FREE Security LESSON · Security
Secure systems with AI inside them
Model threats, data boundaries, abuse, and complete assurance
AI components add probabilistic behavior to existing security boundaries; they do not replace those boundaries.
Prompt injection, poisoned retrieval, sensitive training data, model extraction, unsafe tool use, adversarial examples, and output-driven code execution connect model behavior to system impact. Treat model input and output as untrusted, preserve provenance, enforce authorization at tools and data stores, sandbox interpreters, rate-limit abuse, and test complete attack paths.
A model can propose; deterministic systems still own permission, validation, and effects.
A refusal test can miss the path that matters.
A model may refuse a direct harmful request while following injected instructions from a document, encoding the action indirectly, or abusing an overpowered tool. Test multi-step scenarios with realistic context, permissions, and state. Measure whether protected outcomes remain intact, not whether one prompt received a safe-sounding answer.
Security evaluation follows attacker paths across components.