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The widespread use of Large Language Model (LLM)-based conversational agents(CAs), especially in high-stakes domains, raises many privacy concerns.Building ethical LLM-based CAs that respect user privacy requires an in-depthunderstanding of the privacy risks that concern users the most. However,existing research, primarily model-centered, does not provide insight intousers' perspectives. To bridge this gap, we analyzed sensitive disclosures inreal-world ChatGPT conversations and conducted semi-structured interviews with19 LLM-based CA users. We found that users are constantly faced with trade-offsbetween privacy, utility, and convenience when using LLM-based CAs. However,users' erroneous mental models and the dark patterns in system design limitedtheir awareness and comprehension of the privacy risks. Additionally, thehuman-like interactions encouraged more sensitive disclosures, whichcomplicated users' ability to navigate the trade-offs. We discuss practicaldesign guidelines and the needs for paradigmatic shifts to protect the privacyof LLM-based CA users.

"It's a Fair Game'', or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents