Show HN: GlassBox – what the browser reveals, and how identifiable you are

The solution for me is not to be more common. The solution is to create a very unique profile each and every time.

> 1 in 7.6 billion browsers share this profile

I always knew I was so unique

Also see https://coveryourtracks.eff.org/

Pretty sure someone else also promoted Claude to make something like this and posted it a few weeks back.

This doesn’t seem right. 1 in 6.2 billion for Firefox on an iphone, not including ip/network uniqueness?

Seems more plausible that anti-fingerprinting is throwing it off?

It's horrifying to see that our browsers give up anything at all. I feel like we need a new model where we just get served the content, and we don't serve up anything to the content provider. I feel ill.

It's worth noting that you need both uniqueness and some form of stability. If you consistently show up as a diffent fingerprint every time you visit or for every different site, then that is a form of privacy as well.

Cool tool, but the language is painfully characteristic of AI. Maybe we are collectively getting over caring about that, but if not, it's worth a pass of "make this sound a little less like AI."

E.g., the guide page ends with

> The honest bottom line. Perfect anonymity [...]

If your ISP issues an IPv6 address, isn't that pretty much game over for anonymity/uniqueness?

Lying AI slop. Says I am unique among 7.2B devices and shows me a different fingerprint every time.

Idk how this vibe coded slopware made it to the front page of HN

Interesting work! Would randomnize the least frequent used fonts, apis and settings be an way to anti-fingerprinting?

Fascinating visualization , as someone building productivity extensions, it is always sobering to see exactly how much surface area the browser exposes.

This does not account for anti fingerprinting. Also, are there any new tricks that are not in fingerprintjs?

The "Hardware & Environment" part which supposedly links me across different browsers doesn't work — it differs between Safari, Firefox and Chromium.

In a private Safari window, it's not even stable across reloads.

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.