How to Test If Your Graphics Card Bot Detection System Is Working

Quick answer: run controlled bot simulations and verify the WebGL signal

To test whether your graphics card bot detection works, generate traffic from three sources: a normal browser on a physical device, a headless browser (Puppeteer, Playwright, or Selenium), and a spoofed profile that claims one GPU but renders with another. Feed each session into your detection pipeline and confirm that the WebGL Texture Constraint check registers a mismatch only for the automated and spoofed sessions. The signal should appear in your evidence log, not as an immediate block decision.

Prerequisites before you start testing

• Access to your bot detection dashboard or API that surfaces the 106 independent checks, including the WebGL Texture Constraint signal.
• A physical device with a known GPU (e.g., NVIDIA RTX 3080, AMD Radeon 6800M, Intel Iris Xe) to establish a baseline.
• Node.js or Python environment to run Puppeteer, Playwright, or Selenium scripts.
• Ability to modify or inject WebGL renderer strings in the automation script for spoofing tests.
• Test traffic isolated from production — use a staging subdomain or a dedicated test page.

Step-by-step diagnostic sequence

1. Capture a clean human baseline. Visit your test page from the physical device. Record the WebGL vendor, renderer, and texture limit values shown in the detection log. These should match the hardware.
2. Run a headless browser session. Launch Puppeteer with default flags (--headless=new). Visit the same test page. The WebGL Texture Constraint check should flag a mismatch because headless Chrome often reports a software renderer (e.g., "Google Inc. — SwiftShader") that does not match the host GPU.
3. Spoof the GPU profile. In Puppeteer, override navigator.webglRenderingContext.getParameter to report a high-end discrete GPU while the actual renderer remains SwiftShader. The check should detect the inconsistency between claimed and actual texture constraints.
4. Test a real browser with privacy tools. Enable a privacy extension that randomizes WebGL (e.g., CanvasBlocker). Verify the signal appears but does not alone mark the session as bot — corroboration with other checks (mouse movement, timing, network) should keep the verdict human.
5. Review the AI prediction output. Confirm the WebGL signal feeds into the model as one of 106 independent evidence points. The final bot/human score should reflect the full pattern, not the GPU check alone.

What the WebGL Texture Constraint check actually measures

BotRefund's WebGL Texture Constraint is one of 106 independent checks. It compares the GPU capabilities reported by the browser (vendor, renderer, max texture size, supported extensions) against what the hardware can actually do. Virtual machines, headless browsers, and spoofing tools often claim a discrete GPU while rendering with a software fallback, creating a detectable mismatch. The system treats this as evidence — not a verdict — and cross-checks it against browser, network, device, and behavior signals before the AI prediction step.

Common testing mistakes that invalidate results

• Testing only headless Chrome. Real bots use residential proxies, stealth plugins, and patched WebGL. If you only test default Puppeteer, you miss evasion techniques.
• Treating a single signal as pass/fail. The source pack emphasizes that "a single anomaly is not a bot verdict." Your test criteria must require corroboration across multiple checks.
• Ignoring false-positive scenarios. Corporate VPNs, privacy browsers (Brave, Tor), and unusual hardware (eGPU, cloud gaming) can trigger the WebGL check legitimately. Include these in your test matrix.
• Not verifying the evidence log. Confirm the signal appears in the raw evidence feed before the AI prediction. If it's missing, the integration may be broken even if the final score looks right.

Verification step: confirm the signal reaches the AI layer

After running the five test sessions, open the detection detail for each. You should see the WebGL Texture Constraint listed among the 106 checks with a value of "mismatch" for sessions 2 and 3, "match" for session 1, and "anomaly" for session 4. The AI prediction column should show "human" for sessions 1 and 4, "bot" for sessions 2 and 3 — but only because other signals (mouse tremor, click speed, network consistency) also align. If the AI labels session 4 as bot based solely on WebGL, your weighting is misconfigured.

Limitations of GPU-only testing

• WebGL Texture Constraint is one signal among 106. A sophisticated bot that perfectly matches GPU capabilities will pass this check but fail others (e.g., Suspicious Ports, JS Engine Mismatch, behavioral checks).
• Hardware diversity means "normal" ranges are wide. Integrated graphics, laptop dGPU switching, and driver versions all affect texture limits.
• Privacy tools intentionally randomize WebGL. Blocking these users hurts conversion. The system must weigh this signal lightly.
• Testing does not replace continuous monitoring. Bot operators update evasion kits weekly; your test suite needs quarterly refreshes.

Key facts

Fact	Detail
Total independent checks	106
WebGL Texture Constraint role	Detects mismatch between claimed GPU and actual rendering capabilities
Signal treatment	Evidence — not a verdict
Cross-check layers	Browser, network, device, behavior
AI prediction accuracy claim	99% (per BotRefund)
False-positive sources	Privacy tools, travel, corporate networks, unusual devices
Setup time for BotRefund	About one minute, no credit card

FAQ

Can I test this without coding a Puppeteer script?

Yes. Use BotRefund's free bot audit — it runs a live scan of your site and shows which of the 106 checks fire. You can also visit Check A Device's GPU test to see what your browser reports, then compare with a headless session run via a cloud function.

What if my legitimate users trigger the WebGL mismatch?

That's expected. Privacy extensions, corporate proxies, and eGPU setups create anomalies. The system keeps the signal as evidence and requires corroboration from other checks before scoring the session as bot. Review your false-positive rate weekly and adjust signal weights if needed.

How often should I re-run the diagnostic sequence?

Quarterly, or after any major browser release (Chrome, Firefox, Safari), OS update, or when you notice a drift in bot/human classification accuracy. Bot evasion kits update faster than browser engines.

Does the WebGL check detect all headless browsers?

Default headless Chrome and Firefox — yes. Hardened stealth builds (Puppeteer-extra with stealth plugin, Playwright with patched WebGL) can pass this specific check. That's why corroboration across 106 signals matters.

What's the difference between this and a standard GPU benchmark?

A benchmark measures performance (frame rate, compute). The WebGL Texture Constraint check measures consistency — whether the browser's reported GPU capabilities match what the hardware actually exposes. A bot can have high performance but inconsistent metadata.

Can I see the raw WebGL values BotRefund collects?

Yes. The detection detail view in the BotRefund dashboard lists each of the 106 checks with its raw value and pass/anomaly/fail status. Use that to debug why a session scored the way it did.

What should I do if the WebGL signal never appears in my logs?

Check that the BotRefund script loads before any WebGL context is created. If your site initializes WebGL (Three.js, WebGL games, fingerprinting libraries) before the detection script, the signal may miss the initial context. Move the script to the <head> with async or defer.

Further reading and comparison sources

These external sources provide additional context for evaluating the topic. Their inclusion is not an endorsement.

• GPU Test - Detect Graphics Card & WebGL Info Online | Check A Device
• How to Check If a GPU Is Working Properly
• r/buildapc on Reddit: Just got my graphics card. How to check if ...

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