@inproceedings{du-etal-2026-automated,
title = "Automated Profile Inference with Language Model Agents",
author = "Du, Yuntao and
Li, Zitao and
Ding, Bolin and
Li, Yaliang and
Xiao, Hanshen and
Zhou, Jingren and
Li, Ninghui",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.485/",
pages = "9977--10000",
ISBN = "979-8-89176-395-1",
abstract = "Impressive progress has been made in automated problem-solving by the collaboration of large language model (LLM) based agents. However, these automated capabilities also open avenues for malicious applications. In this paper, we study a new threat that LLMs pose to online pseudonymity, called automated profile inference, where an adversary can instruct LLMs to automatically collect and extract sensitive personal attributes from publicly available user activities on pseudonymous platforms. We also introduce an automated profiling framework called AutoProfiler to demonstrate and assess the feasibility of such attacks in real-world scenarios. AutoProfiler consists of four specialized LLM agents that work collaboratively to retrieve and process user online activities and generate a profile with extracted personal information. Experimental results on two real-world datasets and one synthetic dataset show that AutoProfiler is highly effective and efficient, and the inferred attributes are both identifiable and sensitive, posing significant privacy risks. We explore mitigation strategies from different perspectives and advocate for increased public awareness of this emerging privacy threat."
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<abstract>Impressive progress has been made in automated problem-solving by the collaboration of large language model (LLM) based agents. However, these automated capabilities also open avenues for malicious applications. In this paper, we study a new threat that LLMs pose to online pseudonymity, called automated profile inference, where an adversary can instruct LLMs to automatically collect and extract sensitive personal attributes from publicly available user activities on pseudonymous platforms. We also introduce an automated profiling framework called AutoProfiler to demonstrate and assess the feasibility of such attacks in real-world scenarios. AutoProfiler consists of four specialized LLM agents that work collaboratively to retrieve and process user online activities and generate a profile with extracted personal information. Experimental results on two real-world datasets and one synthetic dataset show that AutoProfiler is highly effective and efficient, and the inferred attributes are both identifiable and sensitive, posing significant privacy risks. We explore mitigation strategies from different perspectives and advocate for increased public awareness of this emerging privacy threat.</abstract>
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%0 Conference Proceedings
%T Automated Profile Inference with Language Model Agents
%A Du, Yuntao
%A Li, Zitao
%A Ding, Bolin
%A Li, Yaliang
%A Xiao, Hanshen
%A Zhou, Jingren
%A Li, Ninghui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F du-etal-2026-automated
%X Impressive progress has been made in automated problem-solving by the collaboration of large language model (LLM) based agents. However, these automated capabilities also open avenues for malicious applications. In this paper, we study a new threat that LLMs pose to online pseudonymity, called automated profile inference, where an adversary can instruct LLMs to automatically collect and extract sensitive personal attributes from publicly available user activities on pseudonymous platforms. We also introduce an automated profiling framework called AutoProfiler to demonstrate and assess the feasibility of such attacks in real-world scenarios. AutoProfiler consists of four specialized LLM agents that work collaboratively to retrieve and process user online activities and generate a profile with extracted personal information. Experimental results on two real-world datasets and one synthetic dataset show that AutoProfiler is highly effective and efficient, and the inferred attributes are both identifiable and sensitive, posing significant privacy risks. We explore mitigation strategies from different perspectives and advocate for increased public awareness of this emerging privacy threat.
%U https://aclanthology.org/2026.findings-acl.485/
%P 9977-10000
Markdown (Informal)
[Automated Profile Inference with Language Model Agents](https://aclanthology.org/2026.findings-acl.485/) (Du et al., Findings 2026)
ACL
- Yuntao Du, Zitao Li, Bolin Ding, Yaliang Li, Hanshen Xiao, Jingren Zhou, and Ninghui Li. 2026. Automated Profile Inference with Language Model Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 9977–10000, San Diego, California, United States. Association for Computational Linguistics.