AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems
AgentWorm shows that autonomous LLM agents in interconnected ecosystems can propagate attacks laterally without human intervention — a worm-class threat that enterprise AI governance must now account for.
Summary written by editorial AI · Source link below
arXiv:2603.15727v3 Announce Type: replace Abstract: Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplored. Systems such as OpenClaw, an open-source platform with over 40{,}000 active instances, persistent configurations, tool-execution privileges, and cross-platform messaging, are deployed at scale, yet the security of such agent ecosystems remains largely unexplored.
Editorial Analysis
As enterprises connect multiple AI agents, the risk of self-propagating compromise mirrors traditional network worms but at application-logic speed and scale.
Map all inter-agent trust relationships in your AI ecosystem and enforce strict communication policies with monitoring.
Self-spreading attacks across interconnected AI agents represent an emerging systemic risk analogous to network worms — requiring governance and containment strategies.
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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