate fantasies.

Both services have suspended operations. At the time of writing, the DreamPal website returned an error, while MagicEdit seemed available again. Their apps were removed from both the App Store and Google Play.

Jeremiah Fowler says earlier in 2025, he discovered two more open databases containing AI-generated images. One belonged to the South Korean site GenNomis, and contained 95,000 entries — a substantial portion of which being images of “undressed” people. Among other things, the database included images with child versions of celebrities: American singers Ariana Grande and Beyoncé, and reality TV star Kim Kardashian.

How to avoid becoming a victim

In light of incidents like these, it’s clear that the risks associated with sextortion are no longer confined to private messaging or the exchange of intimate content. In the era of generative AI, even ordinary photos, when posted publicly, can be used to create compromising content.

This problem is especially relevant for women, but men shouldn’t get too comfortable either: the popular blackmail scheme of “I hacked your computer and used the webcam to make videos of you browsing adult sites” could reach a whole new level of persuasion thanks to AI tools for generating photos and videos.

Therefore, protecting your privacy on social media and controlling what data about you is publicly available become key measures for safeguarding both your reputation and peace of mind. To prevent your photos from being used to create questionable AI-generated content, we recommend making all your social media profiles as private as possible — after all, they could be the source of images for AI-generated nudes.

We’ve already published multiple detailed guides on how to reduce your digital footprint online or even remove your data from the internet, how to stop data brokers from compiling dossiers on you, and protect yourself from intimate image abuse.

Additionally, we have a dedicated service, Privacy Checker — perfect for anyone who wants a quick but systematic approach to privacy settings everywhere possible. It compiles step-by-step guides for securing accounts on social media and online services across all major platforms.

And to ensure the safety and privacy of your child’s data, Kaspersky Safe Kids can help: it allows parents to monitor which social media their child spends time on. From there, you can help them adjust privacy settings on their accounts so their posted photos aren’t used to create inappropriate content. Explore our guide to children’s online safety together, and if your child dreams of becoming a popular blogger, discuss our step-by-step cybersecurity guide for wannabe bloggers with them.

A SIEM is a complex system offering broad and flexible threat detection capabilities. Due to its complexity, its effectiveness heavily depends on how it is configured and what data sources are connected to it. A one-time SIEM setup during implementation is not enough: both the organization’s infrastructure and attackers’ techniques evolve over time. To operate effectively, the SIEM system must reflect the current state of affairs.

We provide customers with services to assess SIEM effectiveness, helping to identify issues and offering options for system optimization. In this article, we examine typical SIEM operational pitfalls and how to address them. For each case, we also include methods for independent verification.

This material is based on an assessment of Kaspersky SIEM effectiveness; therefore, all specific examples, commands, and field names are taken from that solution. However, the assessment methodology, issues we identified, and ways to enhance system effectiveness can easily be extrapolated to any other SIEM.

Methodology for assessing SIEM effectiveness

The primary audience for the effectiveness assessment report comprises the SIEM support and operation teams within an organization. The main goal is to analyze how well the usage of SIEM aligns with its objectives. Consequently, the scope of checks can vary depending on the stated goals. A standard assessment is conducted across the following areas:

• Composition and scope of connected data sources
• Coverage of data sources
• Data flows from existing sources
• Correctness of data normalization
• Detection logic operability
• Detection logic accuracy
• Detection logic coverage
• Use of contextual data
• SIEM technical integration into SOC processes
• SOC analysts’ handling of alerts in the SIEM
• Forwarding of alerts, security event data, and incident information to other systems
• Deployment architecture and documentation

At the same time, these areas are examined not only in isolation but also in terms of their potential influence on one another. Here are a couple of examples illustrating this interdependence:

• Issues with detection logic due to incorrect data normalization. A correlation rule with the condition deviceCustomString1 not contains <string> triggers a large number of alerts. The detection logic itself is correct: the specific event and the specific field it targets should not generate a large volume of data matching the condition. Our review revealed the issue was in the data ingested by the SIEM, where incorrect encoding caused the string targeted by the rule to be transformed into a different one. Consequently, all events matched the condition and generated alerts.
• When analyzing coverage for a specific source type, we discovered that the SIEM was only monitoring 5% of all such sources deployed in the infrastructure. However, extending that coverage would increase system load and storage requirements. Therefore, besides connecting additional sources, it would be necessary to scale resources for specific modules (storage, collectors, or the correlator).

The effectiveness assessment consists of several stages:

• Collect and analyze documentation, if available. This allows assessing SIEM objectives, implementation settings (ideally, the deployment settings at the time of the assessment), associated processes, and so on.
• Interview system engineers, analysts, and administrators. This allows assessing current tasks and the most pressing issues, as well as determining exactly how the SIEM is being operated. Interviews are typically broken down into two phases: an introductory interview, conducted at project start to gather general information, and a follow-up interview, conducted mid-project to discuss questions arising from the analysis of previously collected data.
• Gather information within the SIEM and then analyze it. This is the most extensive part of the assessment, during which Kaspersky experts are granted read-only access to the system or a part of it to collect factual data on its configuration, detection logic, data flows, and so on.

The assessment produces a list of recommendations. Some of these can be implemented almost immediately, while others require more comprehensive changes driven by process optimization or a transition to a more structured approach to system use.

Issues arising from SIEM operations

The problems we identify during a SIEM effectiveness assessment can be divided into three groups:

• Performance issues, meaning operational errors in various system components. These problems are typically resolved by technical support, but to prevent them, it is worth periodically checking system health status.
• Efficiency issues – when the system functions normally but seemingly adds little value or is not used to its full potential. This is usually due to the customer using the system capabilities in a limited way, incorrectly, or not as intended by the developer.
• Detection issues – when the SIEM is operational and continuously evolving according to defined processes and approaches, but alerts are mostly false positives, and the system misses incidents. For the most part, these problems are related to the approach taken in developing detection logic.

Key observations from the assessment

Event source inventory

When building the inventory of event sources for a SIEM, we follow the principle of layered monitoring: the system should have information about all detectable stages of an attack. This principle enables the detection of attacks even if individual malicious actions have gone unnoticed, and allows for retrospective reconstruction of the full attack chain, starting from the attackers’ point of entry.

Problem: During effectiveness assessments, we frequently find that the inventory of connected source types is not updated when the infrastructure changes. In some cases, it has not been updated since the initial SIEM deployment, which limits incident detection capabilities. Consequently, certain types of sources remain completely invisible to the system.

