Why Are Machine Learning Models Vulnerable to Adversarial Attacks?

Machine learning (ML) has transformed industries ranging from healthcare and finance to autonomous driving and cybersecurity. It enables systems to learn patterns from data, make predictions, and automate complex tasks. However, as the reliance on machine learning grows, so do concerns about its robustness and security. One of the most significant threats facing ML systems today is adversarial attacks — malicious inputs designed to deceive the model into making incorrect predictions. These attacks expose fundamental vulnerabilities in the way machine learning models operate.

In this blog, we will explore why machine learning models are vulnerable to adversarial attacks, the different types of adversarial attacks, the underlying causes of these vulnerabilities, and the current state of defenses against such threats.

Understanding Machine Learning Models

Before diving into the vulnerabilities, it’s essential to understand how machine learning models function. In simple terms, ML models, especially in supervised learning, work by learning patterns from a large dataset. They use mathematical functions, known as algorithms, to map input data (like images, text, or numbers) to the correct output (such as labels, categories, or predictions).