The Spy Who Came in from the WiFi: Beware of Radio Network Surveillance (www.kit.edu)

Researchers at Karlsruhe Institute of Technology (KIT) demonstrated a new privacy attack that can identify people simply by passively eavesdropping on routine WiFi communications. Unlike prior WiFi‑sensing work that required special hardware or channel state information (CSI), this method exploits unencrypted beamforming feedback information (BFI) — short feedback frames sent by client devices to routers — which any standard WiFi device in range can read. By treating the BFI streams from multiple devices as radio‑wave “images” and training a machine‑learning model, the team reconstructed multi‑perspective patterns of the environment and achieved near‑100% identification accuracy in a study with 197 participants; once trained, inference takes only seconds and works even if subjects carry no device or have devices switched off. This turns ubiquitous routers into stealth surveillance sensors and raises urgent privacy and policy concerns: the attack is invisible to targets, requires no compromise of infrastructure, and could be abused by companies or authoritarian states to track people across spaces. The authors call for mitigations in the upcoming IEEE 802.11bf standard and other safeguards (e.g., encrypting or minimizing BFI exposure). The work, titled BFId: Identity Inference Attacks utilizing Beamforming Feedback Information, will be presented at ACM CCS and underscores that protocol‑level design choices can create powerful, unexpected sensing channels.