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.

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

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

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

Ransomware attacks decline but remain a major threat

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

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

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

The continued rise of EDR killers and defense evasion tooling

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

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

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

The appearance of new families adopting post-quantum cryptography

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

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

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

The shift to encryptionless extortion

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

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

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

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

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

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

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

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

Ransomware developments on the dark web

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

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

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

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

Law enforcement actions

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

Top ransomware groups in 2025

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

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

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

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

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

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

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

New actors in 2026

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

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

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

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

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

Conclusion and protection recommendations

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

To resist the evolving threat, Kaspersky recommends organizations:

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

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

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

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

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

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

Every year, hundreds of millions of real user passwords leak onto the dark web. We analyzed 231 million unique passwords from dark-web leaks between 2023 and 2026, and the conclusions are bleak: the vast majority are extremely weak. To crack 60% of these passwords, a hacker needs only an hour and a few dollars in their pocket. Furthermore, password cracking is accelerating by the year; in our similar 2024 study, the percentage of vulnerable passwords was lower.

Today we’re looking at just how reliable the average password is (spoiler: not really), and how you can secure your data and accounts using more robust methods. At the same time, we’ll highlight the patterns most commonly found in actual user passwords.

How passwords are cracked

In our previous study, we detailed the methods for storing and cracking passwords, but here’s a quick refresher on the essentials.

These days, passwords are almost never stored in plain text. For instance, if you create an account with the password “Password123!”, the server won’t store it as-is. Instead, the password is hashed using specific algorithms, turning it into a fixed-length string of letters and numbers (a hash) which is what actually stays on the server. For example, here’s what the MD5 hash for “Password123!” looks like:

2c103f2c4ed1e59c0b4e2e01821770fa.

Every time the user enters their password, it’s converted into a hash and compared against the one stored on the server; if the hashes match, the password is correct. If an attacker gets their hands on this hash, they have to decrypt it to recover the original password — this is what’s known as “password cracking”. This is typically done using owned or rented GPUs, and several methods can be employed for the crack:

• Exhaustive enumeration (brute force). The computer tries every possible combination of characters, calculating the hash for each one. This method is the easiest way to crack short passwords, or those consisting of a single character set (such as digits only).
• Rainbow tables. A total nightmare for anyone with a simple password, this is essentially a “phone book” for passwords whose hashes have already been cracked via brute force or smart algorithms. All an attacker has to do is find a matching hash and see which password corresponds to it.
• Smart cracking. These algorithms are trained on databases of leaked passwords. They understand the frequency of different character combinations, and run their checks from the most likely to the least popular sequences. They account for dictionary words, character substitutions (a → @ or s → $), and consider common password structures like “dictionary word + number + special character”, while checking hashes against rainbow tables. Combining these methods significantly accelerates the cracking process.

Beyond that, attackers can also intercept passwords in plain text. There are numerous ways to do this, ranging from phishing (where a victim is lured to a fake web page and enters their password voluntarily) and keyloggers that capture keystrokes, to stealers or Trojans that swipe documents, cookies, clipboard data, and more. Unfortunately, many users keep their passwords as plain text in notes, messaging apps, and documents, or save them in browsers where attackers can extract them in seconds.

Every year, we track around a hundred million plain-text password leaks. We use these databases to warn Kaspersky Password Manager users if their data has been compromised. To address the most frequent question we get on this: no, we don’t know our users’ passwords. We’ve explained in non-techie language exactly how we compare your passwords to leaked ones without actually knowing them — and why neither your passwords stored in Kaspersky Password Managernor even their hashes ever leave your device — in our overviews of our leak analysis technology and our password manager’s internal architecture. Give them a read; you’ll be surprised by just how elegant the design is.

60% of passwords are cracked in under an hour

We expanded the database from our previous study by an additional 38 million real passwords posted by attackers on dark-web forums and compared the results. Testing was conducted using a single RTX 5090 GPU for passwords hashed with the MD5 algorithm. The data for the analysis was obtained from our Digital Footprint Intelligence service. You can review the algorithm we used to assess password strength in our article on Securelist.