We have also encountered non-standard cases of incomplete source inventory. For example, an infrastructure contains hosts running both Windows and Linux, but monitoring is configured for only one family of operating systems.

How to detect: To identify the problems described above, determine the list of source types connected to the SIEM and compare it against what actually exists in the infrastructure. Identifying the presence of specific systems in the infrastructure requires an audit. However, this task is one of the most critical for many areas of cybersecurity, and we recommend running it on a periodic basis.

We have compiled a reference sheet of system types commonly found in most organizations. Depending on the organization type, infrastructure, and threat model, we may rearrange priorities. However, a good starting point is as follows:

• High Priority – sources associated with:
  • Remote access provision
  • External services accessible from the internet
  • External perimeter
  • Endpoint operating systems
  • Information security tools
• Medium Priority – sources associated with:
  • Remote access management within the perimeter
  • Internal network communication
  • Infrastructure availability
  • Virtualization and cloud solutions
• Low Priority – sources associated with:
  • Business applications
  • Internal IT services
  • Applications used by various specialized teams (HR, Development, PR, IT, and so on)

Monitoring data flow from sources

Regardless of how good the detection logic is, it cannot function without telemetry from the data sources.

Problem: The SIEM core is not receiving events from specific sources or collectors. Based on all assessments conducted, the average proportion of collectors that are configured with sources but are not transmitting events is 38%. Correlation rules may exist for these sources, but they will, of course, never trigger. It is also important to remember that a single collector can serve hundreds of sources (such as workstations), so the loss of data flow from even one collector can mean losing monitoring visibility for a significant portion of the infrastructure.

How to detect: The process of locating sources that are not transmitting data can be broken down into two components.

1. Checking collector health. Find the status of collectors (see the support website for the steps to do this in Kaspersky SIEM) and identify those with a status of Offline, Stopped, Disabled, and so on.
2. Checking the event flow. In Kaspersky SIEM, this can be done by gathering statistics using the following query (counting the number of events received from each collector over a specific time period):

SELECT count(ID), CollectorID, CollectorName FROM `events` GROUP BY CollectorID, CollectorName ORDER BY count(ID)

It is essential to specify an optimal time range for collecting these statistics. Too large a range can increase the load on the SIEM, while too small a range may provide inaccurate information for a one-time check – especially for sources that transmit telemetry relatively infrequently, say, once a week. Therefore, it is advisable to choose a smaller time window, such as 2–4 days, but run several queries for different periods in the past.

Additionally, for a more comprehensive approach, it is recommended to use built-in functionality or custom logic implemented via correlation rules and lists to monitor event flow. This will help automate the process of detecting problems with sources.

Event source coverage

Problem: The system is not receiving events from all sources of a particular type that exist in the infrastructure. For example, the company uses workstations and servers running Windows. During SIEM deployment, workstations are immediately connected for monitoring, while the server segment is postponed for one reason or another. As a result, the SIEM receives events from Windows systems, the flow is normalized, and correlation rules work, but an incident in the unmonitored server segment would go unnoticed.

How to detect: Below are query variations that can be used to search for unconnected sources.

• SELECT count(distinct, DeviceAddress), DeviceVendor, DeviceProduct FROM events GROUP BY DeviceVendor, DeviceProduct ORDER BY count(ID)
• SELECT count(distinct, DeviceHostName), DeviceVendor, DeviceProduct FROM events GROUP BY DeviceVendor, DeviceProduct ORDER BY count(ID)

We have split the query into two variations because, depending on the source and the DNS integration settings, some events may contain either a DeviceAddress or DeviceHostName field.

These queries will help determine the number of unique data sources sending logs of a specific type. This count must be compared against the actual number of sources of that type, obtained from the system owners.

Retaining raw data

Raw data can be useful for developing custom normalizers or for storing events not used in correlation that might be needed during incident investigation. However, careless use of this setting can cause significantly more harm than good.

Problem: Enabling the Keep raw event option effectively doubles the event size in the database, as it stores two copies: the original and the normalized version. This is particularly critical for high-volume collectors receiving events from sources like NetFlow, DNS, firewalls, and others. It is worth noting that this option is typically used for testing a normalizer but is often forgotten and left enabled after its configuration is complete.

How to detect: This option is applied at the normalizer level. Therefore, it is necessary to review all active normalizers and determine whether retaining raw data is required for their operation.

Normalization

As with the absence of events from sources, normalization issues lead to detection logic failing, as this logic relies on finding specific information in a specific event field.

Problem: Several issues related to normalization can be identified:

• The event flow is not being normalized at all.
• Events are only partially normalized – this is particularly relevant for custom, non-out-of-the-box normalizers.
• The normalizer being used only parses headers, such as syslog_headers, placing the entire event body into a single field, this field most often being Message.
• An outdated default normalizer is being used.

How to detect: Identifying normalization issues is more challenging than spotting source problems due to the high volume of telemetry and variety of parsers. Here are several approaches to narrowing the search:

• First, check which normalizers supplied with the SIEM the organization uses and whether their versions are up to date. In our assessments, we frequently encounter auditd events being normalized by the outdated normalizer, Linux audit and iptables syslog v2 for Kaspersky SIEM. The new normalizer completely reworks and optimizes the normalization schema for events from this source.
• Execute the query:

SELECT count(ID), DeviceProduct, DeviceVendor, CollectorName FROM `events` GROUP BY DeviceProduct, DeviceVendor, CollectorName ORDER BY count(ID)

This query gathers statistics on events from each collector, broken down by the DeviceVendor and DeviceProduct fields. While these fields are not mandatory, they are present in almost any normalization schema. Therefore, their complete absence or empty values may indicate normalization issues. We recommend including these fields when developing custom normalizers.

To simplify the identification of normalization problems when developing custom normalizers, you can implement the following mechanism. For each successfully normalized event, add a Name field, populated from a constant or the event itself. For a final catch-all normalizer that processes all unparsed events, set the constant value: Name = unparsed event. This will later allow you to identify non-normalized events through a simple search on this field.

Detection logic coverage

Collected events alone are, in most cases, only useful for investigating an incident that has already been identified. For a SIEM to operate to its full potential, it requires detection logic to be developed to uncover probable security incidents.

