Demo: LLM-Based Browser Extension for Phishing Detection

Phishing emails and scam messages pose a significant cybersecurity threat to the public. In this project, we implement a new method of detection and prevention of these cybersecurity threats by using the latest large language models (LLMs). LLMs, like OpenAI, excel in performing text-related tasks, including in-depth sentiment and behavioral analysis of texts. By integrating these LLM features into a browser extension, we aim to provide a user-friendly and accessible threat detection solution that can protect users from phishing attacks.

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Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1109/svcc65277.2025.11133657
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Demo: LLM-Based Browser Extension for Phishing Detection

Eric K. Pham, Hao Chen, T.X. Bui
Spam and Phishing Detection
article

Demo: LLM-Based Browser Extension for Phishing Detection

Eric K. Pham, Hao Chen, T.X. Bui
article en

Abstract

Phishing emails and scam messages pose a significant cybersecurity threat to the public. In this project, we implement a new method of detection and prevention of these cybersecurity threats by using the latest large language models (LLMs). LLMs, like OpenAI, excel in performing text-related tasks, including in-depth sentiment and behavioral analysis of texts. By integrating these LLM features into a browser extension, we aim to provide a user-friendly and accessible threat detection solution that can protect users from phishing attacks.

San Jose State University (US)
Openalex Percentile: Top 67%
Spam and Phishing Detection
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Demo: LLM-Based Browser Extension for Phishing Detection — Eric K. Pham, Hao Chen, et al. · (2025) | TGRS Research Map | TGRS