Unfortunately, passwords remain as weak as ever, while cracking them becomes faster and easier with every year. Today, 60% of passwords can be cracked in less than an hour; two years ago, that figure was 59%. But the truly frightening part is something else: nearly half of all passwords (48%) are cracked in less than a minute!

Cracking time	Percentage of passwords crackable within this time in 2024	Percentage of passwords crackable within this time today
Less than a minute	45%	48%
Less than an hour	59% (+14%)	60% (+12%)
Less than 24 hours	67% (+8%)	68% (+8%)
Less than a month	73% (+6%)	74% (+6%)
Less than a year	77% (+4%)	77% (+3%)
More than a year	23%	23%

Attackers owe this boost in speed to graphics processors, which grow more powerful every year. While an RTX 4090 in 2024 could brute-force MD5 hashes at a rate of 164 gigahashes (billion hashes) per second, the new RTX 5090 has increased that speed by 34% — reaching 220 gigahashes per second.

And although a high-end video card like that currently retails for several thousand dollars, the price tag isn’t much of a barrier: there are plenty of cheap cloud services available for renting GPU computing power. Depending on the configuration and the model, rental costs range from a few cents to a few dollars per hour. As we’ve seen, one hour is all an attacker needs to crack three out of every five passwords they’ve found in a leak. Plus, depending on the scale of the task, they can always rent ten or even a hundred GPUs instead of just one…

It’s worth noting that cracking every password in a dataset doesn’t take much longer than cracking a single one. During each iteration, once the attacker calculates a hash for a specific character combination, they check if that same hash exists anywhere in the dataset — and the larger the dataset, the easier it is to find a match. If a match is found, the corresponding password is flagged as “cracked”, and the algorithm moves along to the next one.

Which passwords are vulnerable?

The strength of any password depends on its length, content variety, and the randomness of that content. Passwords created by humans turn out to be the least resilient — unfortunately, humans are quite predictable. We use dictionary words and character combinations that smart algorithms have long since mastered, we avoid long random strings, and patterns can be found even in keystrokes we believe are random. Interestingly enough, passwords generated by AI still carry the fingerprints of a human approach; we covered this in a separate post on how to create a strong yet memorable password.

Password length is the primary factor affecting cracking time. As you can see from the table below, it takes less than 24 hours to crack almost any eight-character password.

But the predictability of your password is just as important. Think you’re boosting security by adding a number or a special character to a memorable word? You are, but only slightly. The patterns people use to create passwords are easily predictable and, at times, pretty amusing — though this is no laughing matter.

What we learned about password patterns

Analysis of over 200 million passwords revealed characteristic patterns that allow smart algorithms to crack user passwords with ease.

Pick a number

More than half of all passwords (53%) end with one or more digits, while nearly one in six (17%) starts with a number. Every eighth password (12%) contains sequences that look a lot like years — ranging from 1950 to 2030 — and one in ten (10%) specifically falls between 1990 and 2026. This most likely happens because folks add their birth year (or that of someone close), some other significant year, or the year they created the password or account. Fun fact: based on the distribution of these dates, it suggests that the most active internet users were born between 2000 and 2012.

However, among all numeric combinations, the most popular turned out to be… you guessed it: “1234”. Overall, patterns involving sequential keyboard presses (“qwerty, ,”ytrewq”, and the like) appear in 3% of passwords.

Special characters aren’t a silver bullet

Most password policies in recent years require at least one special character. The absolute winner in this category is the @ symbol: it appears in one out of every 10 passwords. The period (.) comes in second, followed by the exclamation point (!) in third.

Love rules the world… and Skibidi Toilet does too

Emotionally charged words often form the foundation of a password, and despite everything, positive words are more common. Frequently occurring examples include “love”, “angel”, “team”, “mate”, “life”, and “star”. That said, negativity pops up too — mostly in the form of common English swear words.

Interestingly, viral memes are reflected in passwords as well. Between 2023 and 2026, the use of the word Skibidi in passwords skyrocketed 36-fold! Naturally (see the link if it doesn’t seem natural), “toilet” saw a boost too, though to a lesser extent.

Users tend to keep their passwords unchanged for years

More than half of the passwords (54%) we identified in recent leaks have surfaced before. Part of this can be explained by the same data migrating from one dataset to another. However, there’s a much more troubling reason too: many users simply haven’t changed their passwords in years.

