Standard WiFi Routers Can Identify Individuals With 99.5% Accuracy

Standard WiFi routers can now be used to identify specific individuals with near-perfect accuracy, according to new research from the Karlsruhe Institute of Technology (KIT). The study reveals that ubiquitous wireless infrastructure can be repurposed for high-precision surveillance without the target ever carrying a device.

Researchers at KIT's KASTEL department developed a method called Beamforming Feedback Identity inference (BFId) that achieved a 99.5% identification accuracy rate in a study involving 197 participants. The system works by analyzing Beamforming Feedback Information (BFI), which is data transmitted without encryption by connected devices to optimize communication. By observing how radio waves propagate and bounce off objects, the system can generate images of people from multiple viewpoints. Crucially, the method requires no specialized hardware and can identify a person even if they are not carrying a WiFi-enabled device.

The Shift to Standard Hardware

This approach marks a significant departure from previous wireless sensing techniques. Historically, high-accuracy tracking relied on specialized equipment such as LIDAR or the analysis of Channel State Information (CSI). The BFId method, presented at the ACM Conference on Computer and Communications Security (CCS), leverages standard WLAN communications. This transition effectively turns ordinary home and public routers into potential surveillance tools by utilizing data already present in the environment.

Implications for Privacy

Because WiFi networks are invisible and omnipresent, they pose a more covert risk than traditional CCTV cameras. The researchers warn that this capability could be exploited by authoritarian regimes to monitor protesters or by corporations for covert tracking of individuals in public or private spaces. "This technology turns every router into a potential means for surveillance," said Julian Todt of KASTEL. Professor Thorsten Strufe added that by observing radio wave propagation, researchers can create an image of the surroundings and the persons present within them.

The Path Toward Safeguards

To mitigate these risks, the KIT team is urging the industry to implement privacy safeguards into the forthcoming IEEE 802.11bf WiFi standard. The goal is to close the privacy gap that allows unencrypted BFI to be harvested for identity inference. As wireless sensing becomes more integrated into the Internet of Things (IoT) ecosystem, the researchers argue that securing these feedback loops is essential to prevent the widespread deployment of invisible, router-based tracking systems.