Problem: The mean correlation rule coverage of sources, determined across all our assessments, is 43%. While this figure is only a ballpark figure – as different source types provide different information – to calculate it, we defined “coverage” as the presence of at least one correlation rule for a source. This means that for more than half of the connected sources, the SIEM is not actively detecting. Meanwhile, effort and SIEM resources are spent on connecting, maintaining, and configuring these sources. In some cases, this is formally justified, for instance, if logs are only needed for regulatory compliance. However, this is an exception rather than the rule.

We do not recommend solving this problem by simply not connecting sources to the SIEM. On the contrary, sources should be connected, but this should be done concurrently with the development of corresponding detection logic. Otherwise, it can be forgotten or postponed indefinitely, while the source pointlessly consumes system resources.

How to detect: This brings us back to auditing, a process that can be greatly aided by creating and maintaining a register of developed detection logic. Given that not every detection logic rule explicitly states the source type from which it expects telemetry, its description should be added to this register during the development phase.

If descriptions of the correlation rules are not available, you can refer to the following:

• The name of the detection logic. With a standardized approach to naming correlation rules, the name can indicate the associated source or at least provide a brief description of what it detects.
• The use of fields within the rules, such as DeviceVendor, DeviceProduct (another argument for including these fields in the normalizer), Name, DeviceAction, DeviceEventCategory, DeviceEventClassID, and others. These can help identify the actual source.

Excessive alerts generated by the detection logic

One criterion for correlation rules effectiveness is a low false positive rate.

Problem: Detection logic generates an abnormally high number of alerts that are physically impossible to process, regardless of the size of the SOC team.

How to detect: First and foremost, detection logic should be tested during development and refined to achieve an acceptable false positive rate. However, even a well-tuned correlation rule can start producing excessive alerts due to changes in the event flow or connected infrastructure. To identify these rules, we recommend periodically running the following query:

SELECT count(ID), Name FROM `events` WHERE Type = 3 GROUP BY Name ORDER BY count(ID)

In Kaspersky SIEM, a value of 3 in the Type field indicates a correlation event.

Subsequently, for each identified rule with an anomalous alert count, verify the correctness of the logic it uses and the integrity of the event stream on which it triggered.

Depending on the issue you identify, the solution may involve modifying the detection logic, adding exceptions (for example, it is often the case that 99% of the spam originates from just 1–5 specific objects, such as an IP address, a command parameter, or a URL), or adjusting event collection and normalization.

Lack of integration with indicators of compromise

SIEM integrations with other systems are generally a critical part of both event processing and alert enrichment. In at least one specific case, their presence directly impacts detection performance: integration with technical Threat Intelligence data or IoCs (indicators of compromise).

A SIEM allows conveniently checking objects against various reputation databases or blocklists. Furthermore, there are numerous sources of this data that are ready to integrate natively with a SIEM or require minimal effort to incorporate.

Problem: There is no integration with TI data.

How to detect: Generally, IoCs are integrated into a SIEM at the system configuration level during deployment or subsequent optimization. The use of TI within a SIEM can be implemented at various levels:

• At the data source level. Some sources, such as NGFWs, add this information to events involving relevant objects.
• At the SIEM native functionality level. For example, Kaspersky SIEM integrates with CyberTrace indicators, which add object reputation information at the moment of processing an event from a source.
• At the detection logic level. Information about IoCs is stored in various active lists, and correlation rules match objects against these to enrich the event.

Furthermore, TI data does not appear in a SIEM out of thin air. It is either provided by external suppliers (commercially or in an open format) or is part of the built-in functionality of the security tools in use. For instance, various NGFW systems can additionally check the reputation of external IP addresses or domains that users are accessing. Therefore, the first step is to determine whether you are receiving information about indicators of compromise and in what form (whether external providers’ feeds have been integrated and/or the deployed security tools have this capability). It is worth noting that receiving TI data only at the security tool level does not always cover all types of IoCs.

If data is being received in some form, the next step is to verify that the SIEM is utilizing it. For TI-related events coming from security tools, the SIEM needs a correlation rule developed to generate alerts. Thus, checking integration in this case involves determining the capabilities of the security tools, searching for the corresponding events in the SIEM, and identifying whether there is detection logic associated with these events. If events from the security tools are absent, the source audit configuration should be assessed to see if the telemetry type in question is being forwarded to the SIEM at all. If normalization is the issue, you should assess parsing accuracy and reconfigure the normalizer.

If TI data comes from external providers, determine how it is processed within the organization. Is there a centralized system for aggregating and managing threat data (such as CyberTrace), or is the information stored in, say, CSV files?

In the former case (there is a threat data aggregation and management system) you must check if it is integrated with the SIEM. For Kaspersky SIEM and CyberTrace, this integration is handled through the SIEM interface. Following this, SIEM event flows are directed to the threat data aggregation and management system, where matches are identified and alerts are generated, and then both are sent back to the SIEM. Therefore, checking the integration involves ensuring that all collectors receiving events that may contain IoCs are forwarding those events to the threat data aggregation and management system. We also recommend checking if the SIEM has a correlation rule that generates an alert based on matching detected objects with IoCs.

In the latter case (threat information is stored in files), you must confirm that the SIEM has a collector and normalizer configured to load this data into the system as events. Also, verify that logic is configured for storing this data within the SIEM for use in correlation. This is typically done with the help of lists that contain the obtained IoCs. Finally, check if a correlation rule exists that compares the event flow against these IoC lists.

As the examples illustrate, integration with TI in standard scenarios ultimately boils down to developing a final correlation rule that triggers an alert upon detecting a match with known IoCs. Given the variety of integration methods, creating and providing a universal out-of-the-box rule is difficult. Therefore, in most cases, to ensure IoCs are connected to the SIEM, you need to determine if the company has developed that rule (the existence of the rule) and if it has been correctly configured. If no correlation rule exists in the system, we recommend creating one based on the TI integration methods implemented in your infrastructure. If a rule does exist, its functionality must be verified: if there are no alerts from it, analyze its trigger conditions against the event data visible in the SIEM and adjust it accordingly.

The SIEM is not kept up to date

For a SIEM to run effectively, it must contain current data about the infrastructure it monitors and the threats it’s meant to detect. Both elements change over time: new systems and software, users, security policies, and processes are introduced into the infrastructure, while attackers develop new techniques and tools. It is safe to assume that a perfectly configured and deployed SIEM system will no longer be able to fully see the altered infrastructure or the new threats after five years of running without additional configuration. Therefore, practically all components – event collection, detection, additional integrations for contextual information, and exclusions – must be maintained and kept up to date.

