WebGPU

1. What is WebGPU in Browser Fingerprinting

WebGPU (Experimental as of 2025) is the next-generation browser API for interacting with the GPU beyond WebGL. It’s supported across modern Chromium-based browsers and exposes access to lower-level GPU features, including:

• Adapter Information through GPUAdapter
  • adapter.vendor (integrated hardware vendor)
  • adapter.device (GPU model)
  • adapter.architecture
• Supported Features: e.g., timestamp-query, texture-compression-bc, shader-f16
• Limits: precision, texture sizes, buffer alignment, etc., unique per GPU class.
• Shader Compiling Behavior and execution patterns

WebGPU gives fingerprint engines much cleaner, more portable, and precise GPU-related data than WebGL. This makes it a priority in advanced fingerprint detection systems, particularly in precision-driven platforms such as identity verification, gaming, and surveillance-grade analytics.

2. How Platforms Detect WebGPU Fingerprints

Unlike WebGL, WebGPU uses an asynchronous approach to gather device-level capabilities. Platforms extract this data by:

1. Running Async Queries on navigator.gpu.requestAdapter(), and collecting:

• Vendor ID
• Device name
• Adapter architecture
• Driver version (if not spoofed by the kernel or browser sandbox)

2. Checking Supported Features and Limits automatically:

• Dynamically logs which features (like read_only_depth_stencil or downlevel-*) are present
• Logs available GPU memory, compute limits, and precision values

3. Cross-monitoring with Other Fingerprint Vectors like:

• Browser Manufacturer
• Device UID (if stable)
• System Architecture
• WebGL Info
• Operating System Version

4. Detecting Device Type and Stability Signals:

• E.g., detection systems may flag devices that appear to be in environments where GPU access is simulated or virtualized
• WebGPU-based length analysis is often used as a device integrity check for automation and fingerprint tools

Note: WebGPU fingerprinting is currently only available in Chromium-based browsers supporting the webgpu API, making it future-forward fingerprinting technique.

3. How FlashID Masks WebGPU Fingerprints

FlashID introduces advanced WebGPU fingerprint masking to disable the correspondence between a real device and one used online. The system simulates both the adapter data and feature mapping to fit the behavior of a realistic browser-device combo.

FlashID’s WebGPU spoofing includes:

1. Fake GPU Adapter Information:

• adapter.vendor spoofing (e.g., "Google Inc.", "NVIDIA Corporation")
• adapter.device simulation (e.g., "ANGLE (Intel, Mesa DRI Intel(R) UHD Graphics 630 (Coffeelake))")
• adapter.architecture identity control (like "Skylake" or "Stoney")

2. Feature & Limit Masking:

• Enables setting of custom supportedFeatures such as timestamp-query, depth_clip_control, shader-f16, etc.
• Simulates realistic supportedLimits values that align with known GPU classes or fake adapter profiles

3. Device Capability Stabilization:

• Per-browser-profile configuration ensures consistency across all loads of that browser
• Limits like maxColorAttachments, maxVertexBufferArrayStride, and maxWorkgroupSize are spoofed while remaining behaviorally plausible

4. Cross-API Consistency Management:

• Synced with spoofing for WebGL, Canvas, GPUInfo, and User-Agent, ensuring no contradictions are detectable
• Matching of spoofed GPU vendor and driver with fake IP location, browser language, keyboard layout, and timezone to avoid detection

5. Advanced Detection Resilience:

• FlashID understands heuristic checks from platforms and mimics typical WebGPU timing, structure, and behavior
• If WebGPU access is blocked or disallowed (e.g. by system sandbox or detection engine), FlashID simulates its presence with mock-resolved adapter and fallback capabilities with believable execution latency.

WebGPU is increasingly adopted by tracking vendors due to its clean, structured, GPU-level data exposure. FlashID delivers robust simulation and spoofing support so users can fully change and isolate their WebGPU device profiles, protecting their identity and enabling multi-accounting without increasing signature overlap risk.

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