Analyzing the dates found within passwords shows that combinations containing the years from 2020 through 2024 remain popular. It seems people add the current year to their password when they create it — and then forget about it for several years. This actually allows us to calculate the average lifespan of a password: about three to five years.

This is a dangerous trend. For one, smart algorithms can crack much more complex passwords over that kind of timeframe. Secondly, the longer your password remains unchanged, the higher the probability it will leak — whether through a breach, malware infection, or a phishing attack.

The situation gets even worse when the same password is used across multiple accounts. In this case, attackers don’t even need to crack anything; they just need to find your password in a single leak and plug it into other sites.

How to protect your passwords and accounts

If you’ve realized while reading this post that your own passwords are among those easily crackable — don’t panic. We’ve put together a list of simple but essential tips for you.

Use a password manager

The weakest passwords are the ones people come up with themselves. Creating and memorizing hundreds of sequences of 16–20 random characters (since every site requires a unique, long password) is a daunting, unrealistic task.

That’s why you should delegate password generation and storage to our password manager. It doesn’t just create and store complex, randomized passwords in an encrypted format; it also syncs them across all your devices. To decrypt your vault, you only need to remember one main password that no one knows but you — our guide on mnemonic passwords can help you with that.

Don’t store passwords as plain text

Whatever you do, never write down passwords in files, messages, or documents. They lack the robust encryption provided by a password manager. Furthermore, these kinds of notes fall into the hands of attackers instantly if you happen to pick up a Trojan or an infostealer.

Don’t store passwords in your browser

Many users save their passwords in their browsers — especially since they conveniently offer to do it automatically. Unfortunately, research shows that malware has evolved to extract these passwords from all popular browsers almost instantly. Kaspersky Password Manager can help you import saved passwords from your favorite browser — just follow our simple, three-step guide. Most importantly, don’t forget to clear the browser’s password storage once the import is complete.

Switch to passkeys

Wherever possible, use passkeys — a cryptographic replacement for passwords. In this setup, the service stores a public key, while the private key remains on your device and is never transmitted. During login, the device simply signs a one-time request. Additionally, passkeys are tied to a specific domain, meaning phishing attacks using spoofed addresses won’t work. Kaspersky Password Manager allows you to store both passwords and passkeys, solving the problem of syncing them across different ecosystems, including Windows, Android, macOS, and iOS.

Set up two-factor authentication

Enable two-factor authentication wherever possible. Even if your password is compromised, a properly configured 2FA setup makes it extremely difficult for the attacker to access your account. For maximum security, skip the one-time codes sent via SMS and use authenticator apps instead — and yes, Kaspersky Password Manager comes in handy here, too.

Practice good digital hygiene

Remember, storing your passwords correctly is only half the battle. It’s crucial to follow the rules of digital hygiene: avoid downloading unverified files, pirated software, cheats, or cracks, and don’t click on random links. The number of infostealer attacks has been steadily rising in recent years, which means you need a robust security solution for full protection. We recommend Kaspersky Premium — it protects all your devices from Trojans, phishing, and other threats. Besides, the subscription includes our password manager.

For those serious about account security, check out our collection of posts on passwords, passkeys, and two-factor authentication:

• Passkeys in 2025: your complete guide to passwordless sign-in
• Passkeys in 2025: tips for power users
• Creating an unforgettable password
• How to create strong passwords and where to store them
• Switching to Kaspersky: a step-by-step migration guide

April 7 marks World Health Day. The theme for 2026 is “Together for health. Stand with science” — a call to join forces in the fight for evidence-based medicine and scientific progress. Many people view telehealth as one of the crowning achievements of this progress: you can basically get a doctor’s consultation in five minutes without ever leaving your couch. But there’s a catch…

Medical data sells on the black or gray markets for dozens of times more than credit card info or social media logins. Unlike a credit card, which you can just block and replace, you can’t exactly reset your medical history. Your name, birthday, address, phone number, insurance ID, diagnoses, test results, prescriptions, and treatment plans stay relevant for years. This is a goldmine for everything from targeted marketing to blackmail, fraud, or identity theft.

