Wallflower – reproducing a WiFi identity-inference attack

Researchers at a WiFi-sensing lab reproduced the BFId identity-inference attack, which uses beamforming feedback information (BFI) broadcast in the clear by 802.11ac/ax/be devices to identify individuals by their gait with 99.5% accuracy. The reproduction, called wallflower, captures BFI and CSI traces from multiple perspectives and trains an LSTM classifier, aiming to verify the attack's generalizability beyond a single room. The work highlights a privacy vulnerability in WiFi beamforming protocols that leak person-specific channel perturbations even on encrypted networks.

A WiFi-sensing lab that reproduces the BFId paper — identity inference from the beamforming feedback 802.11 devices broadcast in the clear: Todt, Morsbach, Strufe. "BFId: Identity Inference Attacks Utilizing Beamforming Feedback Information."CCS '25. wallflower - records synchronised CSI + BFI traces from a 4-perspective setup, - segments and parses them into ML-ready variable-length time series, and - trains/evaluates a baseline LSTM identity classifier . The headline paper result it targets is BFI identity accuracy 99.5% ± 0.38 from normal walking; the next milestone is reproducing it on our own captures and showing it generalises beyond one room. 802.11ac/ax/be beamforming requires a receiver to periodically send the transmitter Beamforming Feedback Information BFI — compressed quantised-angle channel-state matrices — in the clear , even on encrypted networks. BFId shows that a passive observer who records this BFI while a person walks can infer who the person is, because gait perturbs the channel in a person-specific way. wallflower reproduces this by capturing BFI alongside ground-truth CSI channel state information from multiple perspectives and training an identity classifier. Feature dimensions from the paper, defined once in wallflower/contract.py : | Modality | Features | Composition | Nominal rate | |---|---|---|---| | BFI | 740 +1 dt | 10 quantised angles × 74 channels | ~10 Hz | | CSI | 212 +1 dt | phase + magnitude × 53 subcarriers × 2 antennas | ~285 Hz | The +1 is an appended time-delta column the models consume; parsers store it separately as dt in each .npz . Operative setup current pilot .A single lab-ownedASUS RT-AXE7800AP on5 GHz / channel 36 / 80 MHz SSID LAB AP , BSSID AA:BB:CC:DD:EE:F4 serves both the CSI and BFI roles — this is what configs/ap channels.yaml and wallflower/contract.py actually encode. On node1 the host wlp1s0 AX210 doesBFI capture + traffic + the live RSSI dashboard;CSI is captured bare-metal by FeitCSIon the second AX210. Everything runs on bare metal — no VMs. Real BFI is captured and decoded. The 6 GHz / dual-AP plan described below is the paper'snominalfull-deployment target, not the current rig. Paper-nominal full 4-perspective deployment logical inventory in configs/nodes.yaml under topology.full : 2× access points , both 6 GHz / 160 MHz: AP-CSI on channel 37 — carries the CSI-driving traffic. AP-BFI on channel 85 — its beamforming sounding is what the passive recorder collects. 4 perspective nodes , each with 2× Intel AX210/AX1675 2×2 radios: radio A = CSI capture monitor mode; FeitCSI . radio B = BFI client associates to AP-BFI to elicit sounding . 1 CSI-traffic node — generates iperf3 traffic 200 Mb/s TCP drives BFI sounding; 30 Kb/s UDP keeps CSI flowing . 1 passive BFI recorder — a single monitor-mode capture tcpdump of all BFI sounding into one bfi recorder.pcapng . 1 controller — orchestrates sessions over SSH and optionally trains. AP-CSI ch37 AP-BFI ch85 6 GHz / 160 MHz | | csi-traffic ---+ +--- bfi-recorder passive, 1 pcapng | | +---------+----------+ +----------+----------+ ... x4 perspectives | perspective node N | | radioA=CSI monitor | | 2x AX210 | | radioB=BFI client | +--------------------+ +---------------------+ \ / \ / controller SSH orchestrator + trainer The controller talks to each node over SSH or locally in pilot via: python3 -m nodes.<agent <action --participant P001 --style normal --trial 001 \ --perspective N --out-dir DIR actions: detect | start | stop | status | health Every agent prints one structured JSON object to stdout {agent, action, ok, node, ts utc, ...