I built this after a thread here about Alibaba using an audio-context trick to fingerprint visitors. I knew a fair number of fingerprinting methods but not that one, and I wanted to see all of them in one place, running against my own browser.

GlassBox runs ~31 probes (canvas, WebGL/WebGPU, audio, fonts, the WASM feature set, math/engine quirks, WebRTC IP, timezone/locale, the permission and API matrices, an incognito heuristic, cross-site login-state, and so on) and shows the raw values plus an estimate of how identifiable you are.

A few deliberate choices:

- One static HTML file, no dependencies, no build step. Everything runs client-side and nothing is sent, with one opt-out exception: IP geolocation, which calls a public API. I didn't want a privacy tool that phones home.

- The "identifiability" number is an honest model, not a measurement. It sums published per-signal entropy (Panopticlick / AmIUnique / Cover Your Tracks), discounts signals your browser masks, and caps at the ~33 bits needed to single out one person on Earth. A no-server tool can't compute true rarity against a live population, so I label it an estimate instead of pretending. For real population numbers, Cover Your Tracks and AmIUnique have the datasets.

- There's a companion guide on lowering your fingerprint, with the caveat that uniqueness isn't privacy: blending into a big crowd (Tor at its default size) beats a bespoke hardened setup that makes you the only one who looks like that.

Source (MIT): https://github.com/HotStartLabs/glassbox

I'd genuinely like to know which vectors I'm missing, especially from the anti-fraud / detection side.