Rendering on Real Silicon: GPU Render-Timing as a Passive, AI-Resistant CAPTCHA Signal

Abstract:Conventional CAPTCHAs pose puzzles that modern AI systems increasingly solve, while behavioral and cryptographic-attestation defenses carry privacy or enrollment costs. We investigate an orthogonal signal: the physical timing behavior of a client's GPU under a controlled WebGL rendering workload. Unlike WebGL fingerprinting, which hashes pixel output into a static device identifier, we measure render-timing dynamics to classify rather than identify, leaking no persistent identifier. We characterize the in-the-wild adversary with a 12-hour passive deployment (207 unsolicited requests; 86% automated; 85% of browser-claiming clients failed HTTP header-consistency checks). We then collect labeled GPU-timing samples through a single public endpoint exercised by real browsers (positive class, 13 distinct GPUs) and by keyed headless automation across a render-backend matrix (negative class). Software-rendered automation -- empirically the dominant real-world adversary -- separates from genuine GPUs by roughly 5x in mean render time. On a confound-controlled comparison (identical GPU family and browser engine, differing only in headless vs. interactive execution), headless automation on real hardware still exhibits a distinct timing signature, separating from human samples by 75-106% on frame jitter, timer-quantization ratio, and coefficient of variation. We report these as pilot-scale findings on a single GPU architecture and outline the cross-architecture collection required to establish generalization.

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2607.23389 [cs.CR]

(or arXiv:2607.23389v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.23389 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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