Evaluating LLM-based Personal Information Extraction and Countermeasures

• URL: http://arxiv.org/abs/2408.07291v3
• Date: Fri, 31 Jan 2025 05:16:50 GMT
• Title: Evaluating LLM-based Personal Information Extraction and Countermeasures
• Authors: Yupei Liu, Yuqi Jia, Jinyuan Jia, Neil Zhenqiang Gong,
• Abstract summary: Large language model (LLM) based personal information extraction can be benchmarked.<n>LLM can be misused by attackers to accurately extract various personal information from personal profiles.<n> prompt injection can defend against strong LLM-based attacks, reducing the attack to less effective traditional ones.
• 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 four datasets including a synthetic dataset generated by GPT-4 and three real-world datasets with manually labeled eight categories of personal information; introduce a novel mitigation strategy based on prompt injection; and systematically benchmark LLM-based attacks and countermeasures using ten LLMs and five datasets. Our key findings include: LLM can be misused by attackers to accurately extract various personal information from personal profiles; LLM outperforms traditional methods; and prompt injection can defend against strong LLM-based attacks, reducing the attack to less effective traditional ones.

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