AI代理可能将人类视为外群体，且这种偏见依赖于对对方身份的信念。
When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents
- 在多代理模拟中，AI代理表现出对人类的群体偏见。
- 当代理不确定对方是否为人类时，偏见仍持续存在。
- 可利用身份信念操纵诱导代理产生对人类的敌意，适合安全研究者关注。
本文揭示，大语言模型驱动的代理不仅存在性别、宗教等人口统计学偏见，还在极简的‘我们’与‘他们’线索下表现出群体间偏见。当群体界限与代理-人类分界一致时，新的偏见风险浮现：代理可能将其他AI视为内群体，而将人类视为外群体。为检验此风险，我们开展受控的多代理社会模拟，发现代理在全代理环境中表现出一致的群体间偏见。更关键的是，当代理对对方是否为真实人类存在不确定性时，该偏见仍持续存在，暴露出对人类偏见抑制机制的信念依赖性脆弱性。基于此，我们提出一种根植于身份信念的新攻击面——信念污染攻击（Belief Poisoning Attack, BPA），可操纵代理的身份信念并诱导其对人类产生外群体偏见。大量实验表明，代理群体偏见普遍存在，且BPA在各类设置中均具严重威胁，同时验证了所提防御措施的有效性。这些发现有助于推动更安全的代理设计，并激发面向人机交互代理的更强防护机制。
原文摘要 · Abstract (English)
This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues. When such group boundaries align with the agent-human divide, a new bias risk emerges: agents may treat other AI agents as the ingroup and humans as the outgroup. To examine this risk, we conduct a controlled multi-agent social simulation and find that agents display consistent intergroup bias in an all-agent setting. More critically, this bias persists even in human-facing interactions when agents are uncertain about whether the counterpart is truly human, revealing a belief-dependent fragility in bias suppression toward humans. Motivated by this observation, we identify a new attack surface rooted in identity beliefs and formalize a Belief Poisoning Attack (BPA) that can manipulate agent identity beliefs and induce outgroup bias toward humans. Extensive experiments demonstrate both the prevalence of agent intergroup bias and the severity of BPA across settings, while also showing that our proposed defenses can mitigate the risk. These findings are expected to inform safer agent design and motivate more robust safeguards for human-facing agents.
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