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.

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Gartner, Magic Quadrant for Endpoint Protection Platforms, By Deepak Mishra, Evgeny Mirolyubov, Nikul Patel, May 29, 2026

Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

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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

Introduction

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

The dangers of Amazon SES abuse

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

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

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

How compromise happens

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

Examples of phishing with Amazon SES

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

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

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

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

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

Amazon SES and BEC

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

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

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

Takeaways

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

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

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

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

ARC isan email authentication system that preserves trust and integrity across intermediaries, ensuring legitimate emails are recognized even after multiple hops. It is an important setup for your email ecosystem!