AI RESEARCH
AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks
arXiv CS.AI
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ArXi:2604.01487v2 Announce Type: replace With the rise of personalized, persistent LLM agent frameworks such as OpenClaw, human-centered agentic social networks in which teams of collaborative AI agents serve individual users in a social network across multiple domains are becoming a reality. This setting creates novel privacy challenges: agents must coordinate across domain boundaries, mediate between humans, and interact with other users' agents, all while protecting sensitive personal information.