# Enhancing Phishing Email Identification with Large Language Models

arXiv: https://arxiv.org/abs/2502.04759 · HTML: https://mlsanity.com/paper/2502.04759/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [Debate-Driven Multi-Agent LLMs for Phishing Email Detection](https://mlsanity.com/paper/2503.22038/) (2025)
- [The Phish, The Spam, and The Valid: Generating Feature-Rich Emails for Benchmarking LLMs](https://mlsanity.com/paper/2511.21448/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability](https://mlsanity.com/paper/2506.13746/) (2025)
- [MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection](https://mlsanity.com/paper/2507.17978/) (2025)
- [Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models](https://mlsanity.com/paper/2511.04728/) (2025)

## 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.