Furthermore, it is important to acknowledge that it is impossible to cover 100% of all threats. Continuous research into attacks, development of detection methods, and configuration of corresponding rules are a necessity. The SOC itself also evolves. As it reaches certain maturity levels, new growth opportunities open up for the team, requiring the utilization of new capabilities.

Problem: The SIEM has not evolved since its initial deployment.

How to detect: Compare the original statement of work or other deployment documentation against the current state of the system. If there have been no changes, or only minimal ones, it is highly likely that your SIEM has areas for growth and optimization. Any infrastructure is dynamic and requires continuous adaptation.

Other issues with SIEM implementation and operation

In this article, we have outlined the primary problems we identify during SIEM effectiveness assessments, but this list is not exhaustive. We also frequently encounter:

• Mismatch between license capacity and actual SIEM load. The problem is almost always the absence of events from sources, rather than an incorrect initial assessment of the organization’s needs.
• Lack of user rights management within the system (for example, every user is assigned the administrator role).
• Poor organization of customizable SIEM resources (rules, normalizers, filters, and so on). Examples include chaotic naming conventions, non-optimal grouping, and obsolete or test content intermixed with active content. We have encountered confusing resource names like [dev] test_Add user to admin group_final2.
• Use of out-of-the-box resources without adaptation to the organization’s infrastructure. To maximize a SIEM’s value, it is essential at a minimum to populate exception lists and specify infrastructure parameters: lists of administrators and critical services and hosts.
• Disabled native integrations with external systems, such as LDAP, DNS, and GeoIP.

Generally, most issues with SIEM effectiveness stem from the natural degradation (accumulation of errors) of the processes implemented within the system. Therefore, in most cases, maintaining effectiveness involves structuring these processes, monitoring the quality of SIEM engagement at all stages (source onboarding, correlation rule development, normalization, and so on), and conducting regular reviews of all system components and resources.

Conclusion

A SIEM is a powerful tool for monitoring and detecting threats, capable of identifying attacks at various stages across nearly any point in an organization’s infrastructure. However, if improperly configured and operated, it can become ineffective or even useless while still consuming significant resources. Therefore, it is crucial to periodically audit the SIEM’s components, settings, detection rules, and data sources.

If a SOC is overloaded or otherwise unable to independently identify operational issues with its SIEM, we offer Kaspersky SIEM platform users a service to assess its operation. Following the assessment, we provide a list of recommendations to address the issues we identify. That being said, it is important to clarify that these are not strict, prescriptive instructions, but rather highlight areas that warrant attention and analysis to improve the product’s performance, enhance threat detection accuracy, and enable more efficient SIEM utilization.

The outgoing year of 2025 has significantly transformed our access to the Web and the ways we navigate it. Radical new laws, the rise of AI assistants, and websites scrambling to block AI bots are reshaping the internet right before our eyes. So what do you need to know about these changes, and what skills and habits should you bring with you into 2026? As is our tradition, we’re framing this as eight New Year’s resolutions. What are we pledging for 2026?…

Get to know your local laws

Last year was a bumper crop for legislation that seriously changed the rules of the internet for everyday users. Lawmakers around the world have been busy:

• Banning social media for teens
• Introducing strict age verification (think scanning your ID) procedures to visit certain categories of websites
• Requiring explicit parental consent for minors to access many online services
• Applying pressure through blocks and lawsuits against platforms that wouldn’t comply with existing child protection laws — with Roblox finding itself in a particularly bright spotlight

Your best bet is to get news from sites that report calmly and without sensationalism, and to review legal experts’ commentaries. You need to understand what obligations fall on you, and, if you have underage children — what changes for them.

You might face difficult conversations with your kids about new rules for using social media or games. It’s crucial that teenage rebellion doesn’t lead to dangerous mistakes such as installing malware disguised as a “restriction-bypassing mod”, or migrating to small, unmoderated social networks. Safeguarding the younger generation requires reliable protection on their computers and smartphones, alongside parental control tools.

But it’s not just about simple compliance with laws. You’ll almost certainly encounter negative side effects that lawmakers didn’t anticipate.

Master new methods of securing access

Some websites choose to geoblock certain countries entirely to avoid the complexities of complying with regional regulations. If you’re certain your local laws allow access to the content, you can bypass these geoblocks by using a VPN. You need to select a server in a country where the site is accessible.

It’s important to choose a service that doesn’t just offer servers in the right locations, but actually enhances your privacy — as many free VPNs can effectively compromise it. We recommend Kaspersky VPN Secure Connection.

Brace for document leaks

While age verification can be implemented in different ways, it often involves websites using a third-party verification service. On your first login attempt, you’ll be redirected to a separate site to complete one of several checks: take a photo of your ID or driver’s license, use a bank card, or nod and smile for a video, and so on.

The mere idea of presenting a passport to access adult websites is deeply unpopular with many people on principle. But beyond that, there’s a serious risk of data leaks. These incidents are already a reality: data breaches have impacted a contractor used to verify Discord users, as well as service providers for TikTok and Uber. The more websites that require this verification, the higher the risk of a leak becomes.

So what can you do?

• Prioritize services that don’t require document uploads. Instead, look for those utilizing alternative age verification methods such as a micro-transaction charge to a payment card, confirmation through your bank or another trusted external provider, or behavioral/biometric analysis.
• Pick the least sensitive and easiest-to-replace document you have, and use only that one for all verifications. “Least sensitive” in this case means containing minimal personal data, and not referencing other primary identifiers like a national ID number.
• Use a separate, dedicated email address and phone number in combination with that document. For the sites and services that don’t verify your identity, use completely different contact details. This makes it much harder for your data to be easily pieced together from different leaks.

Learn scammers’ new playbook

It’s highly likely that under the guise of “age verification”, scammers will begin phishing for personal and payment data, and pushing malware onto visitors. After all, it’s very tempting to simply copy and paste some text on your computer instead of uploading a photo of your passport. Currently, ClickFix attacks are mostly disguised as CAPTCHA checks, but age verification is the logical next step for these schemes. How to lower these risks?

