LiL vulnerabilities are more severe than ecosystem and conventional bugs and drop LLM-based repair Pass@1 by ~10.8%, with three categories often at 0% success.
Opening a pandora’s box: things you should know in the era of custom gpts
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
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.
years
2026 2representative citing papers
A qualitative study of 21 U.S. GenAI users reveals that existing security and privacy transparency is perceived as ineffective and lacking credibility, leading users to rely on proxies like popularity and constraining high-stakes use.
citing papers explorer
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Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities
LiL vulnerabilities are more severe than ecosystem and conventional bugs and drop LLM-based repair Pass@1 by ~10.8%, with three categories often at 0% success.
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Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI
A qualitative study of 21 U.S. GenAI users reveals that existing security and privacy transparency is perceived as ineffective and lacking credibility, leading users to rely on proxies like popularity and constraining high-stakes use.