And with the rise of AI, the internet is now flooded with fake websites that claim to offer medical services but are actually designed to strip-mine confidential info from unsuspecting victims. Today, we’re diving into which medical details are at risk, why hackers want them, and how you can stop them in their tracks.

More valuable than credit cards

Scammers monetize stolen medical data both in bulk and through individual sales. Their first move is usually to extort a ransom from the companies they’ve successfully hacked. (In fact, back in 2024, 91% of malware-related healthcare data leaks in the U.S. were the result of ransomware attacks.) But later, the leaked data is then used for pinpointed, personal attacks. It allows hackers to build a medical profile of a victim — what meds they buy, how often, and what they take long-term — to then sell that info to big pharma or marketers, or to use it for targeted phishing scams like pitching a fake innovative treatment. They can even blackmail a patient over a sensitive diagnosis or use the info to fraudulently score prescriptions for controlled substances. On top of that, insurance companies are also hungry for this kind of data. They analyze these details to hike up insurance premiums for patients or, in some cases, refuse to provide coverage altogether. In short, there are plenty of ways they can use it against you.

How bad is it really?

The biggest medical data breach in history went down in February 2024, when the BlackCat hacking group broke into the systems of Change Healthcare. This is a division of UnitedHealth Group, which processes around 15 billion insurance transactions a year and acts as the financial middleman between patients, healthcare providers, and insurance companies.

For nine days, the attackers roamed freely through Change Healthcare’s internal systems, siphoning off six terabytes of confidential data before finally launching their ransomware. UnitedHealth was forced to completely yank Change Healthcare datacenters offline to stop the encryptor from spreading, and they ended up paying a 22-million-dollar ransom to the extortionists. The attack effectively paralyzed the U.S. healthcare system. The number of victims was revised three times: first 100 million, then 190 million, and the final tally hit a staggering 192.7 million people, with total damages estimated at 2.9 billion dollars. And the reason (on the Change Healthcare’s side) for this massive incident — which we broke down in detail in a separate post — was simply… a lack of two-factor authentication on a remote desktop access portal.

Before that, the mental health telehealth startup Cerebral embedded third-party tracking tools directly into its website and apps. As a result, the data of 3.2 million patients — including names, medical and prescription histories, and insurance info — leaked out to LinkedIn, Snapchat, and TikTok. The U.S. Federal Trade Commission slapped the company with a 7.1-million-dollar fine, and issued an unprecedented ban on using medical data for advertising purposes. By the way, that same startup also made the headlines for sending its clients promotional postcards without envelopes, displaying patient names and phrasing that made it easy for anyone to figure out their diagnosis.

Why telehealth is so vulnerable

Let’s take a look at the main weak spots in telehealth services.

• Ad trackers in medical apps. Trackers from Facebook, TikTok, Snapchat, and other tech giants are often baked right into telehealth platforms, leaking patient data to advertisers without users ever knowing.
• Unsecured communication channels. Sometimes doctors chat with patients through regular messaging apps instead of certified medical platforms. It’s convenient, sure, but it’s illegal for the clinic and totally unsafe for the patient.
• Platform vulnerabilities. Telemedicine platforms are prone to classic web attacks, such as SQL injections that let hackers dump entire patient databases, session hijacking, and data interception when connection encryption is weak or nonexistent.
• Poor staff training. Our research showed that 30% of doctors have dealt with compromised patient data specifically during telehealth sessions, and 42% of medical staff don’t actually understand how their patients’ data is being protected.
• Outdated medical devices. Many wearable medical gadgets (like heart monitors or blood pressure cuffs) use an old data transfer protocol called MQTT. It’s full of holes that could potentially allow hackers to steal sensitive info or even mess with how the device functions.

Spam and phishing in telehealth

Hackers aren’t the only ones interested in the medical field — spammers and scammers are all over it, too. They pitch “medical services” with deals that look way too good to be true, send out emails about supposed changes to your health insurance, or talk up “ancient Himalayan healing traditions”. Of course, all the links they send lead to suspicious websites offering dubious goods or services.