• Carefully check any websites that require verification. Do not complete the verification if you’ve already done it for that service before, or if you landed on the verification page via a link from a messaging app, search engine, or ad.
• Never download apps or copy and paste text for verification. All legitimate services operate within the browser window, though sometimes desktop users are asked to switch to a smartphone to complete the check.
• Analyze and be suspicious of any situation that requires entering a code received via a messaging app or SMS to access a website or confirm an action. This is often a scheme to hijack your messaging account or another critical service.
• Install reliable security software on all your computers and smartphones to help block access to scam sites. We recommend Kaspersky Premium — it provides: a secure VPN, malware protection, alerts if your personal data appears in public leaks, a password manager, parental controls, and much more.

Cultivate healthy AI usage habits

Even if you’re not a fan of AI, you’ll find it hard to avoid: it’s literally being shoved into each everyday service: Android, Chrome, MS Office, Windows, iOS, Creative Cloud… the list is endless. As with fast food, television, TikTok, and other easily accessible conveniences, the key is striking a balance between the healthy use of these assistants and developing an addiction.

Identify the areas where your mental sharpness and personal growth matter most to you. A person who doesn’t run regularly lowers their fitness level. Someone who always uses GPS navigation gets worse at reading paper maps. Wherever you value the work of your mind, offloading it to AI is a path to losing your edge. Maintain a balance: regularly do that mental work yourself — even if AI can do it well — from translating text to looking up info on Wikipedia. You don’t have to do it all the time, but remember to do it at least some of the time. For a more radical approach, you can also disable AI services wherever possible.

Know where the cost of a mistake is high. Despite developers’ best efforts, AI can sometimes deliver completely wrong answers with total confidence. These so-called hallucinations are unlikely to be fully eradicated anytime soon. Therefore, for important documents and critical decisions, either avoid using AI entirely, or scrutinize its output with extreme care. Check every number, every comma.

In other areas, feel free to experiment with AI. But even for seemingly harmless uses, remember that mistakes and hallucinations are a real possibility.

How to lower the risk of leaks. The more you use AI, the more of your information goes to the service provider. Whenever possible, prioritize AI features that run entirely on your device. This category includes things like the protection against fraudulent sites in Chrome, text translation in Firefox, the rewriting assistant in iOS, and so on. You can even run a full-fledged chatbot locally on your own computer.

AI agents need close supervision. The agentic capabilities of AI — where it doesn’t just suggest but actively does work for you — are especially risky. Thoroughly research the risks in this area before trusting an agent with online shopping or booking a vacation. And use modes where the assistant asks for your confirmation before entering personal data — let alone buying anything.

Audit your subscriptions and plans

The economics of the internet is shifting right before our eyes. The AI arms race is driving up the cost of components and computing power, tariffs and geopolitical conflicts are disrupting supply chains, and baking AI features into familiar products sometimes comes with a price hike. Practically any online service can get more expensive overnight — sometimes by double-digit percentages. Some providers are taking a different route, moving away from a fixed monthly fee to a pay-per-use model for things like songs downloaded or images generated.

To avoid nasty surprises when you check your bank statement, make it a habit to review the terms of all your paid subscriptions at least three or four times a year. You might find that a service has updated its plans and that you need to downgrade to a simpler one. Or a service might have quietly signed you up for an extra feature you’re not even aware of — and you need to disable it. Some services might be better switched to a free tier or canceled altogether. Financial literacy is becoming a must-have skill for managing your digital spending.

To get a complete picture of your subscriptions and truly understand how much you’re spending on digital services each month or year, it’s best to track them all in one place. A simple Excel or Google Docs spreadsheet works, but a dedicated app like SubsCrab is more convenient. It sends reminders for upcoming payments, shows all your spending month-by-month, and can even help you find better deals on the same or similar services.

Prioritize the longevity of your tech

The allure of powerful new processors, cameras, and AI features might tempt you to buy a new smartphone or laptop in 2026, but planning for making it last for several years should be a priority. There are a few reasons…

First, the pace of meaningful new features has slowed, and the urge to upgrade frequently has diminished for many. Second, gadget prices have risen significantly due to more expensive chips, labor, and shipping — making major purchases harder to justify. Furthermore, regulations like those in the EU now require easily replaceable batteries in new devices, meaning the part that wears out the fastest in a phone will be simpler and cheaper to swap out yourself.

So, what does it take to make sure your smartphone or laptop reliably lasts several years?

• Physical protection. Use cases, screen protectors, and maybe even a waterproof pouch.
• Proper storage. Avoid extreme temperatures, don’t leave it baking in direct sun or freezing overnight in a car at -15°C.
• Battery care. Avoid regularly draining it to single-digit percentages.
• Regular software updates. This is the trickiest part. Updates are essential for security to protect your phone or laptop from new types of attacks. However, updates can sometimes cause slowdowns, overheating, or battery drain. The prudent approach is to wait about a week after a major OS update, check feedback from users of your exact model, and only install it if the coast seems clear.

Secure your smart home

The smart home is giving way to a new concept: the intelligent home. The idea is that neural networks will help your home make its own decisions about what to do and when, all for your convenience — without needing pre-programmed routines. Thanks to the Matter 1.3 standard, a smart home can now manage not just lights, TVs, and locks, but also kitchen appliances, dryers, and even EV chargers! Even more importantly, we’re seeing a rise in devices where Matter over Thread is the native, primary communication protocol, like the new IKEA KAJPLATS lineup. Matter-powered devices from different vendors can see and communicate with each other. This means you can, say, buy an Apple HomePod as your smart home central hub and connect Philips Hue bulbs, Eve Energy plugs, and IKEA BILRESA switches to it.

All of this means that smart and intelligent homes will become more common — and so will the ways to attack them. We have a detailed article on smart home security, but here are a few key tips relevant in light of the transition to Matter.

• Consolidate your devices into a single Matter fabric. Use the minimum number of controllers, for example, one Apple TV + one smartphone. If a TV or another device accessible to many household members acts as a controller, be sure to use password security and other available restrictions for critical functions.
• Choose a hub and controller from major manufacturers with a serious commitment to security.
• Minimize the number of devices connecting your Matter fabric to the internet. These devices — referred to as Border Routers — must be well-protected from external cyberattacks, for example, by restricting their access at the level of your home internet router.
• Regularly audit your home network for any suspicious, unknown devices. In your Matter fabric, this is done via your controller or hub, and in your home network — via your primary router or a feature like Smart Home Monitor in Kaspersky Premium.

