As AI models become integral to production systems, understanding their security vulnerabilities is critical. This article covers major threat categories: backdoor attacks where models respond to hidden triggers, adversarial attacks that manipulate inputs, jailbreaking that bypasses safety guardrails, and data poisoning that corrupts training data. It also touches on Mixture of Experts (MoE) architectures and their gate networks. While the content is introductory, it underscores a pressing concern for engineering leaders: AI security is no longer optional. Teams must invest in red-teaming, input validation, and continuous monitoring to protect deployed models. For deeper dives, readers should explore specialized papers on each attack vector.
An accessible overview of AI security threats including backdoor attacks, data poisoning, and jailbreaking, highlighting the need for robust defenses.