Enhancing Phishing Email Identification with Large Language Models
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Abstract (arXiv)
Phishing has long been a common tactic used by cybercriminals and continues to pose a significant threat in today's digital world. When phishing attacks become more advanced and sophisticated, there is an increasing need for effective methods to detect and prevent them. To address the challenging problem of detecting phishing emails, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. In this work, we take steps to study the efficacy of large language models (LLMs) in detecting phishing emails. The experiments show that the LLM achieves a high accuracy rate at high precision; importantly, it also provides interpretable evidence for the decisions.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Feb 2025.
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Citing papers per year: 2025: 6.
6 papers.
- 2025Debate-Driven Multi-Agent LLMs for Phishing Email Detectioncited by 9
- 2025The Phish, The Spam, and The Valid: Generating Feature-Rich Emails for Benchmarking LLMscited by 3
- 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agentscited by 2
- 2025Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainabilitycited by 1
- 2025MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detectioncited by 1
- 2025Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Modelscited by 0
- 2025The Phish, The Spam, and The Valid: Generating Feature-Rich Emails for Benchmarking LLMs26 Nov 2025
- 2025Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models6 Nov 2025
- 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents20 Oct 2025
- 2025MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection23 Jul 2025
- 2025Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability16 Jun 2025
- 2025Debate-Driven Multi-Agent LLMs for Phishing Email Detection27 Mar 2025