Repository Analysis

fingerprintjs/fingerprintjs

The most advanced free and open-source browser fingerprinting library

3.5 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of fingerprintjs/fingerprintjs, a TypeScript project with 27,697 GitHub stars. SynthScan v2.0 examined 10,710 lines of code across 144 source files, recording 25 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 3.5 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

3.5
Adjusted Score
3.5
Raw Score
100%
Time Factor
2026-06-30
Last Push
27.7K
Stars
TypeScript
Language
10.7K
Lines of Code
144
Files
25
Pattern Hits
2026-07-14
Scan Date
0.01
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 2MEDIUM 2LOW 21

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 25 distinct pattern matches across 4 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Hyper-Verbose Identifiers19 hits · 19 pts
SeverityFileLineSnippetContext
LOW…ources/content_blocking/get_unique_filter_selectors.ts98async function getSelectorsFromDirectory(directoryPath: string) {CODE
LOWresources/content_blocking/make_selectors_tester.ts68function getSelectorFromFilterRule(rule: string): string | undefined {CODE
LOWresources/content_blocking/make_selectors_tester.ts109async function getJsToDetectBlockedSelectors(selectors: readonly string[]) {CODE
LOWtests/utils/index.ts93export function getBrowserEngineMajorVersion(): number | undefined {CODE
LOWsrc/agent.ts81function componentsToCanonicalString(components: UnknownComponents) {CODE
LOWsrc/utils/async.ts22export function requestIdleCallbackIfAvailable(fallbackTimeout: number, deadlineTimeout = Infinity): Promise<void> {CODE
LOWsrc/sources/canvas.ts35export function getUnstableCanvasFingerprint(skipImages?: boolean): CanvasFingerprint {CODE
LOWsrc/sources/canvas.ts165function doesBrowserPerformAntiFingerprinting() {CODE
LOWsrc/sources/audio.ts41export function getUnstableAudioFingerprint(): number | (() => Promise<number>) {CODE
LOWsrc/sources/audio.ts98function doesBrowserSuspendAudioContext() {CODE
LOWsrc/sources/audio.ts106function doesBrowserPerformAntifingerprinting() {CODE
LOWsrc/sources/private_click_measurement.ts13export default function getPrivateClickMeasurement(): string | undefined {CODE
LOWsrc/sources/audio_base_latency.ts12export default function getAudioContextBaseLatency(): number {CODE
LOWsrc/sources/audio.test.ts42function doesBrowserPerformAntifingerprinting() {CODE
LOWsrc/sources/audio.test.ts48function doesBrowserSuspendAudioContext() {CODE
LOWsrc/sources/screen_resolution.ts25export function getUnstableScreenResolution(): ScreenResolution {CODE
LOWsrc/sources/webgl.ts241export function shouldAvoidDebugRendererInfo(): boolean {CODE
LOWsrc/sources/webgl.ts249export function shouldAvoidPolygonModeExtensions(): boolean {CODE
LOWsrc/sources/hardware_concurrency.ts30export function getUnstableHardwareConcurrency(): number | undefined {CODE
Cross-Language Confusion (JS/TS)2 hits · 10 pts
SeverityFileLineSnippetContext
HIGH…ources/content_blocking/get_unique_filter_selectors.ts67 print(`Filter "${filterName}" has no selectors`)CODE
HIGH…ources/content_blocking/get_unique_filter_selectors.ts80 print(message)CODE
AI Slop Vocabulary2 hits · 6 pts
SeverityFileLineSnippetContext
MEDIUMrollup.config.ts58 // UMD for users who use Require.js or Electron and want to leverage themCOMMENT
MEDIUMsrc/sources/cookies_enabled.ts12 // navigator.cookieEnabled cannot detect custom or nuanced cookie blocking configurations. For example, when blockingCOMMENT
Over-Commented Block2 hits · 2 pts
SeverityFileLineSnippetContext
LOW.github/workflows/codeql_analysis.yml1# For most projects, this workflow file will not need changing; you simply needCOMMENT
LOWsrc/confidence.ts41 // WindowsCOMMENT