CreepJS

https://abrahamjuliot.github.io/creepjs

The purpose of this project is to shed light on weaknesses and privacy leaks among modern anti-fingerprinting extensions and browsers.

Detect and ignore JavaScript tampering (prototype lies)
Fingerprint lie patterns
Fingerprint extension code
Fingerprint browser privacy settings
Use large-scale validation and collect inconsistencies
Feature detect and fingerprint new APIs that contain high entropy
For fingerprinting, use APIs that are the most difficult to fake

Tests are focused on:

Tor Browser (SL 1 & 2)
Firefox (RFP)
ungoogled-chromium (fingerprint deception)
Brave Browser (Standard/Strict)
puppeteer-extra
FakeBrowser
Bromite
uBlock Origin (aopr)
NoScript
DuckDuckGo Privacy Essentials
JShelter (JavaScript Restrictor)
Privacy Badger
Privacy Possum
Random User-Agent
User Agent Switcher and Manager
CanvasBlocker
Trace
CyDec
Chameleon
ScriptSafe
Windscribe

Fingerprinting APIs

Service is limited to the CreepJS GitHub page.

Prediction API: https://creepjs-api.web.app/decrypt

/decrypt captures fingerprints (Canvas, WebGL, etc.) in a data model and renders the data to cloud storage. The data model follows a set of instructions on how to respond if the fingerprint appears again. This includes reject, merge, timestamp, modify, log data, and self-learn from patterns. Some patterns are configured to trigger a manual review.

Newly discovered data starts off with a low score. If the data reappears with unique visits, the score will improve. If the score does not improve within 3 days, the data will be placed in a queue for auto-deletion. Any data with a last visit timestamp older than 7 days is automatically deleted. This design aims to make it difficult for abnormal data to blend in and establish any level of trust over time.

Fingerprint API: https://creepjs-api.web.app/fp

/fp computes a fingerprint profile derived from unique patterns. If certain suspicious patterns are detected, then the Prediction API will go into "locked" mode, in which case all further learning and data merging on the server will be shut down.

Web Traffic API: https://creepjs-api.web.app/analysis

/analysis creates a hidden fingerprint profile and collects as much unique data as possible, both stable and unstable. This profile is used to analyze patterns and improve fingerprinting on the front end. It is also used to identify and prevent network abuse. If you receive a tag of sus or bad, it means that your fingerprint was identified as highly suspicious and easily trackable, even with any anti-fingerprinting measures taken.

Rate-Limits

The challenge, if you choose to accept it, is to avoid getting put on timeout or banned.

Each network is allotted 500 tokens per hour to spend on API calls.
- There is a token bucket for each of API. The /analysis API simulates cost, but it does not implement a timeout.
One token is consumed per request.
- Complete refunds will be given to networks that pause for at least 5 minutes.
- If a request to the /decrypt API is delayed for less than a minute, the token cost will be doubled. Requests to this API are sent only at the start of a session or when the fingerprint changes (if the API is not locked).
- If the number of encrypted IP addresses on the network exceeds 5, the network will be suspended from receiving refunds. Moreover, the token cost will be multiplied by the number of IPs divided by 2 (100 IPs is the maximum tracked per network).
- If all 500 tokens are consumed within a given hour, the network will be put on timeout for 1 hour multiplied by the number of historic timeouts given to the network.
Fingerprints that cause a constant network storm without pause may be marked and banned.

Data

data collected: worker scope user agent, webgl gpu renderer, js runtime engine, hashed browser fingerprints (stable, loose, fuzzy, & shadow), masked network ip address, system location, dates, and other fingerprint data displayed on the website
data retention:
- browser fingerprint profiles auto delete:
  - 30 days after last visit
  - Or, 30 days after we change the fingerprint
- prediction data profiles auto delete:
  - if it fails to establish a good crowd-blending score within 3 days
  - 7 days after last seen
- web analytics and traffic history auto discards:
  - after 60 days

