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arXiv · 2610.34640

InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants

Pengrun Huang·Yiwei Fu·Luoxi Meng·Kamalika Chaudhuri
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Abstract

Large language model (LLM)-based personal agents are increasingly deployed to handle everyday tasks on users' behalf. These agents have access to private user information, while their tasks involve communicating with third parties; this raises a critical privacy concern that agents may inappropriately reveal sensitive user information to these recipients. Existing benchmarks primarily assess direct privacy leakage, where an agent explicitly states a pre-specified secret or attribute. However, they do not capture \textit{inferential leakage}, where the model leaves indirect cues in the reply that implicitly leaks sensitive attributes. We introduce \textsc{InferPriv}, a benchmark that measures whether an honest-but-curious recipient can infer a user's sensitive attributes from an LLM's reply on routine task. Evaluating five frontier models, we find that inferential leakage substantially exceeds direct leakage across models and prompts. Under a privacy-aware system prompt, GPT-6.1-Sol, Sonnet-5.5, and Gemini-3.8-Flash achieve near-zero direct leakage, yet the inferential leakage rate remains between 16.2--25.2\%. These findings suggest that evaluating explicit leakage alone can substantially understate the privacy risks.

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Pengrun Huang, Yiwei Fu, Luoxi Meng, Kamalika Chaudhuri. 2026-10-25. InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants. https://arxiv.org/abs/2610.34640

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