Welcome back, cyberwarriors!

A while back, we walked you through building your own hacking drone. It was a drone loaded up with tools designed to help you out during an actual pentest. That was a hands-on project in every sense of the word. If you built one, you probably learned a ton just from putting the hardware together.

This time, we’re doing something different. No soldering iron. We’re staying entirely inside your laptop working with the Damn Vulnerable Drone, which is an open-source simulator built for teaching you exactly how drones get hacked, without you ever touching a real drone.

The Damn Vulnerable Drone

The Damn Vulnerable Drone, or DVD, is a training simulator that was made for people who want to learn drone hacking without buying a drone. It recreates an entire drone system in software, including a flight controller, an onboard companion computer, a ground control station and the wireless links connecting them all. Every piece is there, and every piece runs inside Docker containers on a single computer.

The project was built by Nicholas Aleks, a security researcher and co-founder of DEF CON Toronto, and it’s aimed squarely at intermediate-level red teamers and hacking enthusiasts who want to practice with actual drone protocols and architecture. Drone hardware and radios are genuinely expensive, and a mistake on a real flight controller can be costly. You get to make your mistakes safely, over and over, until you actually understand what you’re doing. DVD runs actual ArduPilot firmware as an ordinary program and pairs ArduPilot’s SITL with Gazebo, which is a 3D robotics simulator that supplies realistic physics. Motors spin up, GPS signals drift the way they really do, and the drone actually flies through a rendered 3D world.

Under the Hood

Every Docker container gets its own address on an internal network. That’s a design choice that mirrors how a real drone’s components actually work. The first piece is the Flight Controller, which runs the ArduPilot firmware itself and talks directly to the Gazebo simulator to process virtual sensor data. The second piece is the Companion Computer, which handles Wi-Fi, camera streaming, telemetry logging, and autonomous navigation, and which also exposes its own web interface for you to interact with. The third piece is the Ground Control Station, the pilot’s side of the operation, covering mission planning, mapping, video, and joystick control, all communicating over a simulated wireless MAVLink link. And the fourth piece is the Simulator itself, the Gazebo container that models flight physics behind the scenes. The documentation specifically tells you not to attack this fourth container directly, because doing so can crash the entire lab out from under you. Everything else is fair game. That one, leave alone.

Getting Started

To install it we need to pull down containers. The project offers two configurations based on whether you have a dedicated graphics card.

If the answer is no, you need Lite Mode. It uses a simplified 2D flight model, needs no GPU at all, and runs comfortably on 4 to 8 GB of RAM, 2 CPU cores, and about 100 GB of disk space. It works on Kali Linux or most other Linux distributions, and you can run it either on bare metal or inside a virtual machine. If the answer is yes, Full Mode gives you the complete Gazebo 3D environment, but it asks more of your machine in return. You need 8 to 16 GB of RAM, 2 to 4 CPU cores, 100 GB of disk space, and a GPU with at least 2 GB of VRAM supporting OpenGL 3.0 or newer. Full Mode is Kali Linux only, and it strongly prefers bare metal, though a virtual machine with GPU passthrough will also work.

Kali Linux is the officially supported operating system either way, and both modes need Docker and Docker Compose installed as the only real software dependency you have to worry about. Once Docker is installed, the whole lab comes up with a handful of commands. 

First, if Docker isn’t already on your system, you’ll want to install it:

kali > printf '%s\n' "deb https://download.docker.com/linux/debian bullseye stable" | sudo tee /etc/apt/sources.list.d/docker-ce.list

kali > curl -fsSL https://download.docker.com/linux/debian/gpg | sudo gpg --dearmor -o /etc/apt/trusted.gpg.d/docker-ce-archive-keyring.gpg

kali > sudo apt update -y
kali > sudo apt install docker-ce docker-ce-cli containerd.io -y
kali > sudo systemctl enable docker --now
kali > sudo usermod -aG docker $USER && newgrp docker

Then, clone the repository and pull down the containers. If you’re going with Lite Mode, do this:

kali > git clone https://github.com/nicholasaleks/Damn-Vulnerable-Drone.git && cd Damn-Vulnerable-Drone
kali > docker compose -f docker-compose-lite.yaml pull

From there, three small scripts manage the whole lab’s lifecycle for you:

kali > sudo ./start.sh --mode lite --Wi-Fi wpa2
kali > sudo ./status.sh
kali > sudo ./stop.sh

The start.sh script alone has quite a few options worth knowing about. The –mode full or –mode lite flag picks your simulation type, matching the two modes described above. And the –Wi-Fi wep or –Wi-Fi wpa2 flag is optional, but it’s worth turning on, because it spins up a virtual wireless network alongside everything else. That means your practice can actually include real Wi-Fi attacks as the very first step, instead of starting the exercise with network access already handed to you.