Should you land on such a phishing site, scammers will try to squeeze every bit of private info they can out of you: photos of your ID, insurance policy, prescriptions, and sometimes even… photos of body parts that supposedly need medical attention. From there, this data can be dumped and sold on the dark web — or used for blackmail, extortion, and follow-up phishing attacks. To learn more about how the underground data assembly line works, check out our post, What happens to data stolen using phishing?

As a rule of thumb, fake clinic sites usually skip the privacy policy section, and bombard you with “today only” deals that seem too good to be true. That said, with the help of AI, creating a professional-looking site that’s indistinguishable from the real thing is now a total breeze: you don’t even need design skills or fluency in the victim’s language. That’s exactly why we recommend using our comprehensive security suite — it’s designed to sniff out spam, scams and phishing, and warn you about fake websites before you land on them.

Safety tips for telehealth patients

• Set up a dedicated email address for medical services. If this address leaks because a clinic gets hacked, it makes it much harder for scammers to track the rest of your digital life.
• Avoid using Google, Apple, or social media sign-in for telehealth sites. Keeping things separate makes it way tougher to link your medical data to your personal accounts.
• Double-check which platform is being used for your consultation. If the clinic suggests a call or chat through a standard messaging app, that’s a red flag. A secure, encrypted patient portal provided by the clinic is significantly safer.
• Never send medical documents via chat apps or social media. Always upload lab results, scans, and records through the clinic’s official patient portal.
• Use a unique, complex password for every account. Your government portal, clinic login, and doctor-booking app should each have a separate password. Kaspersky Password Manager can generate and store all of them for you; it also regularly scans leak databases, and alerts you if any of your accounts are compromised.
• Turn on two-factor authentication. Do this first of all for government services and medical organizations. We recommend using an authenticator app rather than SMS codes: it’s more secure and totally anonymous. Kaspersky Password Manager can help you out here, too.
• Share only what’s necessary. Don’t feel obligated to fill out every optional field in medical apps or on websites. The less data a service stores, the less there is to leak.
• Be careful about sharing health info on social media or in chat apps. Scammers love to exploit people when they’re vulnerable. For instance, in 2024, hackers gained the trust of the XZ Utils developer who had publicly posted about burnout and depression. They convinced him to hand over control of his tool, which they then loaded with malicious code. Since XZ Utils is used in tons of Linux systems and affects OpenSSH (a protocol for remote server connections), the attack could have wrecked a huge chunk of the internet if it hadn’t been caught in time.
• Don’t install telehealth apps from unknown developers. Check the reviews and take a minute to skim the privacy policy — even major platforms might be sharing your data with third parties.
• Keep an eye on your medical records. Strange prescriptions, doctor visits you never made, or meds you’ve never heard of can all be signs that your account has been compromised.
• Configure and regularly update your health gadgets. Fitness trackers, blood pressure monitors, smart scales, and activity trackers all send data to the web. Improper settings or unpatched vulnerabilities are an open door for data breaches.

What else you need to know about protecting your health online:

• Brain drain: vulnerabilities in mental health apps
• Why data brokers build dossiers on you, and how to stop them doing so
• How chatting with a bot can lead to tragedy
• Privacy in reproductive health apps
• Five problems of telehealth

In February 2026, the cybersecurity firm Oversecured published a report that makes you want to factory reset your phone and move into a remote cabin in the woods. Researchers audited 10 popular Android mental health apps — ranging from mood trackers and AI therapists to tools for managing depression and anxiety — and uncovered… 1575 vulnerabilities! Fifty-four of those flaws were classified as critical. Given the download stats on Google Play, as many as 15 million people could be affected. The real kicker? Six out of the ten apps tested explicitly promised users that their data was “fully encrypted and securely protected”.

We’re breaking down this scandalous “brain drain”: what exactly could leak, how it’s happening, and why “anonymity” in these services is usually just a marketing myth.

What was found in the apps

Oversecured is a mobile app security firm that uses a specialized scanner to analyze APK files for known vulnerability patterns across dozens of categories. In January 2026, researchers ran ten mental health monitoring apps from Google Play through the scanner — and the results were, shall we say, “spectacular”.

