arXiv:2606. 19464v1 Announce Type: new Abstract: Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance.
By Anupam Joshi, Tim Finin, Karuna Pande Joshi, Lalana Kagal
The paper proposes a tiered, reusable identity assurance model that separates assurance state from capability gates, allowing participants to disclose only what is necessary for each act. It introduces a typed entity taxonomy, a two‑axis coordinate system for assertion scope and source, and a time‑indexed jurisdiction attribute, with reliance recorded in bitemporal snapshots. The design is evaluated against existing flat‑verification and per‑credential models, addressing cross‑border reuse and data‑erasure versus evidentiary retention concerns.
By Walter Kurz
arXiv:2606. 03518v1 Announce Type: new Abstract: As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the traditional boundaries of software systems blur.
By Amjad Ibrahim, Yong Li
arXiv:2608. 14074v1 Announce Type: new Abstract: AI agents increasingly act on external systems through standardized tool-calling protocols such as the Model Context Protocol (MCP), yet no infrastructure layer constrains their actions to what a principal has verifiably authorized: authorization logic lives in application code, is neither signed nor independently auditable, and the resulting logs lack evidentiary value.
By Giovanni Racioppi
arXiv:2606. 26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf.
By Nada Lahjouji, Ashwin Gerard Colaco
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