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:2609.37196v1 Announce Type: cross
Abstract: Tool-using LLM agents remain vulnerable to indirect prompt injection because trusted instructions and untrusted observations share one context, allow...
By Yanjie Li, Xiangyu He, Xuelong Dai, Bin Xiao
arXiv:2607. 21325v2 Announce Type: replace-cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
By M. Llamb\'i-Morillas, D. Fern\'andez-Fern\'andez
arXiv:2607. 21325v1 Announce Type: cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
By M. Llamb\'i-Morillas, D. Fern\'andez-Fern\'andez
arXiv:2607. 21325v3 Announce Type: replace-cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
By M. Llamb\'i-Morillas, D. Fern\'andez-Fern\'andez
arXiv:2608. 15888v1 Announce Type: new Abstract: LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents.
By Xabier Muruaga
arXiv:2605. 27488v2 Announce Type: replace-cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds.
By Qiancheng Wu, Wenhui Zhang, Gan Fang, Sheng Mao, Biao Gao, David Levitsky, Shawna Murphy Butterworth, Rob Cameron
arXiv:2603.17170v2 Announce Type: replace-cross
Abstract: AI agents increasingly execute users' natural-language (NL) tasks by calling Web services, yet today's Web authorizes these calls through OAu...
By Reshabh K Sharma, Linxi Jiang, Shuo Chen, Zhiqiang Lin
arXiv:2609.08062v1 Announce Type: new
Abstract: Tool-using language agents can delegate and revoke permissions while acting through external services. We show that two authorization histories can hav...
By Moonwon Choi, Seokho Jeong, Seunggeun Lee
arXiv:2608.30091v1 Announce Type: new
Abstract: Modern agent frameworks compose planners, tool agents, remote services, and shared specialists into runtime delegation graphs, but their revocation API...
By Lifei Liu, Haoran Yu, Xiaochong Jiang
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:2609.22573v1 Announce Type: cross
Abstract: LLM agents translate natural-language context, which may include attacker-controlled text, into privileged tool calls, so authorization must remain e...
By Huan Li, Yuwei Wang, Srinivasan Manoharan