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 API tampering (API lies)
- Fingerprint lie types
- Fingerprint extension code
- Fingerprint browser privacy settings
- Employ large-scale validation, but allow possible inconsistencies
- Feature detect and fingerprint new APIs that reveal high entropy
- Rely only on APIs that are the most difficult to spoof when generating a pure fingerprint
Tests are focused on:
- Tor Browser (SL 1 & 2)
- Firefox (RFP)
- ungoogled-chromium (fingerprint deception)
- Brave Browser (Standard/Strict)
- puppeteer-extra
- Bromite
- uBlock Origin (aopr)
- NoScript
- DuckDuckGo Privacy Essentials
- JShelter (JavaScript Restrictor)
- Privacy Badger
- Privacy Possom
- Random User-Agent
- User Agent Switcher and Manager
- CanvasBlocker
- Trace
- CyDec
- Chameleon
- ScriptSafe
- Windscribe
- data collected: worker scope user agent, webgl gpu renderer, js runtime engine, hashed browser fingerprints (
stable
,loose
,fuzzy
, &shadow
), encrypted ip, encrypted system location, dates, and boolean metrics - data retention: auto deletes 30 days after last visit
- visit tracking: limited to data retention and new feature scaling
Purpose: learn and predict browser engine and platform version, device, and gpu
{
cleanup: false,
decrypted: "Blink",
devicePrimary: "Windows 10 (64-bit)",
deviceTrust: `{
"Windows 10 (64-bit)": ["6a9","fe3","bb7"],
"Windows 7 (64-bit)": ["8a3"],
"Windows 11 (64-bit)": ["e4a"]
}`,
devices: [
"Windows 10 (64-bit)",
"Windows 7 (64-bit)",
"Windows 11 (64-bit)"
],
gpus: [],
healEvents: [],
highEntropyLossYield: false,
highEntropyLost: true,
id: "01aa0cc74cd124b8985d7e386e5499b34770353cab321e214a2aae122b4c1995",
lock: false,
logger: [
"8eff_75d6295c_345026a9: Blink (12/5/2021, 2:54:02 AM)"
],
reporter: `{
"dates": ["12/5/2021","12/10/2021","12/17/2021","12/22/2021"],
"ips": ["8eff","66fa","6ac2","5887"]
}`,
reporterTrustScore: 100,
reviewed: true,
suggested: "no change",
systemCore: "unknown",
systems: [
"Windows"
],
timestamp: "2022-01-15T16:34:23.807Z",
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"
]
}
Purpose: identify browser visit history and activity
{
bot: 0.125,
botHash: "00000001",
botLevel: "stranger:csl",
crowdBlendingScore: 36,
fingerprint: "18ce59ae1e65397c81b38da98e6eed23a8f6d4bd3a2a349ed800f7daebd6f9dc",
firstVisit: "2022-01-17T15:39:21.964Z",
fuzzyInit: "1879e559e5de22c3dceb603775ff8062bb274c41547f9fc0b38e919fc4000000",
fuzzyLast: "1879e559e5de22c3dceb603775ff8062bb274c41547f9fc0b38e919fc4000000",
lastVisit: "2022-01-17T15:39:21.964Z",
lastVisitEpoch: 1642433961964,
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
}
- scaling should occur no more than once per week
- new weekly features may render fingerprints anew
- view deploy history
- 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
- 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
A failing trust score is unique
- start at
100
- less than 2 loose fingerprints: reward
5
extra credit - 0 shadow bits (session metric revisions): reward
10
extra credit - 2 - 10 loose fingerprints: subtract
total*0.1
- 11+ loose fingerprints: subtract
total*0.2
- shadow bits: subtract
(total/64)*31
- trash: subtract
total*5.5
- lies: subtract
total*31
- errors: subtract
total*3.5
A metric with only 1 reporter is unique
- Metric scores decline by metric uniqueness
- Final score is the minimum of all metrics scores
- Blocked or openly poisoned metrics 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
- Blocked/Openly Poisoned
- Unique metrics get 2 weeks to improve their score before auto-deletion
Bots leak unusual behavior and can be denied services
- Excessive loose fingerprints
- User agent version or platform does not match features
- worker scope tampering
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
(worker scope blocked)00000001:stranger:csl
(crowd-blending score low)00000000:friend
(none of the above)
Loose metric 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
- 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 fingerprint ids gathered from
WorkerNavigator.userAgent
and other stable metrics - 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 45 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
- contentWindow (Self) object
- CSS System Styles
- CSS Computed Styles
- HTMLElement
- JS Runtime (Math)
- JS Engine (Console Errors)
- Emojis (DomRect)
- DomRect
- SVG
- Audio
- MimeTypes
- Canvas
- TextMetrics
- WebGL
- GPU Params (WebGL Parameters)
- GPU Model (WebGL Renderer)
- Fonts
- Voices
- Screen
- Resistance (Known Patterns)
- layout rendering engines:
Gecko
,Goanna
,Blink
,WebKit
- JS runtime engines:
SpiderMonkey
,JavaScriptCore
,V8
- unusual results
- forgivable lies (invalid metrics capable of being restored)
- failed calculations that may reasonably occur at random (loose fingerprint metrics)
- prototype tampering
- mismatch in worker scope or iframe
- failed math calculations
- ungracefully blocked features that break the web
- failed executions
window.Fingerprint
window.Creep
- collects as much entropy as possible
- permits loose metrics
- adapts to browsers and distrusts known noise vectors
- aims to ignore entropy unique to a browser version release
- gathers compressed and static entropy
Contributions are welcome.