Example Data Models

Prediction Samples

Purpose: learn and predict browser engine and platform version, device, and gpu

{
	cleanup: false,
	decrypted: "Blink",
	devicePrimary: "Windows 10 (64-bit)",
	deviceTrust: `{
		"Windows:Windows 10 (64-bit)": ["6a9","fe3","bb7"],
		"Windows:Windows 7 (64-bit)": ["8a3"],
		"Windows:Windows 11 (64-bit)": ["e4a"]
	}`,
	devices: [
		"Windows:Windows 10 (64-bit)",
		"Windows:Windows 7 (64-bit)",
		"Windows:Windows 11 (64-bit)"
	],
	gpuBrands: [
		"INTEL"
	],
	gpus: [
		"INTEL:ANGLE (Intel(R) UHD Graphics Direct3D11 vs_5_0 ps_5_0)",
		"INTEL:ANGLE (Intel, Intel(R) UHD Graphics 620 Direct3D11 vs_5_0 ps_5_0, D3D11)"
	],
	gpuWatch: [
		"INTEL:460191600000:8/2/1984:703722......:18"
	],
	healEvents: [],
	highEntropyLossYield: false,
	highEntropyLost: true,
	id: "01aa0cc74cd124b8985d7e386e5499b34770353cab321e214a2aae122b4c1995",
	lock: false,
	logger: [
		"8eff_75d6295c_345026a9: Blink (2/5/1984, 2:54:02 AM)"
	],
	reporter: `{
		"dates": ["2/5/1984","2/10/1984","2/17/1984","2/22/1984"],
		"ips": ["8eff","66fa","6ac2","5887"]
	}`,
	reporterTrustScore: 100,
	reviewed: true,
	suggested: "no change",
	systemCore: "unknown",
	systems: [
		"Windows"
	],
	systemWatch: [
		"Windows:Windows:460191600000:8/2/1984:703722......:18"
	],
	timestamp: "1984-08-01T07:00:00.000Z",
	trash: false,
	type: "Canvas System",
	userAgents: [
		"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36"
	]
}

Fingerprints

Purpose: identify browser visit history and activity

{
	bot: 0.125,
	botHash: "00000001",
	botLevel: "stranger:csl",
	crowdBlendingScore: 36,
	fingerprint: "18ce59ae1e65397c81b38da98e6eed23a8f6d4bd3a2a349ed800f7daebd6f9dc",
	firstVisit: "1984-08-01T07:00:00.000Z",
	fuzzyInit: "1879e559e5de22c3dceb603775ff8062bb274c41547f9fc0b38e919fc4000000",
	fuzzyLast: "1879e559e5de22c3dceb603775ff8062bb274c41547f9fc0b38e919fc4000000",
	lastVisit: "1984-08-01T07:00:00.000Z",
	lastVisitEpoch: 460191600000,
	looseFingerprints: [
		"f331fd21a4f8dec8054ffaec88c32723f840f6a6174303cd787fb676a513bbf6"
	],
	looseSwitchCount: 0,
	maxErrors: 0,
	maxLies: 0,
	maxTrash: 0,
	score: 100,
	scoreData: `{
		"switchCountPointGain": 5,
		"errorsPointGain": 0,
		"trashPointGain": 0,
		"liesPointGain": 0,
		"shadowBitsPointGain": 10,
		"grade": "A+"
	}`,
	shadow: "0000000000000000000000000000000000000000000000000000000000000000",
	shadowBits: 0,
	signature: "",
	timeHoursAlive: 0,
	timeHoursFromLastVisit: 0,
	timeHoursIdleMax: 0,
	timeHoursIdleMin: 0,
	visits: 1,
	benchmark: 565.4,
	resistance: ''
}

New feature scaling

scaling should occur no more than once per week
new weekly features may render fingerprints anew
view deploy history

Signatures

you may optionally sign your fingerprint with 4-64 characters
signatures can be memorable descriptors
in low entropy browsers, a signature can signal to others that the fingerprint is shared

Fingerprint Tracing Formulas

Fingerprint Hashing

FP-ID: SHA-256 hashing of stable fingerprint (Creep)
Fuzzy: fuzzy hashing of first loose fingerprint
Diffs: fuzzy hashing of current loose fingerprint
Shadow: fuzzy hashing diffs history

FP-ID...: 9368a2b8913acba5633aa8f353bfd546aaaf77fd57c1416580e90fc41666feb2
Fuzzy...: 98fcf569e50680c3dcfb8e53e34874e2b2075c415208a1c05292119ec4000000
Diffs...: 50ed3569e50680c3dcfb8e00e3387c5fb2075c415408a2006292119ec4000000
Shadow..: 1111100000000000000000110010011100000000010001101000000000000000

Trust Score

A failing trust score is unique

The trust score shows the level of trust computed from the browser fingerprint values and revision indicators. If the score is 100%, there is a high level of trust in the reported values. Values should not be trusted when the score is low. No attempt is made to score how well a browser performs against fingerprint traceability and linkability. It is not always beneficial to have a high trust score, and sometimes a low trust score is not bad.

start at 100
less than 20 loose fingerprints: reward 3 extra credit
less than 0.2 shadow bits (revision indicator): reward 4 extra credit
20-40 loose fingerprints (revision indicator): subtract count * 0.1
40 or more loose fingerprints: subtract count * 0.5
0.1 or more shadow bits: subtract value * 15
trash: subtract count * 5.5
lies: subtract count * 31
errors: subtract count * 3.5
finally add the crowdBlendingScore to the above total and divide by 2

Definitions

Trash

unusual results or rare data
forgivable lies: invalid data that can either be restored or used to create a better fingerprint

platform = 'Cat OS'
gpu = '   Cat Adaptor'
// ¯\_(ツ)_/¯

failed calculations that may reasonably occur at random (loose fingerprints)

userAgent = 'Chrome 102'
features = '101' // I disabled a feature
gpu = '^5zeD4 Cat Titan V' // We can forgive this

Lies

JS tampering of native API functions via Proxy or Object.defineProperty()
Window scope values not matching WorkerGlobalScope values
top-level Window values not matching HTMLIFrameElement.contentWindow values
failed Math calculations or invalid DOMRect coordinates
Inconsistent results when rendering the same data repeatedly.

