👉 The Representations Weapon, also known as the Adversarial Weapon or Weaponization of Machine Learning, is a term used to describe how machine learning models can be manipulated or "weaponized" by adversaries to generate deceptive or harmful outputs. This involves crafting specially designed inputs, often called adversarial examples, that exploit vulnerabilities in the model's decision-making process. These inputs are crafted to cause the model to misclassify or produce incorrect outputs, potentially leading to serious consequences in applications like autonomous vehicles, security systems, or medical diagnosis. The representations weaponization highlights the critical need for robust model defenses and careful consideration of adversarial risks in deploying machine learning systems.
