Welcome back, aspiring cyberwarriors!

Lately, we’ve covered several tools you can use with your laptop to track nearby devices and people. While they’re useful, their effectiveness depends on the strength of your Bluetooth adapter, and, of course, you need to have your laptop with you.

This time, we’re doing things differently. We want to show you a device that can automatically monitor nearby devices for extended periods, anywhere you choose to place it, and as often as you want. It doesn’t rely solely on Bluetooth, as it also uses Wi-Fi, which is far more likely to be enabled, increasing the chances of detecting someone in your area.

What is Paxcounter

Paxcounter is an open-source firmware project that takes a cheap little ESP32 development board and turns it into a sensor that can count people. Almost every smartphone in the world is constantly sending out small Wi-Fi signals, called probe requests, and Bluetooth signals too, even when the phone is not connected to anything. Paxcounter listens for these signals in the air. It counts how many different devices it hears during each scan, and from that, it can tell you a real time estimate of how many people are nearby.

The project started out as a simple way to measure how many passengers or pedestrians pass through a certain spot. But over time, it grew into something much bigger. Now it works as a general purpose IoT platform, built on hardware that usually costs somewhere between $10 and $30. Besides its main job of counting Wi-Fi and Bluetooth devices, a Paxcounter can also read environmental sensors, track its GPS position, keep accurate time, and send all of that data out through LoRaWAN, MQTT, a local serial connection, or straight onto an SD card. 

How the Counting Works

The way Paxcounter counts people is simple, but it was clearly built with privacy in mind from the very start. Every scan cycle, which lasts 60 seconds by default, the device switches its Wi-Fi and Bluetooth radios into scanning mode and listens for probe requests and advertisement packets coming from nearby devices. Each of these packets carries a MAC address. Paxcounter takes just the last two bytes of that address and turns them into a short, temporary ID. This ID is only used to check for duplicates during that one scan cycle. Once the cycle ends, the count of unique IDs gets sent out, and the whole list is wiped from memory. The firmware also does not try to fingerprint any device. It never tries to figure out a phone’s brand, its operating system, or who owns it. All it wants to know is whether that device has already been counted in the current window.

This scan and clear cycle just keeps repeating, either nonstop or on a schedule if deep sleep power saving is turned on. The results, which include the Wi-Fi count, the Bluetooth count, and sometimes live sensor readings too, get packed into a small payload and sent out through whatever channel the device is set up to use. One thing worth knowing is that Wi-Fi and Bluetooth scanning actually share the same 2.4 GHz radio hardware on the ESP32. So running both scans at the same time slightly lowers the accuracy of each one. Because of that, the project’s own advice is to split Wi-Fi only counting and Bluetooth only counting across two separate devices whenever the best possible accuracy is needed for both.

One Firmware, Many Boards

Paxcounter comes with a hardware abstraction layer and its own pin mapping files for dozens of ESP32 and ESP32-S3 boards. These come from well known manufacturers like LILYGO and TTGO, Heltec, Pycom, WeMos, M5Stack, and Adafruit, and there is also a generic template ready for boards that are not officially supported yet. LILYGO even sells a ready-made board called Paxcounter LoRa, built specifically to run this firmware. 

Depending on which board you pick, your device can end up supporting a LoRaWAN radio for sending data over long distances while using very little power, an OLED status screen, or a single color, RGB, or larger LED matrix light to show status. It can also support a physical button for flipping through display pages or sending an alarm message, battery voltage monitoring, GPS positioning, a real time clock chip along with IF482 or DCF77 time telegram output, and even an SD card slot for logging data locally when there is no network around.

Because the whole system was designed to be truly portable, the documentation goes into real detail about power draw, which usually sits somewhere between 450 and 1000 milliwatts depending on how the device is set up. It also makes good use of the ESP32’s deep sleep mode, so a device can keep running for a long stretch of time on just one 18650 lithium ion battery cell. Members of the community have already shared several 3D printable enclosure designs on Thingiverse for the more popular boards.

Getting the Device Up and Running

Paxcounter is built using PlatformIO instead of the plain Arduino IDE. This choice lets it work smoothly with editors like Visual Studio Code, Atom, or Eclipse, and it gives the project reproducible, script driven builds. In fact, the repository runs an automated PlatformIO build check every single time the code changes, using GitHub Actions, and there is even a CodeFactor badge that keeps an eye on ongoing code quality.

The configuration is intentionally spread across a handful of different files instead of being crammed into just one. This keeps board specific settings, behavioral settings, and personal settings nicely separated from each other. The platformio.ini file is where you select which board’s hardware profile you want to compile against. The paxcounter.conf file handles behavioral settings, things like how long a scan cycle lasts, sleep timing, and payload options. The shared lmic_config.h file sets the LoRaWAN region and frequency plan, so it matches the rules where you live. The shared loraconf.h file holds the device’s LoRaWAN join credentials, and the project recommends using OTAA rather than ABP for this. And the shared ota.conf file stores the Wi-Fi credentials the device uses for over the air firmware updates.

