Robot Visions: Breaking reCAPTCHA at Zero Cost and Zero Shot
Google reCAPTCHA is the most widely deployed visual CAPTCHA service, protecting hundreds of thousands of websites from automated bots. It serves as a critical line of defense against automated attacks, including credential stuffing, bulk account creation, and automated form abuse. It has proven largely effective since its introduction in 2007. However, the rise of accessible AI now threatens its efficacy. Prior work has demonstrated that commercial cloud-based vision-language models (VLMs) can solve visual CAPTCHA challenges, but at non-trivial monetary cost per attempt. In this paper, we show that free and locally-run models can break Google reCAPTCHA. We conduct a comprehensive study of reCAPTCHA and present a taxonomy of its challenge types: Type A (independent image tiles, with static and dynamic sub-variants) and Type B (a single image partitioned into a 4x4 grid), each demanding a distinct solving strategy. We design zero-shot, no-cost solvers built entirely on open-source local models, specifically CLIP (58% per-challenge accuracy on Type A) and OWLv2 (43.5% on Type B), requiring no model training and no API access. Our end-to-end automated solver achieves a 92.6% per-session success rate across 500 real-world reCAPTCHA sessions. We further demonstrate that reCAPTCHA can be defeated by a non-technical adversary, using only natural-language instructions to a commodity AI assistant. This collapses the practical attacker skill floor to near zero and fundamentally changes the threat model for challenge-based CAPTCHAs. Although reCAPTCHA increasingly favors reputation-based verification, visual challenge-based fallback persists as a safety net that, paradoxically, has become the weakest link in the defense chain, suggesting that challenge-based visual CAPTCHAs may have reached the end of their useful life.