AgentSocialBench：评估以人为中心的代理社交网络中的隐私风险

AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks

April 1, 2026

作者: Prince Zizhuang Wang, Shuli Jiang

cs.AI

摘要

随着OpenClaw等个性化、持久化大语言模型智能体框架的兴起，以人类为中心的智能体化社交网络正逐渐成为现实。在这种网络中，由协作AI智能体组成的团队可为社交网络中的个体用户提供跨领域服务。这一场景催生了新型隐私挑战：智能体需在跨领域协调、人际中介以及与其他用户智能体交互的同时，保护敏感个人信息。尽管已有研究评估过多智能体协调与隐私保护机制，但以人类为中心的智能体化社交网络的动态特性与隐私风险尚未得到探索。为此，我们推出首个系统性评估该场景隐私风险的基准框架AgentSocialBench，其包含基于具有分层敏感度标签和定向社交图的真实用户画像，覆盖双向及多方互动的七类场景。实验表明，智能体化社交网络中的隐私保护远难于单智能体场景：（1）即使明确要求智能体保护信息，跨领域跨用户的协调仍会形成持续性信息泄露压力；（2）指导智能体抽象化敏感信息的隐私指令反而会加剧相关讨论（我们称之为抽象化悖论）。这些发现表明，当前大语言模型智能体在以人类为中心的智能体化社交网络中缺乏稳健的隐私保护机制，要实现智能体中介式社交协调的安全部署，亟需突破提示工程范畴的新方法。

English

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. While prior work has evaluated multi-agent coordination and privacy preservation, the dynamics and privacy risks of human-centered agentic social networks remain unexplored. To this end, we introduce AgentSocialBench, the first benchmark to systematically evaluate privacy risk in this setting, comprising scenarios across seven categories spanning dyadic and multi-party interactions, grounded in realistic user profiles with hierarchical sensitivity labels and directed social graphs. Our experiments reveal that privacy in agentic social networks is fundamentally harder than in single-agent settings: (1) cross-domain and cross-user coordination creates persistent leakage pressure even when agents are explicitly instructed to protect information, (2) privacy instructions that teach agents how to abstract sensitive information paradoxically cause them to discuss it more (we call it abstraction paradox). These findings underscore that current LLM agents lack robust mechanisms for privacy preservation in human-centered agentic social networks, and that new approaches beyond prompt engineering are needed to make agent-mediated social coordination safe for real-world deployment.