  The emergence of large language models (LLMs) has significantly accelerated
the development of a wide range of applications across various fields. There is
a growing trend in the construction of specialized platforms based on LLMs,
such as the newly introduced custom GPTs by OpenAI. While custom GPTs provide
various functionalities like web browsing and code execution, they also
introduce significant security threats. In this paper, we conduct a
comprehensive analysis of the security and privacy issues arising from the
custom GPT platform. Our systematic examination categorizes potential attack
scenarios into three threat models based on the role of the malicious actor,
and identifies critical data exchange channels in custom GPTs. Utilizing the
STRIDE threat modeling framework, we identify 26 potential attack vectors, with
19 being partially or fully validated in real-world settings. Our findings
emphasize the urgent need for robust security and privacy measures in the
custom GPT ecosystem, especially in light of the forthcoming launch of the
official GPT store by OpenAI.
