To address the challenging problem of detecting phishing webpages, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. Among these, brand-based phishing detection that uses models from Computer Vision to detect if a given webpage is imitating a well-known brand has received widespread attention. However, such models are costly and difficult to maintain, as they need to be retrained with labeled dataset that has to be regularly and continuously collected. Besides, they also need to maintain a good reference list of well-known websites and related meta-data for effective performance.
In this work, we take steps to study the efficacy of large language models (LLMs), in particular the multimodal LLMs, in detecting phishing webpages. Given that the LLMs are pretrained on a large corpus of data, we aim to make use of their understanding of different aspects of a webpage (logo, theme, favicon, etc.) to identify the brand of a given webpage and compare the identified brand with the domain name in the URL to detect a phishing attack. We propose a two-phase system employing LLMs in both phases: the first phase focuses on brand identification, while the second verifies the domain. We carry out comprehensive evaluations on a newly collected dataset. Our experiments show that the LLM-based system achieves a high detection rate at high precision; importantly, it also provides interpretable evidence for the decisions. Our system also performs significantly better than a state-of-the-art brand-based phishing detection system while demonstrating robustness against two known adversarial attacks.
SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
National University of Defense Technology（国防科技大学）；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）
I can't recognize (yet): Delayed Rendering to Defeat Visual Phishing Detectors
Sapienza University of Rome（罗马第一大学）；University of Padua（帕多瓦大学）；Reykjavik University（雷克雅未克大学）；University of Liechtenstein（列支敦士登大学）；Örebro University（厄勒布鲁大学）
PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool
University of New South Wales（新南威尔士大学）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）；UNSW Institute for Cyber Security（新南威尔士大学网络安全研究所）；CSIRO Data61（联邦科学与工业研究组织Data61）
Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping
The Chinese University of Hong Kong, China（香港中文大学）；Singapore Management University, Singapore（新加坡管理学院）；Renmin University of China, China（中国人民大学）；Tsinghua University, China（清华大学）；Southwest University, China（西南大学）
PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing
Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）；Professor, Dept. of CSE United International University Dhaka, Bangladesh（计算机科学与工程系，国际联合大学，达卡，孟加拉国）
A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features
Department of Cyber Security Science, School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria（网络安全科学系，信息与通信技术学院，联邦科技大学，米纳，尼日利亚）
Continuous Multi-Task Pre-training for Malicious URL Detection and Webpage Classification
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）；Hangzhou Research Institute of Xidian University（西安电子科技大学杭州研究院）；Defence Industry Secrecy Examination and Certification Center（国防工业保密审查与认证中心）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；Yantai Research Institute, Harbin Engineering University（哈尔滨工程大学烟台研究院）
