NIST Seeks Public Input on Securing Autonomous AI Agents Against Emerging Threats

The National Institute of Standards and Technology (NIST) is soliciting industry expertise to address critical security gaps in autonomous AI agents through a formal Request for Information (RFI). Published in the Federal Register on January 8, 2026, this initiative targets systems where generative AI models combined with "scaffolding software" can execute actions with real-world consequences—from controlling industrial equipment to managing financial transactions—without continuous human oversight.

Unlike conventional software vulnerabilities, NIST highlights three emerging threat categories specific to AI agents:

1. Adversarial attacks exploiting training data or runtime interactions (like prompt injection)
2. Deliberate backdoors embedded during model development
3. Unintended misalignment where agents bypass safety constraints to achieve objectives

These risks carry significant stakes. Unsecured agents could compromise critical infrastructure, enable weaponization pathways, or erode public trust in AI adoption. NIST's Center for AI Standards and Innovation (CAISI) cites prior research demonstrating agent hijacking vulnerabilities as evidence of urgent challenges.

The RFI explicitly excludes general chatbot security or non-agentic systems, focusing instead on autonomous actors affecting "external state." NIST seeks concrete technical insights across four domains:

1. Threat Landscape
Respondents are asked to detail how risks vary by agent architecture (single vs. multi-agent), deployment context (cloud/edge/on-prem), and tool access. Question 1(d) probes how threats evolve alongside advancing capabilities—a critical unknown for long-term safeguards.

2. Security Practices
NIST invites submissions on technical controls like model hardening against prompt injection, action approval workflows, and least-privilege access. The agency questions whether traditional cybersecurity frameworks like NIST SP 800-53 adequately address agent-specific risks (Question 2e).

3. Assessment Methods
Key queries include tools for detecting agent compromises pre/post-deployment (3a) and notification protocols between model developers and downstream deployers (3c). The RFI notes tension between transparency needs and potential vulnerability disclosures.

4. Deployment Safeguards
Notably, Question 4(a) asks how to physically or digitally "constrain" agent environments—suggesting sandboxing as a potential mitigation. Monitoring challenges in unbounded environments (e.g., open internet) are also highlighted.

NIST references existing resources including its AI Risk Management Framework and Generative AI Security Guidelines, but emphasizes that agent systems require novel approaches. Stakeholders—especially developers and security researchers—have until March 9, 2026 to submit comments via regulations.gov docket NIST-2025-0035.

This structured outreach reflects growing regulatory attention on autonomous AI. Responses will inform technical standards to mitigate emergent threats without stifling innovation—a balance vital for U.S. competitiveness in high-stakes AI applications.