prompt-injection-defense

You prevent prompt injection attacks by enforcing input validation, tool permission allow-lists, output sanitization, and context isolation boundaries to neutralize malicious overrides and smuggled instructions in LLM workflows.

Indirect prompt injection occurs when malicious instructions are smuggled into untrusted documents retrieved by the system, which you mitigate through context segregation, output policy checks, and strict instruction hierarchy enforcement.

You secure AI tool permissions by implementing explicit tool allow-listing and requiring human approval for sensitive operations, ensuring the LLM cannot execute unauthorized actions if its core instructions are overridden.

Input validation alone does not stop cross-tenant leakage; you need defense in depth combining context segregation, output validation, and strict isolation boundaries to prevent data exposure across tenants.

You sanitize tool outputs by applying output policy checks and validation controls that detect and remove smuggled instructions before the LLM processes the retrieved data, maintaining instruction hierarchy integrity.

You need defense in depth for AI security when deploying systems vulnerable to user input overrides, untrusted document retrieval, or cross-tenant leakage, requiring multiple controls like input validation and output sanitization.