Modern Anatomy of Fingerprints: Bypassing JA4+, WebGPU, and a New Era of Anti-Detection

[Papa Carder]

Introduction: Ending the Illusion of Privacy​

Remember those sweet days when clearing cookies, changing your IP, and setting a random User-Agent via a simple extension was enough to bypass antifraud? Forget it. Welcome to 2026, where your browser knows more about your hardware than you do.

The main news story of recent years has been Google's Privacy Sandbox and third-party cookies saga. The good corporation long promised to kill third-party cookies, but ultimately technically backed away from the topic, leaving everything to "user choice." In fact, this gave the green light to legal fingerprinting at the browser engine level.

Recent research (including high-profile papers on Real-Time Bidding analysis and the FPTrace framework) has proven the harsh truth: clearing cookies no longer works. Anti-fraud systems (AFS) like Cloudflare, Akamai, DataDome, and Stripe no longer look at what's stored in your LocalStorage. They focus on how your browser communicates with the server and renders pixels. Changes to your digital fingerprint directly impact your trust score, CAPTCHA detection, account bans, and even advertising rates.

The purpose of this article is to remove these rose-colored glasses, examine modern detection vectors (how we're being caught right now), and show how camouflage methods have evolved.

Part 1. New Detection Vectors: How Are We Being Spotted in 2026?​

If you still think you're being caught out by fonts and Canvas, I have bad news. This is the baseline that's being checked as soon as the page loads. The real magic (and the main catch) happens at deeper levels.

1. The Death of JA3 and the Arrival of JA4+ (Network Fingerprinting)​

For a long time, JA3 was the de facto standard for network fingerprinting. It collected a hash from the TLS (Client Hello) connection parameters when the browser first contacted the server. But starting with Chrome 110+, Google implemented TLS ClientHello randomization. JA3 started producing junk data, and antifraud switched to the heavy artillery – JA4+.

JA4+ (developed by the guys at FoxIO) is more than just a TLS hash. It's a comprehensive network fingerprint that analyzes everything:

• TCP stack: Window Size, MSS, TTL. These parameters are provided by your operating system (Windows 11, Linux, or macOS) before encryption is set.
• TLS (JA4): Protocol version, Cipher Suites, ALPN extensions.
• HTTP/2 (JA4H): Header ordering, SETTINGS frames, pseudo-headers.

How do bot operators get into trouble?
If you use standard requests in Python, axios in Node.js, or even the default Puppeteer, your network fingerprint screams, "I'm a Linux script masquerading as Chrome on Windows!" The server detects a mismatch between Linux TCP parameters and Windows-specific HTTP/2 headers. The result is an instant IP ban or an endless CAPTCHA from Cloudflare Turnstile, even if the proxies are squeaky clean.

2. WebGPU Fingerprinting: The New Graphics King​

WebGL, which we've painstakingly randomized for years, is being retired. WebGPU has finally arrived —a new API that gives the browser low-level access to your graphics card (much like Vulkan, Metal, or DirectX).

Unlike WebGL, which could easily be "noised" with JavaScript injection, WebGPU operates at higher speeds and provides frighteningly accurate hardware metrics:

• Antifraud via navigator.gpu.requestAdapter() retrieves adapter.features and device.limits.
• These limits contain precise information about your GPU architecture: support for specific shaders, maximum buffer sizes, SIMD parallelism.
• Timing attacks: AI checkers force the graphics card to render a hidden, complex mathematical object and measure the execution time down to the microsecond. Faking this correctly (via JavaScript hooks) is nearly impossible—the timings will immediately reveal the emulation.

3. Behavioral Biometrics​

Modern fraud control is integrated with AI-powered machine learning models. They collect telemetry data on your behavior:

• Mouse Microtiming: How do you move the cursor? Bots move it mathematically perfectly (even with Bézier curves, this is obvious due to the lack of natural tremor).
• Typing speed and rhythm (Keystroke Dynamics): Dwell time and flight time.
• Mobile sensors: If you're emulating a mobile device, AFS will request data from the gyroscope and accelerometer. Perfect zeros or random garbage in the coordinates immediately trigger the "Emulator" flag.

Part 2. The Evolution of Anti-Detectives: What's Changed in the Last 2 Years?​

The arms race between antifraud systems (AFS) and multi-account software developers has never stopped, but it was around 2025-2026 that a fundamental paradigm shift occurred. Old approaches are dying out en masse.

