Attacks on Neural Networks

Attacks on neural networks are deliberate attempts to trick or manipulate AI systems. Hackers may input specially crafted data—often called adversarial examples—that cause the neural network to make incorrect decisions. This can lead to misclassification, errors, or security breaches. These attacks exploit vulnerabilities in the model's learned patterns, making the system unreliable or exploitable. Understanding and defending against such attacks is crucial to ensure AI systems remain accurate and secure in real-world applications.