PhishAgent: A Robust Multimodal Agent for Phishing Webpage Detection

Bibliographic Details
Title: PhishAgent: A Robust Multimodal Agent for Phishing Webpage Detection
Authors: Cao, Tri, Huang, Chengyu, Li, Yuexin, Wang, Huilin, He, Amy, Oo, Nay, Hooi, Bryan
Publication Year: 2024
Collection: Computer Science
Subject Terms: Computer Science - Cryptography and Security
More Details: Phishing attacks are a major threat to online security, exploiting user vulnerabilities to steal sensitive information. Various methods have been developed to counteract phishing, each with varying levels of accuracy, but they also face notable limitations. In this study, we introduce PhishAgent, a multimodal agent that combines a wide range of tools, integrating both online and offline knowledge bases with Multimodal Large Language Models (MLLMs). This combination leads to broader brand coverage, which enhances brand recognition and recall. Furthermore, we propose a multimodal information retrieval framework designed to extract the relevant top k items from offline knowledge bases, using available information from a webpage, including logos and HTML. Our empirical results, based on three real-world datasets, demonstrate that the proposed framework significantly enhances detection accuracy and reduces both false positives and false negatives, while maintaining model efficiency. Additionally, PhishAgent shows strong resilience against various types of adversarial attacks.
Comment: Accepted at AAAI 2025 (Oral)
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2408.10738
Accession Number: edsarx.2408.10738
Database: arXiv
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  Data: Phishing attacks are a major threat to online security, exploiting user vulnerabilities to steal sensitive information. Various methods have been developed to counteract phishing, each with varying levels of accuracy, but they also face notable limitations. In this study, we introduce PhishAgent, a multimodal agent that combines a wide range of tools, integrating both online and offline knowledge bases with Multimodal Large Language Models (MLLMs). This combination leads to broader brand coverage, which enhances brand recognition and recall. Furthermore, we propose a multimodal information retrieval framework designed to extract the relevant top k items from offline knowledge bases, using available information from a webpage, including logos and HTML. Our empirical results, based on three real-world datasets, demonstrate that the proposed framework significantly enhances detection accuracy and reduces both false positives and false negatives, while maintaining model efficiency. Additionally, PhishAgent shows strong resilience against various types of adversarial attacks.<br />Comment: Accepted at AAAI 2025 (Oral)
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      – SubjectFull: Computer Science - Cryptography and Security
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      – TitleFull: PhishAgent: A Robust Multimodal Agent for Phishing Webpage Detection
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