Consumers are increasingly delegating purchase decisions to AI agents, providing natural-language descriptions of their preferences and identity. We argue that these representations constitute an information channel, role coherence, through which sellers can infer willingness to pay without explicit disclosure by the buyer agent, leading to preference leakage. In an experiment where a language-model buyer agent shops on behalf of a verbal consumer profile, we show that seller-side inference from dialogue alone recovers willingness to pay nearly one-for-one. Comparing this setting to a numeric-budget condition with confidentiality instructions cleanly isolates role coherence as distinct from instruction-following failure. Because this leakage arises from delegation itself, it cannot be mitigated at the prompt level. Instead, we propose architectural interventions that trade off personalization against preference privacy.

翻译：消费者正越来越多地将购买决策委托给AI代理，通过自然语言描述其偏好与身份特征。我们论证这类表述构成了信息通道——角色一致性——卖家可通过此通道推断支付意愿而无需买家代理明确披露，进而导致偏好泄露。在语言模型买家代理代表口头消费者画像进行购物的实验中，我们表明仅通过对话文本的卖方侧推断即可近乎一比一地还原支付意愿。将此设置与包含保密指令的数字预算条件进行对比，可清晰区分角色一致性与指令遵循失效。由于该泄露源于委托本身，无法在提示层面缓解。为此，我们提出通过架构层面的干预措施来权衡个性化与偏好隐私保护。