You can upload firmware the traditional way, over USB, or once a device has joined a LoRaWAN network, you can push updates over the air instead. A remote command tells the board to connect to Wi-Fi, check a hosted repository called PAX.express for a newer build, and then download and flash it automatically. If anything goes wrong during that process, it will roll back to the previous version on its own. Devices can also be set up to open a small local web based bootstrap menu right when they power on, which lets you upload a firmware file manually, even from a phone in tethering mode, without needing PlatformIO installed on site.

Configuration and Extensibility

Beyond just picking a board, Paxcounter gives you a long list of settings you can tune to fit your needs. It can log environmental data from sensors like the Bosch BMP180, BME280, BMP280, or BME680, read a Nova SDS011 particulate matter sensor to track dust in the air, and keep accurate time using either a DS3231 real time clock or a connected GPS module. 

Display and LED

On boards that come with an OLED display, Paxcounter shows live status information you can cycle through with a short press of the button. This includes the current pax count, meaning the people count, a histogram of recent activity, GPS status, environmental sensor readings, and the time of day. 

A long press of that same button sends an alarm message out over the network instead, which is a simple way to flag a problem from out in the field without needing any other kind of interface. Even on boards that do not have a display at all, a status LED still tells you what the device is doing through its blink pattern. You get a brief flash whenever a new Wi-Fi or Bluetooth device is spotted, a quick blink while the device is joining the LoRaWAN network, a short blink during data transmission, and a slow, long blink if there is a LoRaWAN stack error. Boards that have an RGB LED get a color coded version of these same signals.

How You Receive the Data

Once a Paxcounter has counted the people nearby and packed everything into a message, that data has to go somewhere so you can actually see it. How that happens depends on which output the device is using, and the good news is you can turn on more than one at the same time. If you are using LoRaWAN, which is the most common setup, the device does not send the data straight to you. Instead, a nearby LoRaWAN gateway picks up the signal first and forwards it on to a network server, usually The Things Stack. There is a small decoder script included with the project, and its job is to take that raw message and turn it into numbers you can actually read, something like a pax count of 14. From there, The Things Stack can pass the data along to your own app or dashboard using MQTT or a webhook, or you can simply watch it come in live through the built in console.

If a board does not have LoRa hardware built in, it can just skip the gateway completely and send that same kind of data straight to an MQTT service over Wi-Fi instead. You can also connect the device to a computer using a USB cable and read the numbers directly from a serial connection. This is a simple way to test things out without needing to set up a network at all. If SD card logging is turned on, everything also gets saved locally as a CSV file, so you can pull the card out later and open it up in a spreadsheet. This comes in handy in places where there is no network coverage to rely on. 

Where It’s Used

Because a single Paxcounter device is cheap to build and can be left running unattended for a long time, you will find it popping up in a pretty wide range of places. Retailers and shopping centers use it to measure foot traffic without needing to install cameras. Event organizers use it to watch how crowds move around a venue in real time. Pentesters can get a passive read on how many Wi-Fi and Bluetooth devices are active in a building, or to notice unexpected devices showing up where they shouldn’t, all without needing camera access or network credentials.

Legal and Privacy Considerations

Since Paxcounter’s whole job involves listening to wireless traffic, its documentation is unusually upfront about the legal side of things. It points out that sniffing Wi-Fi and Bluetooth MAC addresses may be regulated or restricted depending on where you live, and it links to specific starting references for the US, the UK, the Netherlands and the EU, and Germany. It also makes clear that the legal responsibility for how a device is built and deployed falls on the person doing it, especially for public deployments where the results might get published somewhere. On the technical side of privacy, the project’s own design actually holds up pretty well against that legal backdrop. Identifiers are only ever built from the last two bytes of a scanned MAC address, they are kept in memory just for the length of one scan cycle, and then they are discarded completely. No MAC addresses or identifiers are ever sent out over the network, and the firmware does not do any extra tracking or fingerprinting of the devices it scans.

Summary

What really makes Paxcounter stand out is not any single feature on its own. It is the whole combination working together. One piece of open source firmware supports dozens of cheap boards, runs for a long time on a small battery, counts people without saving anything identifying about them, doubles as a general environmental sensor node, speaks LoRaWAN, MQTT, serial, and SD card all at once, and can be fully reconfigured from a distance once it is out in the field. The full source code, the complete board list, and all the documentation are available on GitHub.

If you enjoy experimenting with frequencies and trying new things, we recommend signing up for our SDR for Hackers training. With Master OTW, you’ll learn how to use your computer and inexpensive SDR hardware to explore and hack a wide range of radio signals.