} . start writes a pidfile so stop can terminate the capture. Output filenames come from wallflower.contract csi raw name p , bfi recorder name . data/raw/participant=P001/style=normal/trial=001/ metadata.json csi p1.raw csi p2.raw csi p3.raw csi p4.raw bfi recorder.pcapng logs/ wallflower/ ├── wallflower/ shared CONTRACT constants, paths, dataclasses — import this │ └── contract.py single source of truth: feature dims, channels, layout ├── orchestrator/ controller CLI wallflower — session/trial orchestration over SSH ├── nodes/ per-node agents csi, bfi client, bfi recorder, traffic ├── capture/ low-level capture helpers tcpdump/iw wrappers, stdlib-only ├── parsers/ CSI .raw + BFI .pcapng - variable-length .npz / parquet ├── models/ baseline LSTM identity classifier + training/eval ├── configs/ lab.yaml, nodes.yaml, ap channels.yaml ├── scripts/ operator convenience scripts ├── experiments/ experiment configs / run outputs └── data/ raw / processed / models / reports git-ignored; .gitkeep Config files kept consistent with wallflower/contract.py : configs/lab.yaml — data root, 80/20 split, perspectives, styles + repeats, band/width/channels, clock-sync tolerance, sample-rate targets, traffic. configs/nodes.yaml — full 4-perspective inventory and the pilot profile mapping every role onto node1. configs/ap channels.yaml — operative RF plan: single ASUS AP, 5 GHz / 80 MHz, both roles on ch36 SSID LAB AP , BSSID AA:BB:CC:DD:EE:F4 , with the paper-nominal 6 GHz / dual-channel plan noted as historical context. Core orchestrator + nodes + capture stay stdlib-first; only pyyaml needed : pip install -e . ML stack parsers + models : numpy/torch/pandas/pyarrow/scikit-learn/optuna pip install -e ". ml " BFI pcapng parsing helper scapy : pip install -e ". capture " Dev tooling ruff + pytest : pip install -e ". dev " Requires Python ≥ 3.12. - Ubuntu 26.04 LTS, kernel 7.0.0, Python 3.14; iw 6.17, tcpdump , ssh present. 2× Intel AX210/AX1675 2×2 radios iwlwifi loaded, firmware present :- PCI 01:00.0 → wlp1s0 , phy1 , MAC AA:BB:CC:DD:EE:02 → BFI client / traffic role. - PCI 02:00.0 → wlp2s0 , phy2 , MAC AA:BB:CC:DD:EE:03 → BFI recorder role. - PCI - Wired control plane: eno1 . Assumed MISSING degrade gracefully / document install : tshark , iperf3 , chrony / ntp , ptp4l , PicoScenes, git , gcc / make / cmake / dkms . sudo on node1 requires a password — non-interactive root is not available. Anything needing root monitor mode, channel set via iw , package install, raw socket capture is printed for the operator to run , prefixed clearly, e.g.: OPERATOR-RUN sudo iw dev wlp1s0 set type monitor OPERATOR-RUN sudo iw dev wlp1s0 set channel 36 80MHz 5 GHz Read-only inspection lspci , iw dev , ip link , reading /sys works without root. The pilot collapses every role onto node1 see configs/nodes.yaml profile pilot . It validates the end-to-end pipeline on a single machine before scaling to 4 perspectives. 0. Inspect radios without root sanity check the pilot mapping : python3 -m nodes.csi agent detect --perspective 1 1. Create a session writes session config under data/ : wallflower init-session --participant P001 --profile pilot 2. Start a trial clock-sync check, spawns node agents, writes metadata.json : wallflower start-trial --participant P001 --style normal --trial 001 ...participant walks back and forth... 3. Stop the trial terminates captures via pidfiles, finalises metadata : wallflower stop-trial --participant P001 --style normal --trial 001 4. Validate everything that was recorded for the session: wallflower validate-session --participant P001 Any privileged step that cannot run will print an OPERATOR-RUN command for you to execute manually, then continue without crashing. Installed as the wallflower console script orchestrator.cli:main : | Command | Purpose | |---|---| init-session | Create/validate a session and select a profile pilot or full . | start-trial | Run the clock-sync gate, spawn node agents CSI, BFI client, BFI recorder, traffic , write the trial metadata.json . | stop-trial | Stop captures via their pidfiles and finalise metadata.json . | validate-session | Check raw layout, file presence, sample-rate / clock-sync tolerances per configs/lab.yaml and report problems. | Apache 2.0 — see LICENSE /punklabs-ai/wallflower/blob/main/LICENSE .