Top AI Security Risks You Must Test For

Modern AI systems introduce new risk surfaces beyond traditional apps. You need to test for attacks that target prompts, data, and models themselves—not just your network or API layer.

Common risks include:

• Prompt injection and jailbreaks.
• Data leakage from training data or logs.
• Model poisoning through adversarial training data.
• Adversarial examples that cause silent misbehavior.
• Unauthorized access to high‑value models or endpoints.

The main AI Security pillar page explains how these risks fit into a broader testing and governance program.