The post SDR (Signals Intelligence) for Hackers: Tracking People with ESP32-Paxcounter first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Let’s talk about something most people never think about. When the news reports on a cyberattack against a big retail chain, the story usually sounds the same. A database got leaked or ransomware locked up the company’s files. These are real threats, and they deserve attention. But what happens if a hacker skips all of that and simply walks into a physical store with a laptop tucked in a backpack? No malware sent through email and no phishing link, just being there physically.

In this article, we are going to build a picture, drawn from several real walkthroughs of ordinary retail stores, all pointed toward one goal. We want to see the store the way a pentester sees it.

A Hacker in the Supermarket

Imagine someone stepping through the front doors with that mindset. Within a few minutes of walking the floor, a handful of things stand out.

There are the transformer checkout terminals and the self service kiosks, the modern face of retail, and also a possible weak point. There are staff call buttons mounted near the aisles, small radio transmitters that broadcast a fixed code each time someone presses them, a code that could potentially be captured and played back later. There are wireless DECT handsets still in use on some sales floors, the same cordless phone technology many offices have relied on for years. There are data collection terminals, plain Android devices that sometimes carry no password protection at all, with access to the store’s Wi-Fi settings. And running along the floor and behind the counters, there are network cables, which in the wrong circumstances could let anyone plug in and reach the store’s internal network.

Day 1 – Becoming an Insider

Many corporations believe their internal network is sealed off from the outside world, safe behind firewalls and passwords. That sense of safety can end at the first unlabeled cable lying loose on the floor.

Someone can walk up to a transformer checkout terminal,  unplug its network cable, plug in a laptop instead (or better yet, one of those devices we showed in previous articles), and type a simple command.

kali > sudo dhclient

That laptop could be handed an IP address from the store’s own internal network. If the network uses a /27 mask, that means an entire segment of the corporate infrastructure could open up right there.

Scanning the network might take only a couple more minutes, and inside, a hacker could find exactly what you would expect from a typical store. There could be the store manager’s workstation, with an open RDP port for remote access. There could be a Wi-Fi router still running its factory default settings. There could be a DECT base station handling internal telephony. There could be surveillance cameras, other registers and terminals, and tucked away in shared folders and configuration files, credentials and passwords saved in plaintext.

From there, someone could try connecting to the manager’s computer. If the RDP client offers a choice of accounts, and one of those accounts, say one named operator, needs no password at all, that should raise a flag. Normally Windows blocks RDP logins for accounts with blank passwords, so a setup like that means someone deliberately switched that protection off, likely to keep an easy access route open for themselves. Sysadmins often do it. But that’s a backdoor. We often see the same issue with VNC. That route could lead to the remote desktop of an employee with access to corporate email, internal messenger conversations, financial documents, work schedules, and delivery data.

And since Chrome is installed on nearly every computer in sight, opening Passwords could show saved logins for internal services, everything from the CRM system to the warehouse management software, sitting there in plain view.

How to Fix It

Passwordless accounts feel almost like a relic from an earlier era, yet they still turn up in retail environments from time to time. Alongside them, flat, unsegmented networks are common, where cameras, workstations, and Wi-Fi routers all sit together on the same segment. Add to that the simple physical accessibility of the equipment. Network cables, ports, and switches are often placed exactly where any employee, or any visitor, could reach them without much trouble.

Segment the network properly, giving separate VLANs to registers, service equipment, and employee workstations, so a breach in one area does not open a door to everything else. Restrict which devices are even allowed to connect through RDP in the first place. Turn on MAC address whitelisting along with Port Security, so an unknown device cannot simply be plugged into an open port and join the network. Require real passwords on every local account, without exception. Disable browser based password storage for anything tied to internal systems.

And finally, ask security staff to keep a closer eye on the registers themselves.

Day 2 – Telephone Game

Consider a small, easy to overlook detail, a staff call button tucked into a corner near an aisle. Pressed once, it sends a chime ringing across the store, and a salesperson comes over a moment later. Simple enough, on the surface.

Except with a HackRF One someone could intercept and record the exact signal the button sends the moment it is pressed. If that button broadcasts the same static signal every time, with no protection against replay, then anyone who plays that recorded signal back over the air could trigger the same chime, without ever touching the actual button. This is what we call a replay attack, and it remains a real possibility even now.

Once that chime lives on someone’s laptop, a single click could ring it out across the entire store. Employees might rush toward the sound, leaving a register briefly unattended, while someone else nearby has a short window to act.

The same HackRF One, paired with an open source tool called gr dect2, could also be used to listen to the surrounding airwaves. If a store still relies on wireless DECT handsets for internal communication, a call placed from one handset to another could, in principle, be intercepted and decrypted in real time as it travels through the air. From that point, anyone listening could pick up delivery schedules, work rosters, and conversations about register problems, all carried over employees’ DECT handsets.