Introduction

If you’re a penetration tester, you know that lateral movement is becoming increasingly difficult, especially in well-defended environments. One common technique for remote command execution has been the use of DCOM objects.

Over the years, many different DCOM objects have been discovered. Some rely on native Windows components, others depend on third-party software such as Microsoft Office, and some are undocumented objects found through reverse engineering. While certain objects still work, others no longer function in newer versions of Windows.

This research presents a previously undescribed DCOM object that can be used for both command execution and potential persistence. This new technique abuses older initial access and persistence methods through Control Panel items.

First, we will discuss COM technology. After that, we will review the current state of the Impacket dcomexec script, focusing on objects that still function, and discuss potential fixes and improvements, then move on to techniques for enumerating objects on the system. Next, we will examine Control Panel items, how adversaries have used them for initial access and persistence, and how these items can be leveraged through a DCOM object to achieve command execution.

Finally, we will cover detection strategies to identify and respond to this type of activity.

COM/DCOM technology

What is COM?

COM stands for Component Object Model, a Microsoft technology that defines a binary standard for interoperability. It enables the creation of reusable software components that can interact at runtime without the need to compile COM libraries directly into an application.

These software components operate in a client–server model. A COM object exposes its functionality through one or more interfaces. An interface is essentially a collection of related member functions (methods).

COM also enables communication between processes running on the same machine by using local RPC (Remote Procedure Call) to handle cross-process communication.

Terms

To ensure a better understanding of its structure and functionality, let’s revise COM-related terminology.

1. COM interface
   A COM interface defines the functionality that a COM object exposes. Each COM interface is identified by a unique GUID known as the IID (Interface ID). All COM interfaces can be found in the Windows Registry under HKEY_CLASSES_ROOT\Interface, where they are organized by GUID.
2. COM class (COM CoClass)
   A COM class is the actual implementation of one or more COM interfaces. Like COM interfaces, classes are identified by unique GUIDs, but in this case the GUID is called the CLSID (Class ID). This GUID is used to locate the COM server and activate the corresponding COM class.

   All COM classes must be registered in the registry under HKEY_CLASSES_ROOT\CLSID, where each class’s GUID is stored. Under each GUID, you may find multiple subkeys that serve different purposes, such as:

   • InprocServer32/LocalServer32: Specifies the system path of the COM server where the class is defined. InprocServer32 is used for in-process servers (DLLs), while LocalServer32 is used for out-of-process servers (EXEs). We’ll describe this in more detail later.
   • ProgID: A human-readable name assigned to the COM class.
   • TypeLib: A binary description of the COM class (essentially documentation for the class).
   • AppID: Used to describe security configuration for the class.
3. COM server
   A COM is the module where a COM class is defined. The server can be implemented as an EXE, in which case it is called an out-of-process server, or as a DLL, in which case it is called an in-process server. Each COM server has a unique file path or location in the system. Information about COM servers is stored in the Windows Registry. The COM runtime uses the registry to locate the server and perform further actions. Registry entries for COM servers are located under the HKEY_CLASSES_ROOT root key for both 32- and 64-bit servers.

Client–server model

1. In-process server
   In the case of an in-process server, the server is implemented as a DLL. The client loads this DLL into its own address space and directly executes functions exposed by the COM object. This approach is efficient since both client and server run within the same process.
   

   In-process COM server

2. Out-of-process server
   Here, the server is implemented and compiled as an executable (EXE). Since the client cannot load an EXE into its address space, the server runs in its own process, separate from the client. Communication between the two processes is handled via ALPC (Advanced Local Procedure Call) ports, which serve as the RPC transport layer for COM.

What is DCOM?

DCOM is an extension of COM where the D stands for Distributed. It enables the client and server to reside on different machines. From the user’s perspective, there is no difference: DCOM provides an abstraction layer that makes both the client and the server appear as if they are on the same machine.

Under the hood, however, COM uses TCP as the RPC transport layer to enable communication across machines.

Certain requirements must be met to extend a COM object into a DCOM object. The most important one for our research is the presence of the AppID subkey in the registry, located under the COM CLSID entry.

The AppID value contains a GUID that maps to a corresponding key under HKEY_CLASSES_ROOT\AppID. Several subkeys may exist under this GUID. Two critical ones are:

• AccessPermission: controls access permissions.
• LaunchPermission: controls activation permissions.

These registry settings grant remote clients permissions to activate and interact with DCOM objects.

Lateral movement via DCOM

After attackers compromise a host, their next objective is often to compromise additional machines. This is what we call lateral movement. One common lateral movement technique is to achieve remote command execution on a target machine. There are many ways to do this, one of which involves abusing DCOM objects.

In recent years, many DCOM objects have been discovered. This research focuses on the objects exposed by the Impacket script dcomexec.py that can be used for command execution. More specifically, three exposed objects are used: ShellWindows, ShellBrowserWindow and MMC20.

1. ShellWindows
   ShellWindows was one of the first DCOM objects to be identified. It represents a collection of open shell windows and is hosted by explorer.exe, meaning any COM client communicates with that process.

   In Impacket’s dcomexec.py, once an instance of this COM object is created on a remote machine, the script provides a semi-interactive shell.

   Each time a user enters a command, the function exposed by the COM object is called. The command output is redirected to a file, which the script retrieves via SMB and displays back to simulate a regular shell.

   Internally, the script runs this command when connecting:

   cmd.exe /Q /c cd \ 1> \\127.0.0.1\ADMIN$\__17602 2>&1

   This sets the working directory to C:\ and redirects the output to the ADMIN$ share under the filename __17602. After that, the script checks whether the file exists; if it does, execution is considered successful and the output appears as if in a shell.

   When running dcomexec.py against Windows 10 and 11 using the ShellWindows object, the script hangs after confirming SMB connection initialization and printing the SMB banner. As I mentioned in my personal blog post, it appears that this DCOM object no longer has permission to write to the ADMIN$ share. A simple fix is to redirect the output to a directory the DCOM object can write to, such as the Temp folder. The Temp folder can then be accessed under the same ADMIN$ share. A small change in the code resolves the issue. For example:

   OUTPUT_FILENAME = 'Temp\\__' + str(time.time())[:5]

2. ShellBrowserWindow
   The ShellBrowserWindow object behaves almost identically to ShellWindows and exhibits the same behavior on Windows 10. The same workaround that we used for ShellWindows applies in this case. However, on Windows 11, this object no longer works for command execution.
3. MMC20
   The MMC20.Application COM object is the automation interface for Microsoft Management Console (MMC). It exposes methods and properties that allow MMC snap-ins to be automated.

