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NIST/CAISI — RFI on security practices for AI agents

• Category: Security

  • What happened: NIST’s Center for AI Standards and Innovation (CAISI) published a Request for Information on how to measure and improve the secure development/deployment of AI agent systems.
  • Threat model (explicit): The notice calls out risks like hijacking, backdoor attacks, and other exploits against agents that take autonomous actions in real-world systems.
  • They want concrete stuff: examples, best practices, case studies, and actionable recommendations from teams building and operating agents.
  • Why this is different: This isn’t generic “AI safety” language — it’s about secure engineering + evaluation methods for tool-using systems.
  • Deadline: comments are due March 9, 2026 (via regulations.gov; docket NIST-2025-0035).
  • Likely downstream impact: procurement checklists and compliance expectations tend to follow NIST framing; agent vendors should expect “show me your evals” questions.
  • Signal for defenders: If you’re running agents in prod, you can treat this as a strong indicator that agent security controls will standardize quickly (auditability, authZ boundaries, logging, red-teaming).

Why it matters

  • Agents turn “prompt bugs” into security bugs: once an LLM can take actions, the failure mode becomes a real incident (data access, configuration changes, payments, tickets, etc.).
  • We need measurable security: the hard part is proving you reduced risk, not just adding guardrails. RFIs like this often shape what “evidence” looks like.
  • Good time to align internally: security teams can use NIST language to negotiate logging, sandboxing, and approvals before agent rollouts become irreversible.

What to do

  1. If you build agents: inventory agent capabilities (tools, permissions, data sources), then write down the actual security boundary for each tool.
  2. Instrument everything: log tool invocations with user identity, parameters, and results; alert on unusual sequences (rapid tool loops, new domains, odd file paths).
  3. Run “agent abuse” evals: create regression tests for prompt injection, indirect injection (content), and privilege escalation via tool-chaining.
  4. Constrain blast radius: least privilege, deny-by-default outbound network, scoped credentials, and explicit approval gates for high-risk actions.
  5. Consider responding: if you have operational experience, submit concrete practices to the RFI — this is how the bar gets set.

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