Interface and Feedback

Once everything is up and running, DVD is controlled through a browser-based management console sitting at localhost:8000

This console is really where the whole exercise plays out. A set of buttons trigger five distinct flight states: Initial Boot, Arm & Takeoff, Autopilot Flight, Emergency/Return-to-Land, and Post-Flight Data Processing. Each one simulates a different phase of a drone’s mission and opens up a different attack surface for you to explore. Triggering “Arm & Takeoff,” for instance, actually gets the simulated drone airborne, which gives GPS and navigation-based attacks something real to act on.

That mapping to real flight phases is there for a reason. A drone accepts different commands, and trusts different sources of data, depending on whether it’s sitting idle on the ground, climbing out after takeoff, cruising on autopilot, or executing an emergency fail-safe. That means exercises built around each individual state end up testing different parts of the system. 

The Attack Scenario Library

This is really the heart of the whole project. It has more than 40 named attack scenarios, organized into six categories, each one with its own documentation page and a spoiler-tagged walkthrough waiting behind it. It’s a deliberately broad menu, and it’s worth noticing that some scenarios are about gathering information without being noticed, while others are about actively manipulating or outright breaking the system in front of you.

Reconnaissance scenarios are about passively fingerprinting the drone, its companion computer, and its ground station by watching Wi-Fi and MAVLink traffic go by, without touching anything yet. Protocol Tampering scenarios involve spoofing telemetry values the drone reports, things like its GPS position, battery level, or system status, to see whether the system properly checks what it’s being told. Denial of Service scenarios focus on disrupting flight through methods like Wi-Fi deauthentication or flooding the communication link until it can’t keep up. Injection scenarios involve sending forged commands directly into the MAVLink stream, ranging all the way from a simple waypoint change to a full companion-computer takeover. Exfiltration scenarios are about pulling data off the drone entirely, whether that’s flight logs, mission plans, or content from the camera feed. And Firmware Attacks focus on modifying or reverse-engineering the ArduPilot firmware itself, right down at the code level.

Because every scenario runs against fully simulated components, you actually get to see the complete effect of an attack play out. A spoofed GPS reading really does nudge the simulated flight path off course. A flooded communication link really does degrade control, right in front of you. And you get to watch all of it happen without any of the legal or physical risk that would come with testing the same techniques on live hardware.

Wi-Fi and Non-Wi-Fi Modes

DVD can be deployed in two different ways, and which one you pick depends on which part of the attack chain you actually want to practice. Wi-Fi Mode spins up a real, functioning virtual wireless network, broadcasting an SSID called Drone_Wi-Fi on the 192.168.13.0/24 range, with your choice of weak WEP encryption or the considerably stronger WPA2. This lets the whole exercise start from the very beginning, with you playing the role of an attacker who doesn’t have network access yet and has to earn it.

Non-Wi-Fi Mode skips that entire step and simply brings the containers up directly. This is useful if you just want to focus purely on protocol-level attacks, or if you’re not running inside a Kali VM with wireless card support to begin with. In this mode, the documentation asks you to treat the situation as though initial access to the drone’s data link has already been established, so you can jump straight to the MAVLink-level work.

Summary

The Damn Vulnerable Drone takes an idea that’s already well proven in web security and applies it to a domain where practicing on the real thing tends to be expensive. By simulating a full ArduPilot and MAVLink drone stack inside Docker, right down to Wi-Fi, camera streaming, and flight physics, it hands penetration testers, students, and researchers a realistic, disposable target, backed by more than 40 documented attack scenarios and built-in walkthroughs to guide the way. It won’t teach you to fly a real drone. But it will teach you exactly how one can be hacked, and for anyone working in drone security, that’s the more useful skill anyway.

If you’re interested in drone hacking, check out our Building Your Own Hacking Drone series, where we walk you through attack scenarios targeting Bluetooth and Wi-Fi across a wide range of devices.

We also offer a Drone Hacking training course, taking place November 10-12 at 4:00 PM UTC, available to Subscriber and Subscriber Pro students.

The post Drone Hacking: Hacking UAVs with Damn Vulnerable Drone first appeared on Hackers Arise.

Welcome back, aspiring cyberwarriors!

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.

The post OSINT: WireTapper – Mapping Surveillance and Wireless Devices Around You 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.