Tag Archives: wi-fi

Identifying People Using Wi-Fi Routers

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/05/identifying-people-using-wi-fi-routers.html

Not identifying people based on their use of Wi-Fi routers, but identifying people using Wi-Fi signals.

This is accomplished through what is known as WiFi sensing, or the use of WiFi signals to infer information about a physical environment. When radio signals like WiFi travel through a space, they interact with the objects and people around them. Those signals can be reflected, scattered, or absorbed. By analyzing how the signal is expected to behave compared with how it is actually received, researchers can infer details about the surrounding environment.

“By observing the propagation of radio waves, we can create an image of the surroundings and of persons who are present,” said Thorsten Strufe, a KIT professor and study co-author, in a press release. “This works similar to a normal camera, the difference being that in our case, radio waves instead of light waves are used for the recognition.”

New Attack Against Wi-Fi

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/new-attack-against-wi-fi.html

It’s called AirSnitch:

Unlike previous Wi-Fi attacks, AirSnitch exploits core features in Layers 1 and 2 and the failure to bind and synchronize a client across these and higher layers, other nodes, and other network names such as SSIDs (Service Set Identifiers). This cross-layer identity desynchronization is the key driver of AirSnitch attacks.

The most powerful such attack is a full, bidirectional machine-in-the-middle (MitM) attack, meaning the attacker can view and modify data before it makes its way to the intended recipient. The attacker can be on the same SSID, a separate one, or even a separate network segment tied to the same AP. It works against small Wi-Fi networks in both homes and offices and large networks in enterprises.

With the ability to intercept all link-layer traffic (that is, the traffic as it passes between Layers 1 and 2), an attacker can perform other attacks on higher layers. The most dire consequence occurs when an Internet connection isn’t encrypted­—something that Google recently estimated occurred when as much as 6 percent and 20 percent of pages loaded on Windows and Linux, respectively. In these cases, the attacker can view and modify all traffic in the clear and steal authentication cookies, passwords, payment card details, and any other sensitive data. Since many company intranets are sent in plaintext, traffic from them can also be intercepted.

Even when HTTPS is in place, an attacker can still intercept domain look-up traffic and use DNS cache poisoning to corrupt tables stored by the target’s operating system. The AirSnitch MitM also puts the attacker in the position to wage attacks against vulnerabilities that may not be patched. Attackers can also see the external IP addresses hosting webpages being visited and often correlate them with the precise URL.

Here’s the paper.

Privacy Implications of Tracking Wireless Access Points

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/05/privacy-implications-of-tracking-wireless-access-points.html

Brian Krebs reports on research into geolocating routers:

Apple and the satellite-based broadband service Starlink each recently took steps to address new research into the potential security and privacy implications of how their services geolocate devices. Researchers from the University of Maryland say they relied on publicly available data from Apple to track the location of billions of devices globally—including non-Apple devices like Starlink systems—and found they could use this data to monitor the destruction of Gaza, as well as the movements and in many cases identities of Russian and Ukrainian troops.

Really fascinating implications to this research.

Research paper: “Surveilling the Masses with Wi-Fi-Based Positioning Systems:

Abstract: Wi-Fi-based Positioning Systems (WPSes) are used by modern mobile devices to learn their position using nearby Wi-Fi access points as landmarks. In this work, we show that Apple’s WPS can be abused to create a privacy threat on a global scale. We present an attack that allows an unprivileged attacker to amass a worldwide snapshot of Wi-Fi BSSID geolocations in only a matter of days. Our attack makes few assumptions, merely exploiting the fact that there are relatively few dense regions of allocated MAC address space. Applying this technique over the course of a year, we learned the precise
locations of over 2 billion BSSIDs around the world.

The privacy implications of such massive datasets become more stark when taken longitudinally, allowing the attacker to track devices’ movements. While most Wi-Fi access points do not move for long periods of time, many devices—like compact travel routers—are specifically designed to be mobile.

We present several case studies that demonstrate the types of attacks on privacy that Apple’s WPS enables: We track devices moving in and out of war zones (specifically Ukraine and Gaza), the effects of natural disasters (specifically the fires in Maui), and the possibility of targeted individual tracking by proxy—all by remotely geolocating wireless access points.

We provide recommendations to WPS operators and Wi-Fi access point manufacturers to enhance the privacy of hundreds of millions of users worldwide. Finally, we detail our efforts at responsibly disclosing this privacy vulnerability, and outline some mitigations that Apple and Wi-Fi access point manufacturers have implemented both independently and as a result of our work.

Using Wi-FI to See through Walls

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/11/using-wi-fi-to-see-through-walls.html

This technique measures device response time to determine distance:

The scientists tested the exploit by modifying an off-the-shelf drone to create a flying scanning device, the Wi-Peep. The robotic aircraft sends several messages to each device as it flies around, establishing the positions of devices in each room. A thief using the drone could find vulnerable areas in a home or office by checking for the absence of security cameras and other signs that a room is monitored or occupied. It could also be used to follow a security guard, or even to help rival hotels spy on each other by gauging the number of rooms in use.

There have been attempts to exploit similar WiFi problems before, but the team says these typically require bulky and costly devices that would give away attempts. Wi-Peep only requires a small drone and about $15 US in equipment that includes two WiFi modules and a voltage regulator. An intruder could quickly scan a building without revealing their presence.

Research paper.