The end of the era of cheap JavaScript injections​

Previously, the classic anti-detection browser worked simply: it used standard Chromium and injected JavaScript code through an extension (or at the start of the page). This code overrode the properties of the navigator object (for example, by replacing navigator.userAgent or navigator.hardwareConcurrency).

Why doesn't this work anymore? Modern protection scripts (like DataDome or the scripts inside Cloudflare Turnstile) have learned to detect the substitution itself. They use:

• Native function checks: If you override navigator.userAgent via Object.defineProperty(), anti-fraud will check your new function's toString() method. If it returns JS code instead of native code, you're dead. Even if you've spoofed toString, smart scripts will check the prototype chain.
• Execution timings: JS code injection takes milliseconds. The protection script measures property access time. Replaced properties respond more slowly than native ones.

This is why checkers like CreepJS have become a real nightmare for cheap anti-cheat tools, happily returning "LIES" for every substituted parameter. The true solution today is a deep modification of the engine's source code (Blink/V8 in Chromium) at the C++ level, so that the browser natively returns the required fingerprints without any JavaScript hacks.

Trend 2026: Cloud Phones​

Mobile device emulation in the browser (when you simply change the User-Agent to Android and narrow the window) is dead. Anti-fraud software requests data from real hardware sensors, Bluetooth modules, and checks OS-specific metrics (for example, Roboto font parameters, which are not available on Windows).

The market's response was Cloud Phones. These services (for example, GeeLark and its analogs) no longer emulate mobile devices on the desktop. They provide access to farms of real or deeply virtualized Android devices in the cloud (often based on ARM servers).

The advantages of Cloud Phones:

• Real hardware: Real IMEI, MAC addresses of Wi-Fi adapters, real battery indicators (charge level, network connection status) and data from gyroscopes.
• Perfect network fingerprint: Traffic comes from a real mobile OS with all its inherent TCP/IP signatures, which makes the network fingerprint (JA4+) impeccable.
• No emulators: Applications (such as cryptocurrency exchange or social media clients) don't show any traces of emulators (like Bluestacks or Nox) because they run on native ARM hardware.

TLS Spoofing Under the Hood (How Networks Are Spoofed)​

As we've already established, JA4+'s network fingerprint is a bot killer. To circumvent it, top solutions (AdsPower, Dolphin{anty}, Multilogin) have stopped relying on standard network libraries.

Instead, they implement TLS spoofing at the kernel level of their software. They use custom libraries (often written in Go, such as tls-client or cycletls), which allow for byte-by-byte construction of network packets:

• Client Hello spoofing: The software generates a TLS initialization packet that matches, down to the comma, the fingerprint of a specific Chrome version running on a specific OS.
• HTTP/2 Frame Forgery: Fine-tune header order and frame sizes (SETTINGS, WINDOW_UPDATE) to match the behavior of a real browser, not a Python script.

It's a complex engineering challenge, but without it, it's impossible to get past modern Cloudflare or Akamai.

Part 3. Practice: Bypassing Security with Your Own Hands (DIY)​

Enough theory, let's write some code. We won't be building a full-fledged anti-detection solution from scratch (that's a task for the C++ development team), but we will build a Python script that will bypass basic JA4+ checks and disguise itself as a real browser.

Bypassing JA4+ with a modified TLS client​

If you try to parse a secure site using standard requests, you'll immediately be banned.

# DO NOT do this
import requests

url = "[URL]https://tools.scrapfly.io/api/fp/ja3[/URL]"
# This request will be detected by default headers and Python's TLS fingerprint
response = requests.get(url)
print(response.json())

To bypass the protection, we need a client that can spoof the TLS fingerprint. We'll use the curl_cffi library (it runs on top of a modified libcurl that can impersonate browsers).

# THE CORRECT APPROACH: TLS and JA4+ Spoofing
from curl_cffi import requests

url = "[URL]https://tools.scrapfly.io/api/fp/ja3[/URL]"

# We explicitly specify who we want to impersonate.
# In this case, Chrome 120.
response = requests.get(url, impersonate="chrome120")

# Print the resulting JA3/JA4 fingerprint.
print("Our fingerprint:", response.json().get('ja3'))

# If you check this fingerprint in the database, it matches the real Chrome 120!

What's going on under the hood of <strong>curl_cffi</strong>?
The library replaces not only the headers (User-Agent, Accept-Language) but also, most importantly, the structure of the TLS Client Hello packet: the order of cipher suites (Cipher Suites), extensions (Extensions), and elliptic curves (Elliptic Curves). Cloudflare sees this request and thinks, "Aha, this is a regular Chrome 120 on Windows, let's skip it."