Older pentest reports sometimes describe this kind of attack as only medium risk, mostly because of the cost of the equipment and the technical skill it supposedly requires. It’s different now. An original HackRF One costs somewhere around three hundred dollars, and less expensive clones can be found on online marketplaces for a fraction of that price. And gr dect2 makes the whole process more accessible, since it is an openly documented, freely available project.

How to Fix It

The fixes here lean more organizational than technical. It makes sense to retire primitive call buttons in favor of systems that use dynamic, constantly changing codes instead of a single static signal. Alongside that, replacing outdated DECT telephony with modern VoIP or straightforward wired communication removes much of this risk entirely.

Day 3 – Corporate Wi-Fi

What about the Wi-Fi? On paper, it can look genuinely solid, not a simple router with a shared password, but full WPA-Enterprise authentication requiring a proper login and password from each user. That sounds like a real obstacle, and in many ways it is. But it does not fully close the door. Someone could set up a rogue access point using the exact same network name as the legitimate one. If an employee’s device, whether a work tablet or a personal smartphone, tries to reconnect automatically, it might see two access points broadcasting the identical name and simply pick whichever one offers the stronger signal and the faster response. A rogue access point built for this purpose could easily be tuned to answer faster than the real one. Once a device connects to that convincing twin, it attempts to authenticate as usual, and in doing so, it sends its credentials straight into someone else’s logs.

How to Fix It

Setting up EAP TLS with proper certificate validation on every client device helps ensure a fake network cannot simply mimic its way into a successful login. Monitoring the surrounding radio spectrum regularly is also worthwhile. Even simple, freely available tools can detect unauthorized access points broadcasting names that match or closely resemble the real corporate network. And training staff matters. If a Wi-Fi password is unexpectedly requested a second time, or a connection seems to take suspiciously long, employees should feel comfortable reporting it to security or the IT security team right away.

Day 4 – Transformer Register and Cash Drawer

A transformer register is really a combined hardware and software unit, built around a metal cash drawer, both stationary and handheld barcode scanners, and a receipt printer. Along its bottom panel often sits a row of unprotected USB ports. Plugging in an ordinary keyboard there opens the door to some experimentation.

Pressing Ctrl Alt and one of the function keys from F1 through F5 can switch the screen to a text console, prompting for a login and password. Full system access could sit right there within reach. Even if the Alt F2 shortcut for quickly launching commands has been disabled, the multi user Linux console underneath may remain fully accessible regardless.

Power cycling the device and pressing Delete could open the BIOS. Without a boot password protecting it, the machine could be booted from an outside USB drive, handing over full control of the system, along with the ability to change settings or install unwanted software.

The most interesting risk, though, waits underneath the register itself. The metal cash drawer typically has a mechanical emergency release button on its underside. If the drawer has not been locked with a physical key, which happens more often than store staff would like to admit, then any customer could simply lean down, press that button, and slide the cash right out.

No discussion of registers is complete without mentioning their close relatives, the self checkout kiosks. These are essentially the same transformer registers, just packaged in a form factor that happens to be even more exposed. USB ports, network ports, and power ports often sit within easy reach. The real difference is that a transformer register might occasionally be watched by a nearby salesperson, while a self checkout kiosk usually sits alone in a corner, without much oversight at all.

Standing casually near a kiosk for just a few minutes could be enough to observe an employee entering their access code. From there, that access could open up the kiosk’s full functionality, including the ability to ring up items, process returns, and open that same metal cash drawer hiding underneath.

How to Fix It

The solution here is fairly clear once the problem is understood. Restricting physical access to the register hardware itself, through USB port blockers, closed enclosures, and sealed covers, prevents outside devices from being connected in the first place. A BIOS password combined with disabling boot from removable media protects against attempts to seize control of the system through a flash drive.

Employee authorization deserves attention too. Since the register already comes equipped with a barcode scanner, a smart approach is issuing personal ID badges with the employee’s password encoded directly into the barcode. The employee scans their badge, the system authenticates them instantly, and the actual password stays hidden from anyone watching nearby. Leaving the alphanumeric combination off the badge entirely prevents it from being typed in manually as a way to bypass the scanner.

And of course, the lock on the cash drawer matters. If it is even possible to leave that drawer unlocked, sooner or later it probably will be. Drawers that lock automatically, without relying on a person remembering to do it, offer a much more reliable solution.

Day 5 – Refund

Consider someone playing the role of an ordinary, everyday customer. They buy a small item in the store, pay with a card, and walk away with a receipt like anyone else. Once a self checkout kiosk sits idle for a moment, tapping the top left corner of the screen could open a hidden staff menu.

The system would ask for authorization. If someone types in a password they had observed a cashier enter earlier, often a simple employee ID number, that alone could be enough to land inside the cashier menu. 

