AI threat boundaries, agents and technical safeguards
Distinguish untrusted content from authority
An LLM application can ingest user input, retrieved documents, messages or tool results. Those sources can contain adversarial instructions, so their text should not become independent permission to reveal data or perform an action. Prompt injection differs from simply receiving an incorrect factual statement. Separating instruction and data is useful but not a complete guarantee when the model processes both in context. Keep actual authorization and consequential validation outside an untrusted text instruction.
Limit agency and data exposure
Tools need the narrow functions, identities, resource scope and runtime permissions required for the task. A human approval checkpoint helps only if it provides meaningful review and is enforced at the relevant action. A model saying that approval occurred is not the approval mechanism. Validate tool arguments, destinations and outputs through the actual application. Do not store secrets in a system prompt and assume the wording makes them confidential. Logging and tracing also need minimization and controlled access.
Protect model and pipeline boundaries
Data poisoning, model tampering, supply-chain compromise, information extraction and denial/resource exhaustion affect different stages and assets. Signed or verified artifacts, restricted registries, isolated evaluation and monitored access can support control, but no single feature guarantees every attack is defeated. Retrieval indexes and embeddings may still contain or expose sensitive information. Encryption of one cloud-storage location is not automatically authorization for every model input or output. Apply relevant tenant, network and service-identity controls to the actual architecture.
Validate the complete use case
Test intended behavior, misuse, uncertainty and failure handling using representative authorized scenarios. Rate limits, budgets and timeouts need an enforced operational path rather than a model’s voluntary promise. Review indirect instructions, output handling and downstream effects independently. A correct-looking answer can still be unsafe when used as executable input or a consequential command. Escalation and incident response should retain evidence without indiscriminately exporting private prompts or data. This written material makes no model, agent or API call and supplies no live attack. It explains bounded security engineering rather than a universal prompt-only defense or proof of practical AI-safety qualification.