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

• URL: http://arxiv.org/abs/2507.13357v1
• Date: Sun, 29 Jun 2025 01:26:25 GMT
• Title: Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models
• Authors: Atharva Bhargude, Ishan Gonehal, Chandler Haney, Dave Yoon, Kevin Zhu, Aaron Sandoval, Sean O'Brien, Kaustubh Vinnakota,
• Abstract summary: This study explores few-shot Adaptive Linguistic Prompting (ALP) in detecting phishing webpages.<n>ALP is a structured semantic reasoning method that guides large language models (LLMs) to analyze textual deception.<n>Our experiments demonstrate that ALP significantly enhances phishing detection accuracy.
• Score: 3.266109137396354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models [1.4999444543328293]
  Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency.<n>This paper presents a comparative evaluation of traditional Machine Learning (ML), Deep Learning (DL), and quantized small- parameter Large Language Models (LLMs) for phishing detection.<n>We show that while LLMs currently underperform compared to ML and DL methods in terms of raw accuracy, they exhibit strong potential for identifying subtle, context-based phishing cues.
  arXiv  Detail & Related papers  (2025-07-10T04:01:52Z)
• Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors [65.27124213266491]
  We propose textbfContrastive textbfParaphrase textbfAttack (CoPA), a training-free method that effectively deceives text detectors.<n>CoPA constructs an auxiliary machine-like word distribution as a contrast to the human-like distribution generated by large language models.<n>Our theoretical analysis suggests the superiority of the proposed attack.
  arXiv  Detail & Related papers  (2025-05-21T10:08:39Z)
• Context-Enhanced Vulnerability Detection Based on Large Language Model [17.922081397554155]
  We propose a context-enhanced vulnerability detection approach that combines program analysis with large language models.<n>Specifically, we use program analysis to extract contextual information at various levels of abstraction, thereby filtering out irrelevant noise.<n>Our goal is to strike a balance between providing sufficient detail to accurately capture vulnerabilities and minimizing unnecessary complexity.
  arXiv  Detail & Related papers  (2025-04-23T16:54:16Z)
• Exploring Gradient-Guided Masked Language Model to Detect Textual Adversarial Attacks [50.53590930588431]
  adversarial examples pose serious threats to natural language processing systems.<n>Recent studies suggest that adversarial texts deviate from the underlying manifold of normal texts, whereas masked language models can approximate the manifold of normal data.<n>We first introduce Masked Language Model-based Detection (MLMD), leveraging mask unmask operations of the masked language modeling (MLM) objective.
  arXiv  Detail & Related papers  (2025-04-08T14:10:57Z)
• Debate-Driven Multi-Agent LLMs for Phishing Email Detection [0.0]
  We propose a multi-agent large language model (LLM) prompting technique that simulates deceptive debates among agents to detect phishing emails.<n>Our approach uses two LLM agents to present arguments for or against the classification task, with a judge agent adjudicating the final verdict.<n>Results show that the debate structure itself is sufficient to yield accurate decisions without extra prompting strategies.
  arXiv  Detail & Related papers  (2025-03-27T23:18:14Z)
• EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability [44.2907457629342]
  EXPLICATE is a framework that enhances phishing detection through a three-component architecture.<n>It is on par with existing deep learning techniques but has better explainability.<n>It addresses the critical divide between automated AI and user trust in phishing detection systems.
  arXiv  Detail & Related papers  (2025-03-22T23:37:35Z)
• Towards General Visual-Linguistic Face Forgery Detection(V2) [90.6600794602029]
  Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust.<n>Recent works demonstrate that leveraging multimodal models can enhance the generalization and interpretability of face forgery detection.<n>We propose Face Forgery Text Generator (FFTG), a novel annotation pipeline that generates accurate text descriptions by leveraging forgery masks for initial region and type identification.
  arXiv  Detail & Related papers  (2025-02-28T04:15:36Z)
• Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework [9.976099891796784]
  Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement. Existing detection methods, which mainly rely on single-feature analysis and binary classification, often fail to effectively identify LLM-generated text in academic contexts. We propose a novel Multi-level Fine-grained Detection framework that detects LLM-generated text by integrating low-level structural, high-level semantic, and deep-level linguistic features.
  arXiv  Detail & Related papers  (2024-10-18T07:25:00Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• Red Teaming Language Model Detectors with Language Models [114.36392560711022]
  Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. Recent works have proposed algorithms to detect LLM-generated text and protect LLMs. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
  arXiv  Detail & Related papers  (2023-05-31T10:08:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.