Basic Canvas Noise (Concept)​

If you need to automate the browser using Playwright, you'll have to contend with graphics fingerprinting. Here's a simple example of how canvas noise pollution works conceptually by intercepting the toDataURL method on the browser side (executed before the site's main script loads).

// This script is injected into the page BEFORE the antifraud tool is loaded
const originalToDataURL = HTMLCanvasElement.prototype.toDataURL;

HTMLCanvasElement.prototype.toDataURL = function(...args) {
const context = this.getContext('2d');

// Add microscopic noise: change the color of one pixel
// This is enough to change the Canvas fingerprint hash
if (context) {
const width = this.width;
const height = this.height;
// Take a random pixel
const x = Math.floor(Math.random() * width);
const y = Math.floor(Math.random() * height);

// Change the alpha channel to an unnoticeable value
context.fillStyle = rgba(255, 255, 255, 0.01);
context.fillRect(x, y, 1, 1);
}

// Call the original method and return the "noisy" result
return originalToDataURL.apply(this, args);
};

// Important: Smart scripts will check the toDataURL.toString() function
// This function also needs to be spoofed (we'll omit it in this example for simplicity)

Note: As we discussed in Part 2, such a simple JS injection will be detected by serious checkers like CreepJS, but it may be sufficient for bypassing simple trackers or outdated versions of AFS.

Part 4. Testing: Challenging the Checkers​

Writing code is only half the battle. Any self-respecting researcher knows: until your script has been verified by public scanners, it's not worth a cent. Let's run our work (from Part 3) through the main "X-rays" of 2026.

1. Network fingerprint test (JA4+)​

Tools: Scrapfly FP Scanner, FoxIO JA4 Database.
Scenario: Run our Python script based on curl_cffi (simulating Chrome 120).
Result: PASS (Success)

• The tracker sees a perfect Client Hello.
• The Cipher Suites order, elliptic curves and TCP window parameters are exactly the same as in a real Windows browser.
• Cloudflare Turnstile allows this request without displaying an interactive captcha because the network signature has a high Trust Score.

2. Browser Fingerprinting Test (Canvas & WebGPU)​

Tool: CreepJS (the ultimate checker) and BrowserLeaks.
Scenario: Inject our JavaScript code to replace toDataURL in Canvas via Puppeteer/Playwright.
Result: FAIL (Hardcore failure).

• BrowserLeaks: Revealed a modified Canvas hash. Seems like a success!
• CreepJS: Instantly gave a Trust Score of 0% and filled the screen with red "LIES" text.

Why did we get caught using CreepJS? A smart checker doesn't just take a fingerprint; it verifies the integrity of the environment.

1. He called HTMLCanvasElement.prototype.toDataURL.toString() and saw that the function was substituted (even though we tried to hide it with the base methods).
2. He checked the Prototype Chain and found anomalies in the Proxy object through which we were trying to intercept calls.
3. He measured the micro timings of the function execution—our JS interceptor executed 2 milliseconds slower than the native C++ code built into the V8 engine. This is sufficient for an "Emulation" verdict.

Conclusion: JS injects are only suitable for parsing dumb websites. For working with crypto exchanges, Google Ads, Stripe, or FB, this approach is a surefire way to kill your account. The only options here are custom Chromium builds (where the substitution occurs at the C++ source code level) or high-quality Cloud Phones.

Conclusion: The arms race continues​

If you've read this far, you understand the main point: it's impossible to hide on the internet of 2026. The very concept of "anonymity" has changed. Today, the goal isn't to become invisible (a person without fingerprints is the ultimate source of antifraud paranoia), but to blend in. Your profile should look like a boring clerk from Ohio using the default Chrome browser on Windows 11.

The era of hacks, free extensions for changing the User Agent, and simple JS injections is gone forever. In their place are:

• Network spoofing (JA4+ and custom TLS clients).
• Low-level WebGPU replacement.
• Hardware emulation (Cloud Phones) and AI generation of behavioral factors (mouse movements, typing timings).

Anti-fraud systems will continue to implement machine learning, analyzing dynamic behavior rather than static hashes. And anti-detection developers will delve ever deeper into the core of browsers and operating systems.

This game of cat and mouse is endless. And that's what makes it so beautiful.