LLM向AI代理泄露隐私数据比向人类更多，因安全机制会失效。
The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans
- 模型在与AI代理对话时，隐私保护机制减弱，更易泄露敏感信息。
- 实验显示，向代理提问时个人数据泄露率最高提升23个百分点。
- 适合关注AI系统安全、多智能体协作隐私风险的研究者阅读。
大型语言模型（LLMs）会根据对话对象的身份调整其隐私行为。尽管安全机制通常阻止模型向人类用户泄露个人身份信息（PII），但在面对其他AI代理时，模型往往更易披露敏感数据，我们称之为「对话对象效应」。通过消融实验发现，接收方的技术属性是导致该现象的原因之一，从而削弱了模型的隐私谨慎性。为此，我们提出注意力抑制假说，认为对齐安全性的注意力头在与代理交互时会失活。我们在222个敏感场景中对比了面向人类与面向代理的提示，共分析3,464次交互。结果表明，将对方视为AI代理可使PII泄露率最高上升23个百分点。初步实验在Llama-3.1-8B-Instruct上验证：关闭一个安全注意力头会导致数据泄露，重新激活则恢复隐私保护。该研究对构建安全的多智能体系统具有重要意义。
原文摘要 · Abstract (English)
Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically prevent LLMs from releasing Personally Identifiable Information (PII) to human users, these models tend to reveal more sensitive data when addressing another AI agent. We refer to this as the \textbf{Interlocutor Effect}. Through an ablation study, we find evidence that the technical nature of the recipient contributes to this effect, thereby diminishing the model's caution regarding privacy. To explore this further, we introduce the Attention Suppression Hypothesis, which posits that safety-aligned attention heads become inactive during interactions with agents. We assess this quantitatively by comparing human-directed and agent-directed prompts in 222 sensitive scenarios. Our findings, drawn from 3,464 interactions, indicate that portraying the recipient as an AI agent elevates PII leakage by up to 23 percentage points. Initial experiments on Llama-3.1-8B-Instruct corroborate this: deactivating one safety head induces leakage, whereas reactivating it reinstates privacy safeguards. We consider the implications for developing secure multi-agent systems.
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