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!

Many of us are being pushed toward insecure messengers and social networks. These communication channels may be monitored and are not trustworthy. That does not mean private communication is impossible. Far from it. One of the oldest and most practical problems in cryptography is how to send a secret message through an open channel without making the message obvious to anyone who sees it. And that problem has already been solved very well.

The encrypted text does not always have to look like encrypted text. A message can be hidden in plain sight so that it looks like ordinary content, or it can be embedded inside something else entirely, such as audio, video, or text that does not raise suspicion. That is the realm of steganography. Cryptography protects the meaning. Steganography helps hide the fact that a message exists at all.

For most people, though, the real need is much simpler. They want a practical and convenient way to encrypt messages quickly and reliably. So let’s look at some easy tools that make that possible.

Workflow

The workflow is always the same. First, the sender and recipient agree on a secret password or passphrase. A short sentence made up of several words is often better than a single word because it is easier to remember and usually much stronger. Then the sender pastes the message into the tool, clicks Encrypt, enters the password, and sends the resulting encrypted text through whatever channel they want, even if that channel is insecure. The recipient then uses the same tool and the same password to decrypt the message.

That is the basic pattern, and it stays consistent across different tools and platforms.

Web-Based Encryption Tools

There are browser-based applications that can encrypt text very effectively, and they are often the easiest place to begin. But there is one very important detail. You want to make sure the encryption happens entirely on the client side. That means the message is processed inside your browser, on your own machine, and the password never leaves your device. If the server never sees the key, the risk of leakage is much lower.

That point is worth checking. A good looking website is not automatically secure. One way to verify local processing is to monitor browser traffic using Developer Tools, or DevTools, and see whether your password is being sent over the network. Another way is to use a firewall application such as Little Snitch and observe whether the service tries to communicate with remote servers during encryption or decryption. If the system is truly local, the encrypted message can later be decrypted either through the same browser-based Decrypt form or offline with OpenSSL.

There are a few websites out there. 

The first one is Encrypt Online. It uses AES-256-CBC to encrypt text, strings, JSON, YAML and config data directly in your browser. It’s considered to be a strong, mathematically unbreakable encryption algorithm.

Paranoia Text Encryption uses AES-256 in EAX mode with keys derived from passwords using Argon2. That combination is strong and modern.

LOCK.PUB is another browser-based option, focused on creating encrypted online notes, polls, images, audio and a lot more. The content can only be accessed with the correct password.

For users who want something more flexible and technical, GCHQ CyberChef is a powerful open-source option from the UK’s GCHQ intelligence agency. It supports many encryption and encoding operations. 

AES Utils is another choice, using AES-256-GCM with PBKDF2 while keeping the interface simple.

Warning

As a contrast, it is useful to look at what should not be considered a proper secure solution. MagicTool encrypts and decrypts text without requiring a password. 

At first glance that may sound convenient, but from a cryptographic point of view it means the same built-in secret is used every time. If anyone knows the website and the service’s behavior, they may be able to infer or recover the messages. In that setup, the tool itself is functioning like the secret key simply by existing.

That is not a strong cryptographic model. However, in some situations, “encryption” without a user-provided key could still serve a purpose. For example, it might be used to deceive an adversary into believing you are an inexperienced user who does not know how to encrypt messages properly, when your real objective is to feed them specific information in a controlled manner.

Offline Encryption Software

Browser tools are convenient, but sometimes you want something local, traditional, and fully under your control. Linux, Windows, and macOS all have native or widely trusted applications that can encrypt text and files without relying on a remote browser service.

Common examples include command-line tools such as GnuPG, OpenSSL, and ccrypt, along with password managers, VeraCrypt, Cryptomator, and a wide range of similar utilities. These tools are often used not only for text messages but also for file encryption, container protection, and secure storage.

Offline tools have an advantage because they reduce the number of outside systems involved in the process. You are not dependent on a remote website staying available, and you do not need to trust a third-party server with your content or password. For many users, that is a better model from a privacy perspective. At the same time, it is important to understand that privacy tools still leave traces. On a Windows system, a digital forensics investigator may be able to see installation artifacts, program execution history, registry keys, recent files, shortcut files, jump lists, user activity traces, prefetch data and remnants of encrypted containers or text editors. Even when the content itself remains protected, the fact that you used a particular application may still be visible in the system’s history.

That is why privacy-conscious users often prefer systems that are designed to leave fewer traces by default. A privacy-oriented operating system, live environment, or hardened Linux distribution can be a better choice when your goal is to reduce unnecessary local exposure. 

Summary

Encrypting messages is a simple and useful privacy skill. Whether you use a browser-based tool or you prefer offline software the basic principle is the same. 

The right tool depends on the situation. Browser-based tools are convenient and fast. Offline tools give you more independence and more control. Some systems are designed for strong cryptography, while others are only suitable for demonstration or deceptive use. Understanding the difference matters.

If you want to go deeper into how privacy can be preserved on real systems and how forensic traces are created and analyzed, our Anti-Forensics training is your next step. We covered advanced techniques for preserving your privacy and understanding what investigators can still see even when you think you have covered your tracks.

The post Anti-Forensics: How to Encrypt Messages in Any Messenger or Social Network first appeared on Hackers Arise.