While many operating systems seem to try to prevent you from peeking under the hood, Unix and Linux positively encourage it. One great tool that we’ve looked at before is strace. Using this tool, you can see details about every system call a program makes. As you might imagine, for any significant program, the output from strace can be huge.

While I’m not always a fan of GUIs, this is one of those cases where making the data easier to browse is a great idea. Enter strace-tui, a text-based GUI for strace from [Rodrigodd]. The program can parse output from strace or manage the strace execution itself, and either way, display the data in a useful way.

I started out looking at [janestreet’s] strace_ui, but the OCaml setup was throwing errors for me, so I just gave up. The strace-tui installs like many Rust programs, using cargo, and it went smoothly.

An Example

The strace-tui interface.
The only issue I had running the tool was that I don’t normally keep ~/.cargo/bin on my path. You can add it to your path, link the executable into your path, or solve that in any number of other ways.

As an example, I traced a symbolic link command (ln -sf nature.txt test.link). It is easy to pick out some essential information on the top line. The command took 112 system calls, 14 of them failed (which isn’t unexpected), there were no unfinished calls, no signals, and only a single PID.

The bottom shows things you can do. Arrows or j and k, along with the usual cursor control keys like Home and Page Down scroll through the list. The right and left arrows will expand or collapse items. That will show details about the call in question, including the arguments and return values. You can consult the help for all the details.

Useful Tools

The real power, though, lies in filtering out the noise and searching for specific things. If you are looking at something you don’t want to see, you can press a lowercase h to hide it, but note that it hides everything similar, not just an individual line. An uppercase H will bring up a filter dialog where you can include or exclude groups of data.

Searching is also a great way to find what you want. A slash key starts a search. The N key navigates with a lowercase entry moving forward and an uppercase one moving backward.

For example, if I only wanted to look at openat commands, I could open the dialog. Not only does it show filters, but it also shows how many things match (there are 30 instances of openat). Pressing a will toggle all entries off and then selecting openat greatly reduces the amount of output. I also selected symlinkat, read, and fstat so I would only look at the file-related items.
Peeking at the system call that does the actual linking.
Many of the file operations are related to loading shared libraries and locales. To find the actual line that makes the link, it was easy to press the slash key and some text from the file like test.link.

That will highlight the symlinkat line, which is no surprise, but this is a simple example. If you press Enter or the right arrow, you can see more detail, including arguments, the return value, the amount of time executing, and a backtrace that shows how your program made it to the call.

This is a simple example, but the program can also visualize multi-threaded or multi-process traces using graphs. That can be helpful for analyzing real programs.

Even this simple program has a lot of output. Sure, if you are trying to debug a locale-related problem, all of the lines about loading locale files that don’t exist might be gold. But most of the time, you don’t really care about all the standard loading scaffolding and a tool like this can help cut through the chatter.

Missing Links

According to the project page, there are some missing features, and we presume this is a roadmap for future development.

In particular, the program can’t filter traces for specific processes or threads. There’s also no way to copy details to the clipboard or export filtered traces out to a file. Of course, it is open source, so you can always volunteer to add some of this or your favorite feature.

If you give strace-tui a shot, or have other strace tips and tricks you’d like to share, let us know in the comments.

hackaday.com/2026/06/02/linux-…

We do sometimes go on about how absurdly powerful microcontrollers are these days, but this time it’s technically a microprocessor, not a microcontroller, at the heart of the build — specifically, an STM32MP2. Still, you know you’re living in the future when an STM32 of any sort can not only run [John Cronin]’s gk handheld game console, but provide 3D acceleration to boot.

Full disclosure: you’ve seen this handheld here before — sorta. That was version 3, which was an STM32-based handheld. V3 used the much less powerful STM32H7S7L8, with a single Cortex-M7 clocked at 600 MHz and a 2D NeoChrom GPU. The STM32MP2, by contrast, has dual Cortex-A35 cores running 1.5 GHz and a bonus Cortex-M33. It’s running a custom OS called gkos, which is mostly POSIX-compliant and boasts nigh-instantaneous boot times.

As with the last version, you can run a bevy of emulators from the 8-bit to the 32-bit era, but the added power and OpenGL support mean this handheld also runs N64 games via a fork of mupen64. There are also emulators for ‘real’ computers, namely Atari ST and XL, and a little-known thing known as a “PC”. DOSBox gets the equivalent performance of a 50 MHz 486, which means you can run all the classics, including DOOM, though that will be more performant running the native-running port of sdl-DOOM.

You also get extra inputs to play with and a bigger screen compared to the last version. Oh, and WiFi. There are accelerometers for tilt control, and did we mention the screen’s touch input is supported? If it weren’t for the form-factor, we’d call this a capable little computer. The GK handheld looks like an awesome handheld console, check it out in the demo video below.

youtube.com/embed/HnWTx0CX4E8?…

hackaday.com/2026/06/02/stm32-…

In our search for the unusual or interesting among the world of operating systems, it might seem unexpected that today’s choice for a Daily Driver is the latest version of Microsoft Windows. Aside from Hackaday perhaps having a larger than average percentage of viewers using Linux based operating systems and generally catering to open source enthusiasts, there’s hardly anything special about Windows, is there?

