SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
New systematisation identifies how individually safe LLM agents create emergent security failures when composed — directly relevant as EU enterprises pilot agentic AI under AI Act scrutiny.
Summary written by editorial AI · Source link below
arXiv:2609.00595v1 Announce Type: new Abstract: Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating failures that local checks may miss. Without an execution-level view, a multi-agent setting can easily be mistaken for evidence of a genuinely multi-agent security effect. We thus systematize MAS security through an execution-centered analysis of 197 works, covering six interaction interfaces, four ad
Editorial Analysis
As enterprises move from single-model deployments to orchestrated multi-agent systems, trust-boundary violations between agents become a new attack surface that traditional AI safety evaluations do not cover.
Mandate multi-agent threat modelling that maps data flow, authority delegation, and state mutation across all agent boundaries before any agentic system enters production.
Composing individually safe AI agents can create emergent security failures — governance must cover the system, not just its parts.
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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