From there, selecting a refund by sales receipt option could display a list of recent transactions, including the very purchase just made. A further step worth testing is whether the refund could be redirected, not back to the same card used to pay, but to a completely different one, belonging to someone else entirely. You might expect the terminal to block an operation like that, or at least demand confirmation from a senior employee before proceeding. In some systems, neither of those things happens, and an ordinary cashier’s password turns out to be enough to redirect the funds elsewhere.

To its credit, a system like this may honestly display a warning that the money will be sent to a different card than the one used for payment. But it can carry out the operation anyway, without further checks.

The item would stay with the customer, the original purchase would turn into a refund on paper, and the store’s money would end up in someone else’s account. One more detail worth checking is whether the refund function has any built in time limits. Many places only allow refunds within a set window, say fourteen days, in line with consumer protection law. But in some systems, attempting to process a refund for a purchase made several months earlier goes through without any resistance at all.

This points to a deeper gap in business logic and access control. The authorization threshold can sit far too low, since a rank and file salesperson’s password may be enough to trigger a real financial operation, and that password is often easy to observe over someone’s shoulder. There may be no check to confirm the refund card actually matches the original payment card. A refund landing on a different card is not automatically suspicious on its own, since many banks and retail chains support this for customer convenience. But operations like that should require sign off from the store manager, a financially liable employee, or someone else holding proper authority. And finally, there may be no meaningful time or amount limits at all, meaning refunds could remain possible over an unlimited stretch of time, and theoretically for an unlimited amount, up to whatever balance the register happens to hold.

How to Fix It

Two tier authorization is genuinely useful here, paired with a strict time window governing refunds. Automatic refunds could be limited to the last fourteen days, with anything older switching over to manual processing, complete with multi level review and documented sign off.

Tying the refund card to the original payment card by default, as a standing rule, closes much of this gap. Cash refunds, or refunds sent to a different card, should remain the exception rather than the norm, strictly regulated and logged separately from everything else.

A dedicated audit log for every refund operation, tied clearly to the cashier’s ID, the receipt number, and the recipient card, makes it possible to review the whole trail later if something looks off.

Summary

Nothing here requires exotic tools or rare expertise. The overall picture is worth taking seriously, because a store is never just a building full of shelves and registers. It functions as a branch of the corporate infrastructure itself, a set of trusted interfaces placed out into public space, right in front of every customer who walks through the door.

But these small, easy to overlook pieces can chain together. Network access can lead to credentials, credentials can lead to internal systems, internal systems can lead to operational data, and operational data can eventually lead to real financial consequences. A useful security assessment in an environment like this does not simply end with a recommendation to close a port and set a stronger password. It ends with a more useful question worth asking. Who decided, at some point along the way, that all of these things should sit within the customer’s reach in the first place?

If you enjoy hacking and would like to get started in cybersecurity, we have created the Cybersecurity Starter Bundle II to equip you with the knowledge and skills needed to begin your journey. If you want to advance your skills even further, our Cyberwarrior Path is made to help you delve deeply into the technology and show you how to break it

The post Pentesting: Hacking the Supermarket first appeared on Hackers Arise.

Welcome back, aspiring hackers!

The density of WiFi access points in modern cities has now reached a point where a large-scale surveillance system may be able to identify almost anyone who walks near a router, even if that person is not carrying a mobile phone. Researchers from the Karlsruhe Institute of Technology (KIT) have published a scientific paper describing this kind of system and the technology that makes it possible.

At the center of this surveillance method is a feature called beamforming, which first appeared with the WiFi 5 (802.11ac) standard in 2013–2014. The basic idea was introduced with WiFi 5, but it became much more refined and effective with WiFi 6 (802.11ax), where the technology matured into something more practical.

Beamforming

Beamforming, also called spatial filtering, is a signal processing technique used to send and receive wireless signals in specific directions rather than spreading them evenly in every direction. In simple marketing language, this is often described as a router that “does not broadcast equally everywhere anymore, but instead follows the user with a focused beam.” That description is not wrong, but it leaves out the technical depth behind the idea.

From an engineering point of view, beamforming works by combining several antennas into a group called an array. When the signals from these antennas are timed and lined up correctly, they boost each other in certain directions. In other directions, they cancel each other out. The result is a signal that is far more focused and efficient than older systems, which simply broadcast outward in every direction at once.

Beamforming gives both senders and receivers the ability to focus on signals coming from one direction while blocking out noise from others. Because of that, the technique is used not only in WiFi, but also in radar, sonar, seismology, wireless communications, radio astronomy, acoustics, and biomedical engineering.

