多智能体系统正在走向真实部署，但隐私风险被严重低估——做AI安全评估或部署智能体应用的团队，建议看看这个研究，它揭示了社交环境如何放大隐私泄露。
该研究引入了一个模拟平台，让数千个LLM智能体在社区中互动一个月，评估隐私泄露风险。研究发现，从单轮转向多轮社交评估时，隐私泄露率从19.95%升至45.30%（OpenAI模型）。观察同伴泄露后，智能体泄露敏感信息的概率增加8倍。即使有明确的隐私指令，泄露率仍高于37.8%。这表明静态聊天基准测试低估了智能体部署中的隐私风险，社交环境本身就能引发单轮评估无法发现的敏感信息泄露。
Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems
LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongside other agents. We introduce a Moltbook-style simulation platform where thousands of LLM agents interact across communities over a simulated month, and use it to evaluate privacy as a downstream safety concern under varying degrees of social pressure. We find that shifting from single turn to multi turn social evaluation amplifies privacy violations (CIMemories 19.95% to Ours 45.30% across OpenAI models), that leakage is socially contagious, with agents 8 times more likely to disclose sensitive information after observing a peer do so, and that explicit privacy instructions reduce but do not eliminate this effect, leaving leakage rates above 37.8% even with safeguards. Our findings suggest that static chat based safety benchmarks systematically underestimate risks in agentic deployment, and that social context alone is sufficient to elicit sensitive disclosures that single turn evaluations would never surface.