The paper examines the security challenges of delegating authority to autonomous LLM agents that act on users’ behalf. It introduces a threat model with four adversaries and eight security requirements, demonstrates that current frameworks (LangGraph, CrewAI, AutoGen, MCP) fail to meet these standards, and presents an authorization broker that blocks all identified threats with minimal overhead. The broker is shown to resist numerous attacks and limits compromised sub‑agents to their delegated tasks, and its principles are implemented in VotalAI’s LLM Shield.
By Panduranga Sai Varma Dantuluri, Jyotirmoy Sundi
arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.
By Yuchen Ling, Shengcheng Yu, Zhenyu Chen, Chunrong Fang
arXiv:2609.00595v1 Announce Type: cross
Abstract: Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating...
By Rui Yang, Junjie Xu, Zhengyu Liu, Neil Fendley, Yang Hong, Ziyang Li, Yinzhi Cao
arXiv:2607. 10487v1 Announce Type: cross Abstract: LLM agents can commit durable effects from authority evidence that was valid earlier in execution: a DOM snapshot, approval epoch, version witness, branch token, or worker result.
By Igor Santos-Grueiro
arXiv:2606. 09549v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents face two distinct security failures: unauthorized external actions and exposure of sensitive plaintext inside the runtime before any final output check can intervene.
By Yuhan Ma, Stefan Schmid
arXiv:2608.29381v1 Announce Type: cross
Abstract: AI agents are moving toward persistent, stateful execution across various applications, accumulating execution state and external effects that are co...
By Guanlong Wu, Dahui Li, Ke Jiang, Jianyu Niu, Cong Wang, Yinqian Zhang