The Granular Gambit: Checkmating Behavior-based CAPTCHAs

Behavior-based CAPTCHAs such as Google reCAPTCHA v3 enable frictionless verification by assigning risk scores from fine-grained user interaction traces. Prior studies, however, largely rely on coarse-grained analyses and fail to capture the atomic behavioral signals underlying v3’s scoring mechanism. This work examines whether agentic AI can synthesize human-like low-level interactions to obtain high trust scores and bypass reCAPTCHA v3. The problem is formulated as sequential decision-making over atomic behaviors, and Seqion, an LLM-driven agentic framework for fine-grained web interaction execution, is introduced. Experiments on a controlled reCAPTCHA v3 testbed, including registration, forum posting, and content reading tasks, show that agentic AI can consistently increase trust scores and achieve up to a 90% bypass success rate, even without domain-specific knowledge. Furthermore, evaluations on an external, real-world website reveal that Seqion achieves a mean trust score of 0.60, significantly outperforming the state-of-the-art baseline of 0.34 and closely approximating the genuine human average of 0.69. These results expose fundamental limitations of frictionless CAPTCHA systems under rapidly advancing agentic AI.