Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
Empirical gap analysis shows that identity, authorization, and runtime governance in multi-agent LLM systems remain fundamentally unsolved, posing escalating risks as agentic deployments grow.
Summary written by editorial AI · Source link below
arXiv:2609.00267v1 Announce Type: new Abstract: Autonomous LLM agents increasingly act on a user's behalf: they hold credentials, call tools and services, and spawn sub-agents that act further on their behalf. This turns a long-standing distributed-systems question -- who is authorized to do what, on whose authority -- into an urgent and largely unsolved problem, because the component driving each agent is a language model an adversary can hijack. We argue that agent security must be evaluated
Editorial Analysis
Enterprises deploying agentic AI are inheriting distributed-systems authorization debt that, left unaddressed, could enable lateral privilege escalation across agent chains.
Before scaling agentic LLM deployments, define explicit delegation policies and implement runtime governance guardrails for sub-agent credential use.
Agentic AI systems create novel authorization risks that existing identity frameworks do not cover — proactive governance investment is needed before scale.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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