Identifying People Through WiFi Signals

As radio waves move through space, they do not simply travel in a straight, clean line. They interact with the world around them in many different ways. They can pass through objects, reflect off surfaces, become absorbed, become polarized, bend around obstacles, scatter in different directions, or refract as they cross boundaries between materials. This means that when a WiFi system sends a signal and later receives it back, the final result contains information about everything the signal encountered along the way. By comparing the expected signal with the received one, it becomes possible to measure interference and use that information to correct transmission errors. But that same interference also reveals details about the environment itself.

For example, when a person enters the path of a WiFi signal, the signal changes. Human bodies affect radio waves in measurable ways. The signal may weaken, shift, scatter, or behave differently depending on movement, posture, and position. If researchers analyze these changes carefully, they can infer a surprising amount of information about the surrounding environment. They may detect whether people are present, what they are doing, and in some cases even who they are.

This whole research area has grown into a separate field known as WiFi Sensing.

Most WiFi Sensing research is presented as useful and harmless, and in many cases it really is. It can support smart-home features, occupancy detection and other practical applications. But the privacy concerns are obvious. When these methods are combined with activity recognition and the massive spread of WiFi hotspots, they can reveal highly sensitive information. One of the most troubling possibilities is that someone could be identified in the range of a hotspot and then tracked over time without ever knowing it.

Using Channel Information for Identification

There are several ways a person can be identified through WiFi. One important method relies on analysis of Channel State Information (CSI), which is sent at the physical layer of WiFi communication. CSI is detailed and useful for WiFi sensing. It gives a rich picture of how the wireless channel behaves. The problem is that CSI is not always easy to access. In many cases, it requires modified firmware and specialized hardware support, which limits how widely it can be used in practice.

The table above compares roughly 25 different systems, evaluating them across several key dimensions. The Paper column lists the name of each system, while the Identities column shows how many different people each system is capable of distinguishing between. The Accuracy column then reflects how reliably each system correctly identifies a person. On the technical side, the Pre-Processing column describes the signal processing techniques each system applies to clean and transform raw WiFi data before passing it to a machine learning model, and the Model Architecture column identifies what type of model is used. The Perspective column shows how subjects were positioned or moving during data collection, such as standing orthogonally, performing gestures, or typing keystrokes.

Beamforming entered the picture for a different reason. As mentioned earlier, it was introduced in WiFi 5 to improve throughput and make wireless communication more efficient. But beamforming also depends on environmental information that is similar to CSI. The difference is that this information is gathered on the transmitter side rather than the receiver side.

The key new dimensions here are the Inference column, showing the wide variety of tasks these systems tackle, from respiratory rate monitoring and crowd counting to sign language recognition.

In a typical beamforming setup, client devices send something called Beamforming Feedback Information (BFI) back to the access point. BFI is a condensed snapshot of current signal conditions. It tells the access point how the wireless channel looks so that it can adjust its transmission for better performance.

The key difference between CSI and BFI is that BFI is transmitted back to the access point without encryption. This makes it much easier to collect using standard, off-the-shelf hardware, without needing any special software modifications. That significantly lowers the bar for potential misuse. The privacy concern gets even more serious when you consider that the IEEE is already working on making WiFi sensing an official standard through the upcoming 802.11bf update and based on the current draft, without putting strong privacy protections in place.

KIT Researchers Demonstrate Phone-Free Identification

Researchers at KIT showed that people can be identified using only BFI data, even when they are not carrying a smartphone or any other wireless device. The method does not depend on a person bringing along a tracked gadget. It works using ordinary WiFi devices already present in the environment and already communicating with one another.

As radio waves move through space and interact with the human body, they create patterns that can be captured, analyzed, and compared. In that sense, the process starts to resemble imaging, almost as if the wireless system were building a rough picture of a scene without using a camera. The result is not a photograph in the normal sense, but the data can carry enough structure to support identity inference.

WiFi Routers as Silent Observers

“The technology turns every router into a potential surveillance device,” says Julian Todt, one of the study’s authors. “If you regularly walk past a café that has a WiFi network, you could be identified without your knowledge and later recognized by government agencies or commercial companies.”

That is a serious warning, and it captures the core concern very well. Intelligence services and cybercriminals already have many easier ways to monitor people, including compromising CCTV systems or intercepting video communications. But wireless networks are different. They create a nearly invisible surveillance layer that already exists in a huge number of places.

Unlike earlier approaches that depended on LiDAR sensors or on reflection-based systems using walls, furniture, and human bodies, this method works with standard WiFi equipment. By collecting BFI data, researchers can build representations of people from several different viewing angles. These representations are then used to distinguish one person from another, even when the number of people is large. Once the machine learning model has been trained, the identification process can happen in just a few seconds.

Experimental Results

The study involved 197 participants. The researchers reported that they were able to identify individuals with nearly 100% accuracy, regardless of viewing angle or walking style. That is an impressive result, but it did not come easily. To reach that level of accuracy, the model needed a substantial amount of machine learning training. Each person in the training set performed around 20 walking passes before the model was trained.

