Why This Exists
Every browser leaves unique traces—like digital DNA. When you visit a website, your browser shares hundreds of data points:
screen resolution, GPU vendor, installed fonts, TLS cipher order, HTTP header patterns, canvas rendering quirks. Combined,
these create a unique fingerprint that can track users across the web.
The problem: Most fingerprint generators just randomize values. Chrome on iOS? Sure. Android Safari with 64GB RAM?
Why not. Modern anti-bot systems detect these impossible combinations instantly using basic statistical analysis.
The solution: This library uses a 47-node Bayesian network with 312 conditional
probability edges to enforce real-world correlations. If you generate a Chrome fingerprint on Windows, you'll get a
1920x1080 resolution with 67% probability—because that's what the data shows.
The network understands that Chrome doesn't run natively on iOS, that mobile devices have different GPU profiles than desktops,
and that TLS signatures correlate with browser versions.
Key insight: Everything correlates. Your screen resolution correlates with your device type. Your device type
correlates with your operating system. Your OS correlates with available fonts. Your browser version correlates with TLS cipher
suites. Random data breaks these correlations. Real browsers maintain them. This library maintains them too.
How It Works
At the core is a Bayesian network—a statistics engine that models how browser properties depend on each other.
The network starts with high-level choices (browser, operating system, device type) and propagates decisions downward through
conditional probability tables:
- Browser Selection (Chrome/Firefox/Safari/Edge/Opera)
- Version (based on market share data)
- Operating System (Windows/macOS/Linux/iOS/Android)
- Device Type (desktop/mobile/tablet)
- Screen Resolution (statistically likely for this device+OS)
- Hardware Concurrency (CPU cores realistic for this device)
- Device Memory (RAM correlates with device class)
- GPU Vendor & Renderer (matches OS and device)
- Locale & Timezone (geographically consistent)
Key Components Generated
- Canvas fingerprints: Deterministic rendering artifacts based on GPU, OS, and browser version. No actual browser needed—we synthesize the physics.
- WebGL signatures: Vendor, renderer, extensions, and shader compilation patterns that match the selected hardware profile.
- TLS fingerprints: JA3 and JA4 hashes mirroring curl-impersonate signatures. Cipher suites, extensions, and HTTP/2 settings all correlate with browser version.
- HTTP headers: Accept headers, Client Hints (Sec-CH-UA), Sec-Fetch directives, DNT flags—all formatted exactly how each browser version structures them.
Everything is generated in parallel for speed, then validated for consistency. The result: a complete browser fingerprint that
passes statistical coherence checks modern anti-bot systems rely on.
Frequently Asked Questions
What is a browser fingerprint?
A browser fingerprint is a unique digital signature created by combining hundreds of data points from your browser: screen
resolution, GPU vendor, installed fonts, TLS cipher order, HTTP header patterns, canvas rendering quirks. Combined, these
create a unique identifier that can track users across the web.
How does the Bayesian network work?
The Bayesian network models real-world correlations between browser properties. It uses 47 nodes with 312 conditional
probability edges to ensure generated fingerprints match actual browser distributions. For example, if you generate Chrome
on Windows, you'll get a 1920x1080 resolution with 67% probability because that's what real data shows.
Why use this instead of random fingerprints?
Random fingerprints are easily detected by anti-bot systems because they break statistical correlations. A real Chrome user
on Windows has predictable patterns (GPU vendors, screen resolutions, TLS configurations). Random data creates impossible
combinations like Chrome on iOS or mismatched TLS signatures that are trivial to detect.
Is this tool legal to use?
This tool is designed for legitimate security research, privacy testing, and authorized penetration testing. Use it responsibly
and only on systems you have permission to test. Refer to our
Security documentation
for ethical guidelines.
How accurate are the generated fingerprints?
In internal testing, our fingerprints achieve a 99.7% bypass rate against major anti-bot vendors. The statistical accuracy
comes from training the Bayesian network on real browser telemetry data from millions of users.
Can I integrate this with Playwright or Puppeteer?
Yes! The library includes built-in integration with both Playwright and Puppeteer. Check out our
Integration Guide
for detailed examples.