App Type	Installs	Security vulnerabilities
		High-severity	Medium-severity	Low-severity	Total
Mood & habit tracker	10M+	1	147	189	337
AI therapy chatbot	1M+	23	63	169	255
AI emotional health platform	1M+	13	124	78	215
Health & symptom tracker	500k+	7	31	173	211
Depression management tool	100k+	0	66	91	157
CBT-based anxiety app	500k+	3	45	62	110
Online therapy & support community	1M+	7	20	71	98
Anxiety & phobia self-help	50k+	0	15	54	69
Military stress management	50k+	0	12	50	62
AI CBT chatbot	500k+	0	15	46	61
Total	14.7М+	54	538	983	1575

The anatomy of the flaws

The discovered vulnerabilities are diverse, but they all boil down to one thing: giving attackers access to data that should be under lock and key.

For starters, one of the vulnerabilities allows an attacker to access any internal activity of the app — even that never intended for external eyes. This opens the door to hijacking authentication tokens and user session data. Once an attacker has those, they essentially could gain access to a user’s therapy records.

Another issue is insecure local data storage with read permissions granted to any other app on the device. In other words, that random flashlight app or calculator on your smartphone could potentially read your cognitive behavioral therapy (CBT) logs, personal notes, and mood assessments.

The researchers also found unencrypted configuration data baked right into the APK installation files. This included backend API endpoints and hardcoded URLs for Firebase databases.

Furthermore, several apps were caught using the cryptographically weak java.util.Random class to generate session tokens and encryption keys.

Finally, most of the tested apps lacked root/jailbreak detection. On a rooted device, any third-party app with root privileges could gain total access to every bit of locally stored medical data.

Shockingly, of the 10 apps analyzed, only four received updates in February 2026. The rest haven’t seen a patch since November 2025, and one hasn’t been touched since September 2024. Going 18 months without a security patch is a lifetime in this industry — especially for an app housing mood journals, therapy transcripts, and medication schedules.

Here’s a quick reminder of just how dangerous the misuse of this type of data gets. In 2024, the tech world was rocked by a sophisticated attack on XZ Utils, a critical component found in virtually every operating system based on the Linux kernel. The attacker successfully pressured the maintainer into handing over code commit permissions by exploiting the developer’s public admission of burnout and a lack of motivation to carry on with the project. Had the attack been completed, the damage would have been mind-boggling given that roughly 80% of the world’s servers run on Linux.

What could leak?

What do these apps collect and store? It’s the kind of stuff you’d likely only share with a trusted clinician: therapy session transcripts, mood logs, medication schedules, self-harm indicators, CBT notes, and various clinical assessment scales.

As far back as 2021, complete medical records were selling on the dark web for US$1000 each. For comparison, a stolen credit card number goes for anywhere between US$5 and US$30. Medical records contain a full identity package: name, address, insurance details, and diagnostic history. Unlike a credit card, you can’t exactly “reissue” your medical history. Furthermore, medical fraud is notoriously difficult to spot. While a bank might flag a suspicious transaction in hours, a fraudulent insurance claim for a phantom treatment can go unnoticed for years.

We’ve seen this movie before

The Oversecured study isn’t just an isolated horror story.

Back in 2020, Julius Kivimäki hacked the database of the Finnish psychotherapy clinic Vastaamo, making off with the records of 33 000 patients. When the clinic refused to cough up a €400 000 ransom, Kivimäki began sending direct threats to patients: “Pay €200 in Bitcoin within 24 hours, or else your records go public”. Ultimately, he leaked the entire database onto the dark web anyway. At least two people died by suicide, and the clinic was forced into bankruptcy. Kivimäki was eventually sentenced to six years and three months in prison, marking a record-breaking trial in Finland for the sheer number of victims involved.

In 2023, the U.S. Federal Trade Commission (FTC) slapped the online therapy giant BetterHelp with a US$7.8 million fine. Despite stating on their sign-up page that your data was strictly confidential, the company was caught funneling user info — including mental health questionnaire responses, emails, and IP addresses — to Facebook, Snapchat, Criteo, and Pinterest for targeted advertising. After the dust settled, 800 000 affected users received a grand total of… US$10 each in compensation.