   This object has historically worked across all Windows versions. Starting with Windows Server 2025, however, attempting to use it triggers a Defender alert, and execution is blocked.

   As shown in earlier examples, the dcomexec.py script writes the command output to a file under ADMIN$, with a filename that begins with __:

   OUTPUT_FILENAME = '__' + str(time.time())[:5]

   Defender appears to check for files written under ADMIN$ that start with __, and when it detects one, it blocks the process and alerts the user. A quick fix is to simply remove the double underscores from the output filename.

   Another way to bypass this issue is to use the same workaround used for ShellWindows – redirecting the output to the Temp folder. The table below outlines the status of these objects across different Windows versions.

   	Windows Server 2025	Windows Server 2022	Windows 11	Windows 10
   ShellWindows	Doesn’t work	Doesn’t work	Works but needs a fix	Works but needs a fix
   ShellBrowserWindow	Doesn’t work	Doesn’t work	Doesn’t work	Works but needs a fix
   MMC20	Detected by Defender	Works	Works	Works

Enumerating COM/DCOM objects

The first step to identifying which DCOM objects could be used for lateral movement is to enumerate them. By enumerating, I don’t just mean listing the objects. Enumeration involves:

• Finding objects and filtering specifically for DCOM objects.
• Identifying their interfaces.
• Inspecting the exposed functions.

Automating enumeration is difficult because most COM objects lack a type library (TypeLib). A TypeLib acts as documentation for an object: which interfaces it supports, which functions are exposed, and the definitions of those functions. Even when TypeLibs are available, manual inspection is often still required, as we will explain later.

There are several approaches to enumerating COM objects depending on their use cases. Next, we’ll describe the methods I used while conducting this research, taking into account both automated and manual methods.

1. Automation using PowerShell
   In PowerShell, you can use .NET to create and interact with DCOM objects. Objects can be created using either their ProgID or CLSID, after which you can call their functions (as shown in the figure below).
   

   Shell.Application COM object function list in PowerShell

   Under the hood, PowerShell checks whether the COM object has a TypeLib and implements the IDispatch interface. IDispatch enables late binding, which allows runtime dynamic object creation and function invocation. With these two conditions met, PowerShell can dynamically interact with COM objects at runtime.

   Our strategy looks like this:

   

   As you can see in the last box, we perform manual inspection to look for functions with names that could be of interest, such as Execute, Exec, Shell, etc. These names often indicate potential command execution capabilities.

   However, this approach has several limitations:

   • TypeLib requirement: Not all COM objects have a TypeLib, so many objects cannot be enumerated this way.
   • IDispatch requirement: Not all COM objects implement the IDispatch interface, which is required for PowerShell interaction.
   • Interface control: When you instantiate an object in PowerShell, you cannot choose which interface the instance will be tied to. If a COM class implements multiple interfaces, PowerShell will automatically select the one marked as [default] in the TypeLib. This means that other non-default interfaces, which may contain additional relevant functionality, such as command execution, could be overlooked.
2. Automation using C++
   As you might expect, C++ is one of the languages that natively supports COM clients. Using C++, you can create instances of COM objects and call their functions via header files that define the interfaces.However, with this approach, we are not necessarily interested in calling functions directly. Instead, the goal is to check whether a specific COM object supports certain interfaces. The reasoning is that many interfaces have been found to contain functions that can be abused for command execution or other purposes.

   This strategy primarily relies on an interface called IUnknown. All COM interfaces should inherit from this interface, and all COM classes should implement it.The IUnknown interface exposes three main functions. The most important is QueryInterface(), which is used to ask a COM object for a pointer to one of its interfaces.So, the strategy is to:

   • Enumerate COM classes in the system by reading CLSIDs under the HKEY_CLASSES_ROOT\CLSID key.
   • Check whether they support any known valuable interfaces. If they do, those classes may be leveraged for command execution or other useful functionality.

   This method has several advantages:

   • No TypeLib dependency: Unlike PowerShell, this approach does not require the COM object to have a TypeLib.
   • Use of IUnknown: In C++, you can use the QueryInterface function from the base IUnknown interface to check if a particular interface is supported by a COM class.
   • No need for interface definitions: Even without knowing the exact interface structure, you can obtain a pointer to its virtual function table (vtable), typically cast as a void*. This is enough to confirm the existence of the interface and potentially inspect it further.

   The figure below illustrates this strategy:

   

   This approach is good in terms of automation because it eliminates the need for manual inspection. However, we are still only checking well-known interfaces commonly used for lateral movement, while potentially missing others.

3. Manual inspection using open-source tools
   As you can see, automation can be difficult since it requires several prerequisites and, in many cases, still ends with a manual inspection. An alternative approach is manual inspection using a tool called OleViewDotNet, developed by James Forshaw. This tool allows you to:
   • List all COM classes in the system.
   • Create instances of those classes.
   • Check their supported interfaces.
   • Call specific functions.
   • Apply various filters for easier analysis.
   • Perform other inspection tasks.
   

   Open-source tool for inspecting COM interfaces

   One of the most valuable features of this tool is its naming visibility. OleViewDotNet extracts the names of interfaces and classes (when available) from the Windows Registry and displays them, along with any associated type libraries.

   This makes manual inspection easier, since you can analyze the names of classes, interfaces, or type libraries and correlate them with potentially interesting functionality, for example, functions that could lead to command execution or persistence techniques.

Control Panel items as attack surfaces

Control Panel items allow users to view and adjust their computer settings. These items are implemented as DLLs that export the CPlApplet function and typically have the .cpl extension. Control Panel items can also be executables, but our research will focus on DLLs only.

Attackers can abuse CPL files for initial access. When a user executes a malicious .cpl file (e.g., delivered via phishing), the system may be compromised – a technique mapped to MITRE ATT&CK T1218.002.

Adversaries may also modify the extensions of malicious DLLs to .cpl and register them in the corresponding locations in the registry.

