As generative AI (GenAI) agents become more common in enterprise settings, they introduce security challenges that differ significantly from those posed by traditional systems. These agents are not just LLMs; they reason, remember, and act, often with minimal human oversight. This paper introduces a comprehensive threat model tailored specifically for GenAI agents, focusing on how their autonomy, persistent memory access, complex reasoning, and tool integration create novel risks. This research work identifies 9 primary threats and organizes them across five key domains: cognitive architecture vulnerabilities, temporal persistence threats, operational execution vulnerabilities, trust boundary violations, and governance circumvention. These threats are not just theoretical they bring practical challenges such as delayed exploitability, cross-system propagation, cross system lateral movement, and subtle goal misalignments that are hard to detect with existing frameworks and standard approaches. To help address this, the research work present two complementary frameworks: ATFAA - Advanced Threat Framework for Autonomous AI Agents, which organizes agent-specific risks, and SHIELD, a framework proposing practical mitigation strategies designed to reduce enterprise exposure. While this work builds on existing work in LLM and AI security, the focus is squarely on what makes agents different and why those differences matter. Ultimately, this research argues that GenAI agents require a new lens for security. If we fail to adapt our threat models and defenses to account for their unique architecture and behavior, we risk turning a powerful new tool into a serious enterprise liability.

本研究聚焦于生成型人工智能（GenAI）代理在企业环境中带来的独特安全挑战，填补了传统系统未能解决的新兴威胁空白。文章提出了一种专门针对GenAI代理的全面威胁模型，并识别了9种主要威胁，强调了代理的自主性、持久记忆、复杂推理及工具集成所带来的新风险，并提供了ATFAA和SHIELD两个框架，以降低企业风险，凸显了对GenAI代理安全性的新视角的重要性。

保障自主人工智能：生成型人工智能代理的全面威胁模型与缓解框架