During the research two TP-Link Archer BE800 routers were used. The experiment relied on channels 37 and 85. It also used two non-overlapping 160 MHz channels in the 6 GHz band available under WiFi 6E. The hardware included Intel AX210 WiFi network adapters.

The researchers stress that the technology is powerful, but also potentially dangerous. The risks are especially serious in authoritarian states, where systems like this could be used for large-scale population surveillance. In such settings, the ability to identify people without their phones, without cameras and without obvious visible monitoring would be a major privacy threat.

For that reason, the authors strongly recommend that privacy protections and security safeguards be built into the upcoming IEEE 802.11bf standard from the start, rather than added later as an afterthought.

WiFi 6 Routers as Motion Sensors

In fact, WiFi-based sensing has become so effective that some modern routers already include motion-detection features right out of the box, and manufacturers openly advertise them.

Features such as WiFi Motion Detection allow homeowners to monitor activity inside their homes through mobile apps, using nothing more than changes in WiFi signal patterns.

A feature designed for convenience in a home can also become part of a much broader surveillance system when deployed at scale.

Related WiFi and Bluetooth Scanning Tools

As an additional note, several tools already exist that monitor wireless activity in nearby environments. They don’t work exactly the same way as the techniques we covered earlier, but they’re still useful.

Pi.Alert scans devices connected to a WiFi network, detects unknown devices, and sends notifications when devices unexpectedly disconnect from the network. It is often used as a practical awareness tool for keeping track of what is present on a home or local network.

WireTapper discovers nearby wireless signals, including WiFi networks, Bluetooth devices, hidden cameras, vehicles, headphones, televisions, and cellular towers. It gives the user a broader view of the wireless environment around them, which can be useful for awareness and inspection.

Video

We also have an video on this topic with Master OTW and Yaniv Hoffman. In the video, OTW explains how hackers can use SDR, AI, and Wi-Fi signals to detect human movement through walls, how the technology works, and talk about practical ways to defend against it. Feel free to check it out.

Summary

As modern routers gain advanced sensing, they can also become tools for observing and identifying people through the way their bodies interact with wireless signals. The KIT research shows that this is a practical technology that can identify individuals with remarkable accuracy using ordinary WiFi hardware. Although WiFi sensing can be valuable for smart homes and automation, it also raises serious privacy concerns. Privacy protections will need to become just as important as performance improvements.

If you’re interested in Wi-Fi security, our Wi-Fi Hacking training can help you gain the necessary experience. This attack vector is often underestimated, and many organizations are vulnerable to it. It is definitely valuable in penetration testing.

The post Wi-Fi Hacking: Wi-Fi Can Now Identify You Without Your Phone first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

Open Source Intelligence, or OSINT, is a powerful yet underrated cybersecurity discipline. A solid reconnaissance phase uncovers vast target information before active probing even begins.

Traditionally, effective OSINT was a painful process. It meant juggling fragmented tools and manually parsing disjointed data to map a target. AI can completely automate this workflow. It calls the right tools in order and explains its analytical reasoning.

This article explores OpenOSINT, an automated intelligence agent built for security researchers. We will install the framework and test its core capabilities using real commands. Let’s get rolling!

What is OpenOSINT?

OpenOSINT is an artificial intelligence assistant designed to automatically gather public information and conduct digital investigations. To interact with OpenOSINT, you can choose from four different methods depending on your comfort level with computers. Beginners will likely prefer the web interface. For those who prefer working with text, the tool offers an interactive terminal. There is also a direct command line interface for running quick commands. Finally, it can act as a background server using MCP that plugs directly into other AI applications, like Claude Desktop.

The way this AI works makes it super reliable and gets rid of that annoying problem of AI making stuff up. When we ask OpenOSINT to do something, it connects to an AI engine. If the AI realizes it needs to look something up, it totally pauses and waits. Your computer then boots into tools, runs the actual program it needs, gathers the right info, and sends that back to the AI. Because the model has to wait for real results instead of just guessing, it can’t fake or invent any findings (or at least will do that less often).

If OSINT is a skill you want to develop seriously, we strongly encourage you to check out the dedicated OSINT series. It covers passive and active reconnaissance techniques, social media intelligence, geolocation, and much more, all taught by practitioners who do this work professionally.

Step 1: Installing OpenOSINT

Let’s clone and install the framework.

kali > git clone https://github.com/OpenOSINT/OpenOSINT.git

kali > cd OpenOSINT

From the observation of the source file, we can confirm that it’s a Python project. Our next step should be the installation of dependencies. Before that, it’s wise to create a dedicated virtual environment.

kali > pip install -e .

The command above uses the -e flag to install the package in editable mode. This means that any changes we make to the source files will be reflected immediately without the need to reinstall the package.