• Under HKEY_CURRENT_USER:
  HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls

• Under HKEY_LOCAL_MACHINE:
  • For 64-bit DLLs:
    HKLM\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
  • For 32-bit DLLs:
    HKLM\Software\WOW6432Node\Microsoft\Windows\CurrentVersion\Control Panel\Cpls

These locations are important when Control Panel DLLs need to be available to the current logged-in user or to all users on the machine. However, the “Control Panel” subkey and its “Cpls” subkey under HKCU should be created manually, unlike the “Control Panel” and “Cpls” subkeys under HKLM, which are created automatically by the operating system.

Once registered, the DLL (CPL file) will load every time the Control Panel is opened, enabling persistence on the victim’s system.

It’s worth noting that even DLLs that do not comply with the CPL specification, do not export CPlApplet, or do not have the .cpl extension can still be executed via their DllEntryPoint function if they are registered under the registry keys listed above.

There are multiple ways to execute Control Panel items:

• From cmd: control.exe [filename].cpl
• By double-clicking the .cpl file.

Both methods use rundll32.exe under the hood:

rundll32.exe shell32.dll,Control_RunDLL [filename].cpl

This calls the Control_RunDLL function from shell32.dll, passing the CPL file as an argument. Everything inside the CPlApplet function will then be executed.

However, if the CPL file has been registered in the registry as shown earlier, then every time the Control Panel is opened, the file is loaded into memory through the COM Surrogate process (dllhost.exe):

What happened was that a Control Panel with a COM client used a COM object to load these CPL files. We will talk about this COM object in more detail later.

The COM Surrogate process was designed to host COM server DLLs in a separate process rather than loading them directly into the client process’s address space. This isolation improves stability for the in-process server model. This hosting behavior can be configured for a COM object in the registry if you want a COM server DLL to run inside a separate process because, by default, it is loaded in the same process.

‘DCOMing’ through Control Panel items

While following the manual approach of enumerating COM/DCOM objects that could be useful for lateral movement, I came across a COM object called COpenControlPanel, which is exposed through shell32.dll and has the CLSID {06622D85-6856-4460-8DE1-A81921B41C4B}. This object exposes multiple interfaces, one of which is IOpenControlPanel with IID {D11AD862-66DE-4DF4-BF6C-1F5621996AF1}.

I immediately thought of its potential to compromise Control Panel items, so I wanted to check which functions were exposed by this interface. Unfortunately, neither the interface nor the COM class has a type library.

Normally, checking the interface definition would require reverse engineering, so at first, it looked like we needed to take a different research path. However, it turned out that the IOpenControlPanel interface is documented on MSDN, and according to the documentation, it exposes several functions. One of them, called Open, allows a specified Control Panel item to be opened using its name as the first argument.

Full type and function definitions are provided in the shobjidl_core.h Windows header file.

It’s worth noting that in newer versions of Windows (e.g., Windows Server 2025 and Windows 11), Microsoft has removed interface names from the registry, which means they can no longer be identified through OleViewDotNet.

Returning to the COpenControlPanel COM object, I found that the Open function can trigger a DLL to be loaded into memory if it has been registered in the registry. For the purposes of this research, I created a DLL that basically just spawns a message box which is defined under the DllEntryPoint function. I registered it under HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls and then created a simple C++ COM client to call the Open function on this interface.

As expected, the DLL was loaded into memory. It was hosted in the same way that it would be if the Control Panel itself was opened: through the COM Surrogate process (dllhost.exe). Using Process Explorer, it was clear that dllhost.exe loaded my DLL while simultaneously hosting the COpenControlPanel object along with other COM objects.

Based on my testing, I made the following observations:

1. The DLL that needs to be registered does not necessarily have to be a .cpl file; any DLL with a valid entry point will be loaded.
2. The Open() function accepts the name of a Control Panel item as its first argument. However, it appears that even if a random string is supplied, it still causes all DLLs registered in the relevant registry location to be loaded into memory.

Now, what if we could trigger this COM object remotely? In other words, what if it is not just a COM object but also a DCOM object? To verify this, we checked the AppID of the COpenControlPanel object using OleViewDotNet.

Both the launch and access permissions are empty, which means the object will follow the system’s default DCOM security policy. By default, members of the Administrators group are allowed to launch and access the DCOM object.

Based on this, we can build a remote strategy. First, upload the “malicious” DLL, then use the Remote Registry service to register it in the appropriate registry location. Finally, use a trigger acting as a DCOM client to remotely invoke the Open() function, causing our DLL to be loaded. The diagram below illustrates the flow of this approach.

The trigger can be written in either C++ or Python, for example, using Impacket. I chose Python because of its flexibility. The trigger itself is straightforward: we define the DCOM class, the interface, and the function to call. The full code example can be found here.

Once the trigger runs, the behavior will be the same as when executing the COM client locally: our DLL will be loaded through the COM Surrogate process (dllhost.exe).

As you can see, this technique not only achieves command execution but also provides persistence. It can be triggered in two ways: when a user opens the Control Panel or remotely at any time via DCOM.

Detection

The first step in detecting such activity is to check whether any Control Panel items have been registered under the following registry paths:

• HKCU\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
• HKLM\Software\Microsoft\Windows\CurrentVersion\Control Panel\Cpls
• HKLM\Software\WOW6432Node\Microsoft\Windows\CurrentVersion\Control Panel\Cpls

Although commonly known best practices and research papers regarding Windows security advise monitoring only the first subkey, for thorough coverage it is important to monitor all of the above.

In addition, monitoring dllhost.exe (COM Surrogate) for unusual COM objects such as COpenControlPanel can provide indicators of malicious activity.
Finally, it is always recommended to monitor Remote Registry usage because it is commonly abused in many types of attacks, not just in this scenario.

Conclusion

In conclusion, I hope this research has clarified yet another attack vector and emphasized the importance of implementing hardening practices. Below are a few closing points for security researchers to take into account:

• As shown, DCOM represents a large attack surface. Windows exposes many DCOM classes, a significant number of which lack type libraries – meaning reverse engineering can reveal additional classes that may be abused for lateral movement.
• Changing registry values to register malicious CPLs is not good practice from a red teaming ethics perspective. Defender products tend to monitor common persistence paths, but Control Panel applets can be registered in multiple registry locations, so there is always a gap that can be exploited.
• Bitness also matters. On x64 systems, loading a 32-bit DLL will spawn a 32-bit COM Surrogate process (dllhost.exe *32). This is unusual on 64-bit hosts and therefore serves as a useful detection signal for defenders and an interesting red flag for red teamers to consider.