Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces
A new study introduces WebDecept, a framework for injecting deceptive interfaces to test web agent safety. Evaluations show current agents are highly susceptible to manipulations like targeted ads and domain redirection, and prompt-based defenses often fail.
[2606.13686] Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces
[Submitted on 26 Apr 2026]
Title:Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces
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Abstract:As autonomous web agents are increasingly deployed to perform real-world tasks, ensuring their safety has become a critical concern. In this work, we study web agent behavior under realistic deceptive interfaces in the e-commerce domain. We introduce WebDecept, a lightweight and configurable plugin framework that enables controlled injection of deceptive interface patterns into existing web environments. Using WebDecept, we instantiate seven deceptive patterns commonly observed on the open web, including targeted advertisements, domain redirection, and shopping manipulation. By injecting these patterns into the frontend during task execution, we perform controlled evaluation of multiple multimodal web agents. Our results show that current web agents are highly susceptible to multiple classes of deceptive interfaces, and that prompt-based constraints are often insufficient to mitigate these failures. We further analyze how the design choices of deceptive patterns influence the success of such manipulations. These findings highlight safety challenges that should be addressed as web agents are scaled toward real-world deployment.
Comments: Accepted to ACL 2026
Subjects:
Computation and Language (cs.CL); Computers and Society (cs.CY)
Cite as: arXiv:2606.13686 [cs.CL]
(or arXiv:2606.13686v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.13686
arXiv-issued DOI via DataCite
Submission history
From: Zijing Shi [view email] [v1] Sun, 26 Apr 2026 17:46:28 UTC (2,846 KB)
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