{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evaluating-large-language-model-based","title":"Evaluating LLM-based Personal Information Extraction and Countermeasures","arxiv_id":"2408.07291","date":"2024-08-14","proceeding":null,"authors":["Yupei Liu","Yuqi Jia","Jinyuan Jia","Neil Zhenqiang Gong"],"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.","url_abs":"https://arxiv.org/abs/2408.07291v3","url_pdf":"https://arxiv.org/pdf/2408.07291v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"evaluating-large-language-model-based","repo_url":"https://github.com/liu00222/llm-based-personal-profile-extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2408.07291","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}