๐ค AI Summary
ๆฌๆ้ๅฏนๅทฅๅ
ทไฝฟ็จๅAIไปฃ็็ๆๆๆถๆ้ฎ้ข๏ผ้่ฟๆๅบไธไธช็ปๆ่ฆๆฑๅๅๅฑๅ่ๆถๆๆฅ่งฃๅณๆๆๅณ็ญ็นใๆง่กๅ้ฎ่ดฃๆบๅถไธ่ถณ็้ฎ้ขใ
๐ Abstract
Tool-using artificial intelligence (AI) agents, systems that autonomously invoke application programming interfaces (APIs), databases, browsers, and inter-agent protocols such as the Model Context Protocol (MCP), are becoming production infrastructure. Yet the security model governing when an agent is authorized to act on a human's behalf remains underdeveloped. Trustworthy human-AI systems require that every consequential agent action be traceable to a human principal, bounded by what that human actually delegated, and contestable after the fact; few documented deployments satisfy all three properties reliably and end to end. Existing literature addresses fragments of this problem in isolation, credential management for non-human identities, classical access control models, prompt injection, and audit trails, while giving little attention to the authorization decision point itself, the moment a tool invocation occurs, and mechanisms that make that decision correct, enforceable, and accountable. This review introduces a principal hierarchy spanning human user, operator/deployer, orchestrator agent, sub-agent, and tool endpoint as an organizing framework, and examines five interdependent layers: agent identity and credential lifecycle; delegation and scope propagation across multi-hop chains; runtime enforcement and just-in-time authorization at policy enforcement points (PEPs); prompt injection as an authorization bypass that breaks the principal hierarchy; and auditability, provenance, and non-repudiation. Drawing on a structured narrative review of 89 primary sources screened from approximately 180 candidates published between 2023 and 2026, we propose seven structural requirements, derive a four-layer reference architecture, apply the requirements to three deployable reference configurations, and identify runtime enforcement and aggregation bounds as the principal unresolved gaps.