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New research has revealed that standard Wi-Fi networks can be used to identify individuals—even if they are not carrying a phone or any connected device. A study by the Karlsruhe Institute of Technology (KIT) has demonstrated that passive monitoring of Wi-Fi signals can effectively track and recognize people based solely on how their bodies interact with surrounding radio waves.
This technique relies on common Wi-Fi infrastructure. No specialized sensors or access to personal devices are needed. Instead, the system observes how standard Wi-Fi signals bounce off people and objects. These patterns are then analyzed using machine learning to generate a profile of the person’s physical presence, similar to how a camera builds an image using light.
According to TechXplore, unlike earlier methods that required specific hardware or tapped into detailed channel state information (CSI), this approach uses what’s known as beamforming feedback information (BFI)—signals that are routinely exchanged between user devices and routers. Although intended for optimizing network performance, this data can also be used to reconstruct a three-dimensional representation of a person’s movement or posture.
Importantly, individuals do not need to carry any connected devices. The presence of other active Wi-Fi devices in the environment is enough to create these profiles. Even a powered-off smartphone won’t prevent identification if there are other devices nearby still communicating with the router.
In a controlled experiment with nearly 200 participants, the system achieved close to 100% accuracy in identifying individuals, regardless of their walking patterns or angle of observation.
Researchers warn that while this method could have legitimate applications—such as security—it also poses privacy risks. Since Wi-Fi networks are present in most public and private spaces, the potential for covert surveillance is considerable, especially in environments with minimal regulatory oversight.
The findings are set to be presented at the ACM Conference on Computer and Communications Security (CCS 2025) in Taipei. The research team is also calling for safeguards in upcoming wireless standards, including IEEE 802.11bf, to limit unintended surveillance capabilities and ensure privacy protections are built into future Wi-Fi technologies.
