arXiv AI By Lifei Liu, Haoran Yu, Xiaochong Jiang

VERA: Authority-Preserving Edge Revocation for Federated AI-Agent Workflows

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arXiv AI
Sep 2

Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems

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 AI
Sep 15

AcquireBound: Runtime Authorization for Resources Acquired by AI Agents

AcquireBound is a runtime authorization framework that ensures AI agents can safely acquire and activate resources such as compute, credentials, and services. It quarantines acquired outputs, resolves their capabilities through authenticated evidence, and activates them only after verifying a manifest, provenance, and relational constraints. The system demonstrates strong safety properties, passing extensive benign and unsafe trace tests across multiple resource classes.

By Genliang Zhu