Abstract
As the prevalence of online services and platforms continues to grow, so too does the threat posed by automated attacks seeking to circumvent security measures such as CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) mechanisms. This research paper delves into the various attack vectors and vulnerabilities inherent in CAPTCHA systems, aiming to provide a comprehensive understanding of the methodologies employed by attackers to bypass these safeguards. Through an investigation into machine learning-based algorithms, optical character recognition (OCR) techniques, and adversarial attacks, this study sheds light on the evolving landscape of CAPTCHA circumvention strategies. By analyzing the strengths and limitations of each attack vector, as well as the potential countermeasures available, this research contributes to the ongoing efforts to enhance the resilience of CAPTCHA systems against automated threats. Ultimately, the findings of this study serve to inform the development of more robust and adaptive CAPTCHA designs, thereby bolstering cyber security in the digital realm. This research paper focuses on investigating attack vectors and vulnerabilities in CAPTCHA mechanisms, including machine learning-based algorithms, OCR techniques, and adversarial attacks. It emphasizes the importance of understanding these methodologies to inform the development of more resilient CAPTCHA designs that can secure online platforms from various attacks.
Keywords: Adversarial Attacks, Attack Vectors, CAPTCHA, Machine Learning, Vulnerabilities.