Next, we will install the external dependencies for OpenOSINT. Each dependency supports a specific investigation module, and the framework will still function properly even if some dependencies are missing. Holehe is used for email account enumeration, while Sherlock conducts username enumeration across more than 300 platforms. Sublist3r is utilized for subdomain enumeration. Installation is simple and can be done easily using pip.

kali > pip install holehe

kali > pip install sherlock-project

kali > pip install sublist3r

Another important tool for OpenOSINT is PhoneInfoga. It’s distributed as a standalone binary rather than a Python package, so download the latest release for your architecture from its GitHub releases page.

kali > tar -xzf phoneinfoga_Linux_x86_64.tar.gz

kali > sudo mv phoneinfoga /usr/local/bin/

Step 2: Configuring API Keys

OpenOSINT becomes significantly more effective when supplemented with additional information from external tools like VirusTotal. This integration allows OpenOSINT to gain a clearer understanding of the situation and ultimately make better decisions.

To configure the API keys, we need to open the .env file:

OpenOSINT supports a wide range of services. In this instance, I will specify my Anthropic API, VirusTotal, and Censys.

Step 3: Testing OpenOSINT Web Mode

OpenOSINT offers several ways to use its features. One of them is the AI REPL (Read-Eval-Print Loop), which is a simple, interactive programming environment that accepts user inputs, executes them, and returns the results. Another option is through the web interface, which I will use. There is also a direct tool usage method, but since it does not involve AI, I will not focus on it here, as we can use those tools directly without OpenOSINT.

To start the OpenOSINT web server, use the following command:

kali> openosint web

This command will open a webpage that looks like the one shown below.

Let’s start with a username investigation.

First, the system initiated a broad tool-based check across numerous platforms, returning raw data showing specific profile URLs for the username “tomilov” on sites ranging from Audiojungle and Behance to Codeforces and Discord, indicating presence on creative, social, and gaming platforms.

Finally, it synthesizes these findings into points, such as the subject’s probable Russian origin and their clear professional background. Essentially, the tool executed a digital footprinting task and transformed the results into categorized intelligence.

Next, let’s investigate a domain.

As shown in the screenshot above, the tool initiated its workflow by pulling WHOIS registration data, which fetched the registrar, the creation date from 2009, and the owning organization, LLC KUPISHUZ. At the same time, it performed a subdomain enumeration scan to map out associated hosts and queried VirusTotal to check the domain’s reputation against multiple security vendors.

The system then processed this raw data into a structured intelligence report. It verified that the domain possesses a long-standing, 14-plus year history with no malicious detections on VirusTotal. It also classified the discovered subdomains into operational roles, identifying specific functions such as a main website, a careers portal, internal networking infrastructure, an order tracking portal, and a Single Sign-On authentication platform.

Now, let’s test the email module.

This tool initiated its workflow by running an email enumeration scan using a utility called Holehe, checking the address across 121 websites. This initial check successfully verified active account registrations on Amazon, Flickr, Gravatar, Office365, and Twitter. During this phase, the tool also attempted to query breach data, paste sites, and search engine footprints, but these background tasks failed.

Following the raw scans, the tool generated a series of pre-configured Google dork links targeted at finding the email or username on LinkedIn, Twitter, and Facebook. The system then compiled these results into an Investigation Summary, detailing the discovered platforms and extracting specific metadata, such as the full name and profile URL linked to the target’s Gravatar account.

Finally, the tool outlined a set of next steps for a manual investigator.

Now, let’s run automated open-source intelligence investigation on the specific IP address.

The single successful data extraction came exclusively from the VirusTotal module. This integration successfully returned infrastructure information revealing that the IP address belongs to a network block managed by Senko Digital LLC, which operates as a hosting or virtual private server provider located in Germany. More importantly, the tool retrieved threat assessment telemetry showing that twelve distinct security engines explicitly flagged this specific IP address as malicious, while one categorized it as suspicious, forty-six marked it clean, and thirty-two left it undetected.

Based entirely on this VirusTotal telemetry, the tool synthesized the findings into a security alert status. It concluded that the infrastructure carries a high risk because it is hosted by a VPS provider commonly leveraged for anonymity and possesses a high detection rate across reputable security vendors.

Summary

OSINT remains one of the most high-leverage skills in the cybersecurity field, and tools like OpenOSINT that bring AI-driven automation to the reconnaissance phase will only become more effective and beginner-friendly. If you want to develop your OSINT skills from first principles alongside hands-on tool training, make sure to check out the OSINT Training and join the growing community of intelligence practitioners.

The post Artificial Intelligence (AI) in Cybersecurity, Part 22: Grounding OpenOSINT with Real Tool Execution to Prevent AI Hallucinations first appeared on Hackers Arise.