Evaluating Large Language Model based Personal Information Extraction and Countermeasures

• URL: http://arxiv.org/abs/2408.07291v1
• Date: Wed, 14 Aug 2024 04:49:30 GMT
• Title: Evaluating Large Language Model based Personal Information Extraction and Countermeasures
• Authors: Yupei Liu, Yuqi Jia, Jinyuan Jia, Neil Zhenqiang Gong,
• Abstract summary: Large language model (LLM) can be misused by attackers to accurately extract various personal information from personal profiles. LLM outperforms conventional methods at such extraction. prompt injection can mitigate such risk to a large extent and outperforms conventional countermeasures.
• Score: 63.91918057570824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatically extracting personal information--such as name, phone number, and email address--from publicly available profiles at a large scale is a stepstone to many other security attacks including spear phishing. Traditional methods--such as regular expression, keyword search, and entity detection--achieve limited success at such personal information extraction. In this work, we perform a systematic measurement study to benchmark large language model (LLM) based personal information extraction and countermeasures. Towards this goal, we present a framework for LLM-based extraction attacks; collect three datasets including a synthetic dataset generated by GPT-4 and two real-world datasets with manually labeled 8 categories of personal information; introduce a novel mitigation strategy based on \emph{prompt injection}; and systematically benchmark LLM-based attacks and countermeasures using 10 LLMs and our 3 datasets. Our key findings include: LLM can be misused by attackers to accurately extract various personal information from personal profiles; LLM outperforms conventional methods at such extraction; and prompt injection can mitigate such risk to a large extent and outperforms conventional countermeasures. Our code and data are available at: \url{https://github.com/liu00222/LLM-Based-Personal-Profile-Extraction}.

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