Which NIST Generative AI Profile suggested actions matter most for securing LLM agents?

The NIST Generative AI Profile highlights several critical areas for securing LLM agents, emphasizing information security, data privacy, and supply chain integrity. Addressing these risks involves implementing robust controls such as input/output scrubbing, provenance tracking, and comprehensive incident response planning.

Key actions for securing LLM agents, derived from the NIST Generative AI Profile and related standards, include:

• Information Security (OWASP LLM01 Prompt Injection): Implement input/instruction separation, establish trust boundaries for retrieved content and tool outputs, and assume models can be steered when handling outputs. Additionally, apply least-privilege tool access, incorporate human-in-the-loop for high-impact actions, and conduct adversarial testing. Output schema validation is a cheap and effective runtime check to interrupt attacks where prompt injection leads to malformed responses.
• Data Privacy (OWASP LLM02 Sensitive Information Disclosure): Employ input/output scrubbing and redaction, minimize data in context, strictly scope RAG sources, ensure tenant isolation, use Data Loss Prevention (DLP) on responses, and avoid placing secrets directly in prompts. Architectures should support per-tenant data residency, configurable redaction at ingestion, and retention aligned with regulatory regimes for logs.
• Value-chain & Component Integration (OWASP LLM03 Supply Chain): Maintain model and dataset provenance and licensing records, use signed artifacts, create an SBOM for the AI stack, vet plugins and MCP tools, and pin component versions. This also involves admission control for skills, MCP servers, and plugins, ensuring nothing runs until scanned.
• Incident Response & Post-Deployment Monitoring (NIST-MANAGE-4.1): Establish an AI/agent incident-response plan covering detection, escalation, containment, communication, and learning. This includes comprehensive instrumentation by default, tamper-evident audit logs, and out-of-band shipping to a SIEM. Procedures for deactivating, rolling back, or safely retiring AI systems that exceed risk tolerances are also crucial.
• Tool Misuse and Unsafe Tool Calls: Implement tool-call validation gates, including schema validation, allowlisted tools/actions, and parameter constraints. Intent re-verification before any consequential action helps ensure the action aligns with the originally attested intent, even if the agent's reasoning has been corrupted.