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Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models

arXiv 2507.13357 ↗First posted 29 Jun 2025cs.CLcited by 0

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Abstract (arXiv)

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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References · 7

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 207
  4. 2024KnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detectioncited by 33
  5. 2024ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detectioncited by 25
  6. 2024LLMs for Cyber Security: New Opportunitiescited by 11
  7. 2024Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Modelscited by 5

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  1. 2025Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
  2. 2025Phishing Email Detection Using Large Language Models
  3. 2026Clouding the Mirror: Stealthy Prompt Injection Attacks Targeting LLM-based Phishing Detection
  4. 2025Can MLLMs Detect Phishing? A Comprehensive Security Benchmark Suite Focusing on Dynamic Threats and Multimodal Evaluation in Academic Environments
  5. 2025Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models
  6. 2026Eliciting Least-to-Most Reasoning for Phishing URL Detection
  7. 2024Multimodal Large Language Models for Phishing Webpage Detection and Identification
  8. 2025PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection