@inproceedings{bhargude-etal-2025-adaptive,
title = "Adaptive Linguistic Prompting ({ALP}) Enhances Phishing Webpage Detection in Multimodal Large Language Models",
author = "Bhargude, Atharva and
Gonehal, Ishan and
Yoon, Dave and
Brien, Sean O and
Vinnakota, Kaustubh and
Haney, Chandler and
Sandoval, Aaron and
Zhu, Kevin",
editor = "Atwell, Katherine and
Biester, Laura and
Borah, Angana and
Dementieva, Daryna and
Ignat, Oana and
Kotonya, Neema and
Liu, Ziyi and
Wan, Ruyuan and
Wilson, Steven and
Zhao, Jieyu",
booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4pi-1.7/",
doi = "10.18653/v1/2025.nlp4pi-1.7",
pages = "77--85",
ISBN = "978-1-959429-19-7",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models
%A Bhargude, Atharva
%A Gonehal, Ishan
%A Yoon, Dave
%A Brien, Sean O.
%A Vinnakota, Kaustubh
%A Haney, Chandler
%A Sandoval, Aaron
%A Zhu, Kevin
%Y Atwell, Katherine
%Y Biester, Laura
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Ignat, Oana
%Y Kotonya, Neema
%Y Liu, Ziyi
%Y Wan, Ruyuan
%Y Wilson, Steven
%Y Zhao, Jieyu
%S Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-19-7
%F bhargude-etal-2025-adaptive
%X 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.
%R 10.18653/v1/2025.nlp4pi-1.7
%U https://aclanthology.org/2025.nlp4pi-1.7/
%U https://doi.org/10.18653/v1/2025.nlp4pi-1.7
%P 77-85
Markdown (Informal)
[Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models](https://aclanthology.org/2025.nlp4pi-1.7/) (Bhargude et al., NLP4PI 2025)
ACL
- Atharva Bhargude, Ishan Gonehal, Dave Yoon, Sean O Brien, Kaustubh Vinnakota, Chandler Haney, Aaron Sandoval, and Kevin Zhu. 2025. Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models. In Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI), pages 77–85, Vienna, Austria. Association for Computational Linguistics.