Executive Summary

AI agents that autonomously browse the web, manage email, execute code, and query databases are being deployed across federal agencies at accelerating pace. These agents create a qualitatively new attack surface: a poisoned email can cause an agent to exfiltrate files; a malicious code comment can redirect an execution pipeline; a hidden webpage instruction can hijack a browsing session. Yet cybersecurity education has not kept pace. Existing AI security tools (Lakera Gandalf, OWASP LLM labs, CTF competitions) teach only chatbot-level prompt injection, a single-turn, text-only model that does not prepare students for agent-mediated threats involving tool access, autonomous action, and multi-step reasoning chains.

This project develops AgentSec, a scenario-based educational platform that teaches undergraduate cybersecurity students to identify, exploit, and defend against threats unique to autonomous AI agents, organized into three thrusts. Thrust 1 creates the educational framework, where a trust boundary taxonomy organizes agent vulnerabilities into three progressive categories, i.e., data ingestion, tool-action, and reasoning chain boundaries. This gives students a transferable mental model. Based on the taxonomy, we design a three-module curriculum with six organization-contextualized scenarios, red-team/blue-team experiential learning cycles, and a configurable difficulty framework. Thrust 2 builds the educational technology, where we develop sandboxed environments to enable students’ interaction with real LLM-powered agents with controlled tool access, a layered assessment system with an AI tutoring agent, an instructor analytics dashboard, and a scenario authoring toolkit for community-contributed content. Thrust 3 evaluates impact through a quasi-experimental study, assessing platform quality, student learning, and workforce placement into government and industry CyberAI roles. All code will be open-source, self-hostable via Docker Compose with open-weight models, and released through CLARK for nationwide adoption.