Oddly for me there is — because while it’s a common enough OS for the masses, the last time I had a Windows computer it ran XP. That venerable OS is a world away from today’s Windows 11, and thus as someone who’s exclusively sat in front of a GNOME desktop for much of the last two decades, it’s an entirely new operating system.

There’s no doubt that it will make a Daily Driver, because of course I’ll be able to do my work on it. Where the interest lies is in seeing what Windows has become. Is it still a useful general purpose operating system, or has it become the locked-down walled garden of crapware that its detractors warn you about? Time to dive in.

A Secret Windows Machine

I have had a Windows partition on this machine since I bought it back in 2024. It’s an ex-corporate laptop from a reseller, and those machines always come with a too-small flash drive and a Windows install. So when I bought a new much larger drive for my Linux install I dropped the Windows partition on it too. After all, you never know when you might need Windows for something, right? Two years later and I’ve never touched it, so my first task in my Windows 11 is to run a system update. I timed the start to 16:30, and left it running. I have a gigabit fibre connection so it should be quick, shouldn’t it. At 19:16 I was finally able to use the computer, but even then Microsoft wasn’t quite finished. There were a slew of permissions choices where I had to opt out of their various data slurps, and their offers and mail.

Coming back to the Windows desktop when your last experience was XP with the Windows 95 theme is a bit of a shock. You instinctively head for the Start menu in the bottom left corner and instead find a widget box full of news feeds and stock tickers you don’t want. Closer inspection shows they’ve chased a macOS style interface with a Windows logo on the bottom bar as the Start menu roughly where Mac users find their folder full of apps.

I’m trying to approach this think as a Windows user would, so instead of heading off and downloading open source installers as you might expect, I’m off to the Microsoft Store. Although Redmond has its hand on my shoulder I was able to find GIMP without issue, so the basic requirements for my normal daily use is sorted without any drama at all. It’s the ancient version 2.1 though, so it was off to gimp.org for the latest version. Installation was the same as any Windows install back in the day, there’s no locking down here.

Crapware’s a Bit Different

So I’ve got a Daily Driver, what are my impressions. After so long away and having missed the debacle of Windows 8’s Metro interface, I think the desktop interface is actually pretty good. It’s kept up with the times in a way macOS — with its barmy top-corner menus which just don’t work in a world of 4K screens — hasn’t. As to the commercial aspects of the OS, I was expecting it to ask me for a Microsoft account and it hasn’t, so that’s a plus. But the thing I had forgotten about was the ubiquity of nag screens. I haven’t had to click a “No, I don’t want to upgrade to your premium version” button in a very long time, and here I am suddenly having all manner of software wanting my attention. No Adobe Acrobat, I don’t want to give you any money! And then there’s the AI. Nothing in my Linux install is trying to offer me AI services, but it seems everything is here.

My jaunt into Windows land will be over when I’ve finished writing this piece, and I guess it’ll be as long again before I revisit this partition. Updating it took nearly three hours, and it’s constantly nagging me for paid upgrades, offering me news stories from sources I don’t like, and trying to push AI services on me. But is it a walled garden of crapware? That’s a more difficult question to answer. I’ve not had to enter a Microsoft account to use it, and I can install the software I want, so it’s not become the walled garden its detractors will tell you it has. The crapware though? Less clear cut.

This is a reseller laptop, so at least in theory, its original drive should have been wiped or even destroyed as part of a corporate data security scheme. So the reseller puts a cheap drive in and gives it a basic Windows install. It’s completely vanilla Windows 11, which is where it differs from many new laptops. There is no bundled software, no nagware, no commercial anti-virus, and no dubious-value security package. It’s as clean as Windows gets, but even so, there’s still too many features being pushed on me that I simply don’t want. It may not have old-style crapware installed, but the crap is still there.

So my final impression? This trip into Windows-land has been interesting, and I’ve found an OS better than I expected. But it’s reminded me again of the reasons why I moved on from dual-booting Windows XP all those years ago, with a lingering feeling that I still don’t quite own it.

Windows 11 then, it’s a daily driver for millions of people, but I still won’t be one of them.

hackaday.com/2026/06/02/jennys…

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.

securelist.com/wardriving-asse…

Photogrammetry is the process of 3D scanning an object by taking a lot of photographs, then using software to turn those into a 3D model. But the process can only scan what the camera can see, and one can’t always get a good view of every part of an object. To solve this, [Thomas Megel] shared an experiment in using a mirror to capture the underside of an object simultaneously with its top. The results were encouraging!
Using a mirror as the turntable allows the camera to image the underside at the same time.
To do this he perched a small tabletop gaming mini on a mirror serving as a turntable platform in his self-designed OpenScan Mini machine, which is designed to take highly structured photos of small objects for scanning purposes. This produced a single scan with two objects, the original and its mirror image, together in one file.

Aligning separate models and combining them into one is a common way to deal with partial or incomplete scans. The idea here is to get two scans at once, instead of separately with a reposition of the object in between. Additionally, it should be possible for the software to automatically separate, align, and combine the two since it is known exactly where the mirror plane is.

As far as a proof of concept, it’s encouraging. [Thomas] is still playing with the idea and looking for suggestions, so if you have any insights be sure to share them.

3D scanning can be a very useful tool, and while photogrammetry can be done with little more than your mobile phone’s camera, in some ways the concept is over a hundred years old.

hackaday.com/2026/06/02/using-…