Errors

ungracefully blocked features that break the web
failed executions

Performance.now = function() {
	// break the web
	throw new Error('Crash the code before it starts!')
}

Shadow Bits

tracks the amount of 1 values (bits) in the shadow fingerprint

bits = 4
totalBins = 64
shadowBits = bits/totalBins // 0.0625

Crowd-Blending Score

A data set with only 1 reporter is unique and easy to trace

In the prediction section, the crowd blending score is a site indicator that scores how well certain fingerprints blend in with others (strictly collected on the same site).

Data scores decline by data uniqueness
Final score is the minimum of all data scores
Blocked or openly poisoned data will collectively reduce the final score by 25%
Scoring formula: 100-(numberOfRequiredReporters ** (numberOfRequiredReporters - numberOfReporters))
Where the number of required reporters is 4:
- Blocked/Openly Poisoned -100
- 1 reporter -64
- 2 reporters -16
- 3 reporters -4
- 4+ reporters is considered a perfect score
Unique data gets 2 weeks to improve its score before auto-deletion

Bot Detection

Bots leak unusual behavior and can be denied services

Do we really know you are a bot? No, but we can have fun trying!

Excessive loose fingerprints
User agent version or platform does not match features
worker scope tampering

bot hash/level

10000000:smart-enemy:lws (lied worker scope)
01000000:crafty-attacker:lpv (lied platform version)
00100000:stealth-hacker:ftp (function toString proxy)
00010000:clumsy-spy:ofv (ua outside features version)
00001000:bold-fraud:elc (extreme lie count)
00000100:hyper-client:elf (excessive loose fingerprints)
00000010:locked-down:wsb (service & shared worker scopes blocked)
00000001:stranger:csl (crowd-blending score low)
00000000:friend (none of the above)

// Cute cat trap. Works every time!
let clientIsBadBot = false
let banned = false
// How long did the client pause to admire the cute cat?
const catTime = await getClientTimeWithCuteCat()
if (catTime < 10000 /* 10 seconds */) {
  clientIsBadBot = true
}
if (catTime < 1000) {
  // client should get banned! Proceed with caution
  // Agent could be extraterrestrial and friendly
  banned = true
}

Shadow

Loose fingerprint revision patterns can follow stable fingerprints like a shadow

Shadow: a string of 64 characters used to capture the history of fuzzy fingerprint diffs
Diffs or revisions may include browser updates, user settings and/or API tampering

Browser Prediction

A prediction is made to decrypt the browser vendor, version, renderer, engine, system, device and gpu
This prediction does not affect the fingerprint
Data is auto matched to fingerprints gathered from WorkerNavigator.userAgent and other stable fingerprints
Decoded samples from the server are auto computed or manually reviewed
Each sample goes through a number of client and server checks before it is considered trustworthy
Samples that are poisoned can self learn and heal themselves
Samples aging 7 days since last timestamp visit are auto discarded (random samples that never return are eventually auto removed)
If the worker scope is blocked and the fingerprint ids exist in the database, the prediction can still be made

Tests

contentWindow (Self) object
CSS System Styles
CSS Computed Styles
HTMLElement
JS Runtime (Math)
JS Engine (Console Errors)
Emojis (DomRect)
DomRect
SVG
Audio
MimeTypes
Canvas (Image, Blob, Paint, Text, Emoji)
TextMetrics
WebGL
GPU Params (WebGL Parameters)
GPU Model (WebGL Renderer)
Fonts
Voices
Screen
Resistance (Known Patterns)
Device of Timezone

Supported

layout rendering engines: Gecko, Goanna, Blink, WebKit
JS runtime engines: SpiderMonkey, JavaScriptCore, V8

Interact with the fingerprint objects

window.Fingerprint
window.Creep

Fingerprint (loose fingerprint)

The loose fingerprint is used to detect rapid and excessive fingerprints

collects as much entropy as possible, including data with instability caused by fingerprinting resistance: JS tampering patterns, random poison, known noise, invalid data, and more
does not collect data containing a high amount of instability: viewport size, performance, network speed,
skips slow fingerprints: webrtc data

Creep (FP ID)

This is the main fingerprint, the creep

adapts to browsers and distrusts known noise vectors
aims to ignore entropy unique to a browser version release
gathers compressed and static entropy

Develop

Contributions are welcome.

🟫 install yarn install
🟩 build yarn build:dev
🟪 watch yarn watch:dev
🟦 release to GitHub pages yarn build

If you would like to test on a secure connection, GitHub Codespaces is supported. It is discouraged to host a copy of this repo on a personal site. The goal of this project is to conduct research and provide education, not to create a fingerprinting library.

randomshell/creepjs