Foundations of AI & Cybersecurity - Lesson 39: Identifying Direct Model-Targeted Attacks

This chapter explains seven early warning signs that an AI system may be compromised, misused, or drifting away from safe and reliable behavior. It covers key indicators such as hallucinations, output integrity attacks, sensitive data disclosure, insecure output handling, excessive agency, overreliance, and model skewing. The main point is that securing AI requires continuous monitoring of behavior, not just traditional perimeter defenses.

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