@inproceedings{kong-etal-2026-malurlbench,
title = "{M}al{URLB}ench: A Benchmark Evaluating Agents' Vulnerabilities When Processing Web {URL}s",
author = "Kong, Dezhang and
Wu, Zhuxi and
Liu, Shiqi and
Tan, ZhiCheng and
Lu, Kuichen and
Li, Minghao and
Liu, Qichen and
Chu, Shengyu and
Xu, Zhenhua and
Liu, Xuan and
Han, Meng",
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.716/",
pages = "14589--14601",
ISBN = "979-8-89176-395-1",
abstract = "LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when processing malicious URLs: accepting a disguised malicious URL enables subsequent access to unsafe webpages, which can cause severe damage to service providers and users. Despite this risk, no benchmark currently targets this emerging threat. To address this gap, we propose MalURLBench, the first benchmark for evaluating LLMs' vulnerabilities to malicious URLs. MalURLBench contains 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites. Experiments with 12 popular LLMs reveal that existing models struggle to detect elaborately disguised malicious URLs. We further identify and analyze key factors that impact attack success rates and propose URLGuard, a lightweight defense module. We believe this work will provide a foundational resource for advancing the security of web agents."
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<abstract>LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when processing malicious URLs: accepting a disguised malicious URL enables subsequent access to unsafe webpages, which can cause severe damage to service providers and users. Despite this risk, no benchmark currently targets this emerging threat. To address this gap, we propose MalURLBench, the first benchmark for evaluating LLMs’ vulnerabilities to malicious URLs. MalURLBench contains 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites. Experiments with 12 popular LLMs reveal that existing models struggle to detect elaborately disguised malicious URLs. We further identify and analyze key factors that impact attack success rates and propose URLGuard, a lightweight defense module. We believe this work will provide a foundational resource for advancing the security of web agents.</abstract>
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%0 Conference Proceedings
%T MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs
%A Kong, Dezhang
%A Wu, Zhuxi
%A Liu, Shiqi
%A Tan, ZhiCheng
%A Lu, Kuichen
%A Li, Minghao
%A Liu, Qichen
%A Chu, Shengyu
%A Xu, Zhenhua
%A Liu, Xuan
%A Han, Meng
%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 kong-etal-2026-malurlbench
%X LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when processing malicious URLs: accepting a disguised malicious URL enables subsequent access to unsafe webpages, which can cause severe damage to service providers and users. Despite this risk, no benchmark currently targets this emerging threat. To address this gap, we propose MalURLBench, the first benchmark for evaluating LLMs’ vulnerabilities to malicious URLs. MalURLBench contains 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites. Experiments with 12 popular LLMs reveal that existing models struggle to detect elaborately disguised malicious URLs. We further identify and analyze key factors that impact attack success rates and propose URLGuard, a lightweight defense module. We believe this work will provide a foundational resource for advancing the security of web agents.
%U https://aclanthology.org/2026.findings-acl.716/
%P 14589-14601
Markdown (Informal)
[MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs](https://aclanthology.org/2026.findings-acl.716/) (Kong et al., Findings 2026)
ACL
- Dezhang Kong, Zhuxi Wu, Shiqi Liu, ZhiCheng Tan, Kuichen Lu, Minghao Li, Qichen Liu, Shengyu Chu, Zhenhua Xu, Xuan Liu, and Meng Han. 2026. MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 14589–14601, San Diego, California, United States. Association for Computational Linguistics.