By 2024, the FTC set its sights on the telehealth firm Cerebral, tagging them with a US$7 million fine. Through tracking pixels, Cerebral leaked the data of 3.2 million users to LinkedIn, Snapchat, and TikTok. The haul included names, medical histories, prescriptions, appointment dates, and insurance info. And the cherry on top? The company sent promotional postcards (sans envelopes) to 6000 patients, which effectively broadcasted that the recipients were undergoing psychiatric treatment.

In September 2024, security researcher Jeremiah Fowler stumbled upon an exposed database belonging to Confidant Health, a provider specializing in addiction recovery and mental health services. The database contained audio and video recordings of therapy sessions, transcripts, psychiatric notes, drug test results, and even copies of driver’s licenses. In total, 5.3 terabytes of data, 126 000 files, or 1.7 million records were sitting there without a password.

Why anonymity is an illusion

Developers love to drop the line: “We never share your personal data with anyone.” Technically, that might be true — instead, they share “anonymized profiles”. The catch? De-anonymizing that data isn’t exactly rocket science anymore. Recent research highlights that using LLMs to strip away anonymity has become a routine reality.

Even the “anonymization” process itself is often a mess. A study by Duke University revealed that data brokers are openly hawking the mental health data of Americans. Out of 37 brokers surveyed, 11 agreed to sell data linked to specific diagnoses (like depression, anxiety, and bipolar disorder), demographic parameters, and in some cases, even names and home addresses. Prices started as low as US$275 for 5000 aggregated records.

According to the Mozilla Foundation, by 2023, 59% of popular mental health apps failed to meet even the most basic privacy standards, and 40% had actually become less secure than the previous year. These apps allowed account creation via third-party services (like Google, Apple, and Facebook), featured suspiciously brief privacy policies that glossed over data collection details, and employed a clever little loophole: some privacy policies applied strictly to the company’s website, but not the app itself. In short, your clicks on the site were “protected”, but your actions within the app were fair game.

How to protect yourself

Cutting these apps out of your life entirely is, of course, the most foolproof option — but it’s not the most realistic one. Besides, there’s no guarantee you can actually nuke the data already collected — even if you delete your account. We previously covered the grueling process of scrubbing your info from data broker databases; it’s possible, but prepare for a headache. So, how can you stay safe?

• Check permissions before you hit “Install”. In Google Play, navigate to App description → About this app → Permissions. A mood tracker has no business asking for access to your camera, microphone, contacts, or precise GPS location. If it does, it’s not looking out for your well-being — it’s harvesting data.
• Actually read the privacy policy. We get it — nobody reads these multi-page manifestos. But when a service is vacuuming up your most intimate thoughts, it’s worth a skim. Look for the red flags: does the company share data with third parties? Can you manually delete your records? Does the policy explicitly cover the app itself, or just the website? You can always feed the policy text into an AI and ask it to flag any privacy deal-breakers.
• Check the last updated date. An app that hasn’t seen an update in over six months is likely a playground for unpatched vulnerabilities. Remember: six out of the 10 apps Oversecured tested hadn’t been touched in months.
• Disable everything non-essential in your phone’s privacy settings. Whenever prompted, always select “ask not to track”. When an app pleads with you to enable a specific type of tracking — claiming it’s for “internal optimization” — it’s almost always a marketing ploy rather than a functional necessity. After all, if the app truly won’t work without a certain permission, you can always go back and toggle it on later.
• Don’t use “Sign in with…” services. Authenticating via Facebook, Apple, Google, or Microsoft creates additional identifiers and gives companies a golden opportunity to link your data across different platforms.
• Treat everything you type like a public social media post. If you wouldn’t want a random stranger on the internet reading it, you probably shouldn’t be typing it into an app with over 150 vulnerabilities that hasn’t seen a patch since the year before last.

What else you should know about privacy settings and controlling your personal data online:

• Geolocation data brokers: what they do and what happens when they leak
• Why data brokers build dossiers on you, and how to stop them doing so
• How to disappear from the internet
• How to shrink your digital footprint
• How smartphones build a dossier on you

A great place that can sometimes be overlooked on an internal penetration test are the secrets hidden in plain sight. That is, a place where no authentication is required in […]

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