用智能提示提升大模型识别钓鱼网页能力
Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models
- 设计结构化语言提示，分析文本欺骗模式
- 多模态融合检测，F1分数达0.93
- 适合网络安全与AI可解释性研究者
钓鱼攻击是重大网络安全威胁，亟需自适应检测技术。本研究探索在GPT-4o和Gemini 1.5 Pro等先进多模态大语言模型中，采用少样本自适应语言提示（ALP）检测钓鱼网页。ALP是一种结构化语义推理方法，通过分解语言模式、识别紧急信号和操纵性措辞，引导模型分析文本欺骗特征。结合文本、视觉和URL分析，提出统一检测模型。实验表明，ALP通过结构化推理与上下文分析显著提升检测准确率，使多模态大模型的钓鱼检测F1-score达到0.93，优于传统方法。结果证明，集成ALP的多模态大模型有望构建更鲁棒、可解释、自适应的钓鱼检测系统。
原文摘要 · Abstract (English)
Phishing attacks represent a significant cybersecurity threat, necessitating adaptive detection techniques. This study explores few-shot Adaptive Linguistic Prompting (ALP) in detecting phishing webpages through the multimodal capabilities of state-of-the-art large language models (LLMs) such as GPT-4o and Gemini 1.5 Pro. ALP is a structured semantic reasoning method that guides LLMs to analyze textual deception by breaking down linguistic patterns, detecting urgency cues, and identifying manipulative diction commonly found in phishing content. By integrating textual, visual, and URL-based analysis, we propose a unified model capable of identifying sophisticated phishing attempts. Our experiments demonstrate that ALP significantly enhances phishing detection accuracy by guiding LLMs through structured reasoning and contextual analysis. The findings highlight the potential of ALP-integrated multimodal LLMs to advance phishing detection frameworks, achieving an F1-score of 0.93, surpassing traditional approaches. These results establish a foundation for more robust, interpretable, and adaptive linguistic-based phishing detection systems using LLMs.
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