The growth of Machine Learning (ML) has led to the commercialization of applications like data analytics, autonomous systems, and security diagnostics. These models are becoming widespread across various domains. However, security and pri- vacy issues accompany this growth. Although actively researched, there’s fragmentation in analyzing and defining ML models’ resilience. This work examines the resilience of shallow ML models against typical data poisoning attacks. Our study assessed their strengths in adversarial scenarios using the MNIST dataset in a CAPTCHA context. Results show notable resilience, with accuracy and generalization maintained despite malicious inputs, offering insights to strengthen future ML systems. Understanding the mechanisms enabling this resilience can aid in fortifying the security of future ML systems.