SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents

• URL: http://arxiv.org/abs/2510.10073v1
• Date: Sat, 11 Oct 2025 07:18:12 GMT
• Title: SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents
• Authors: Zonghao Ying, Yangguang Shao, Jianle Gan, Gan Xu, Junjie Shen, Wenxin Zhang, Quanchen Zou, Junzheng Shi, Zhenfei Yin, Mingchuan Zhang, Aishan Liu, Xianglong Liu,
• Abstract summary: Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks.<n>Existing benchmarks provide only partial coverage, typically restricted to narrow scenarios.<n>We present tool, the first holistic benchmark for evaluating the security of LVLM-based web agents.
• Score: 28.60912756104713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks. However, when deployed in real-world environments, they face serious security risks, motivating the design of security evaluation benchmarks. Existing benchmarks provide only partial coverage, typically restricted to narrow scenarios such as user-level prompt manipulation, and thus fail to capture the broad range of agent vulnerabilities. To address this gap, we present \tool{}, the first holistic benchmark for evaluating the security of LVLM-based web agents. \tool{} first introduces a unified evaluation suite comprising six simulated but realistic web environments (\eg, e-commerce platforms, community forums) and includes 2,970 high-quality trajectories spanning diverse tasks and attack settings. The suite defines a structured taxonomy of six attack vectors spanning both user-level and environment-level manipulations. In addition, we introduce a multi-layered evaluation protocol that analyzes agent failures across three critical dimensions: internal reasoning, behavioral trajectory, and task outcome, facilitating a fine-grained risk analysis that goes far beyond simple success metrics. Using this benchmark, we conduct large-scale experiments on 9 representative LVLMs, which fall into three categories: general-purpose, agent-specialized, and GUI-grounded. Our results show that all tested agents are consistently vulnerable to subtle adversarial manipulations and reveal critical trade-offs between model specialization and security. By providing (1) a comprehensive benchmark suite with diverse environments and a multi-layered evaluation pipeline, and (2) empirical insights into the security challenges of modern LVLM-based web agents, \tool{} establishes a foundation for advancing trustworthy web agent deployment.

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