# ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection

arXiv: https://arxiv.org/abs/2402.18093 · HTML: https://mlsanity.com/paper/2402.18093/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2024.

## Built on by (most cited first, 25 of 25)

- [Defending Against Social Engineering Attacks in the Age of LLMs](https://mlsanity.com/paper/2406.12263/) (2024)
- [Next-Generation Phishing: How LLM Agents Empower Cyber Attackers](https://mlsanity.com/paper/2411.13874/) (2024)
- [Debate-Driven Multi-Agent LLMs for Phishing Email Detection](https://mlsanity.com/paper/2503.22038/) (2025)
- [Multimodal Large Language Models for Phishing Webpage Detection and Identification](https://mlsanity.com/paper/2408.05941/) (2024)
- [Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm](https://mlsanity.com/paper/2509.07287/) (2025)
- [Zero-Shot Spam Email Classification Using Pre-trained Large Language Models](https://mlsanity.com/paper/2405.15936/) (2024)
- [SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection](https://mlsanity.com/paper/2406.06663/) (2024)
- [Gen-AI for User Safety: A Survey](https://mlsanity.com/paper/2411.06606/) (2024)
- [PreScam: A Benchmark for Predicting Scam Progression from Early Conversations](https://mlsanity.com/paper/2605.12243/) (2026)
- [Improving Phishing Email Detection Performance of Small Large Language Models](https://mlsanity.com/paper/2505.00034/) (2025)
- [Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models](https://mlsanity.com/paper/2505.02362/) (2025)
- [Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability](https://mlsanity.com/paper/2506.13746/) (2025)
- [LLM-Powered Intent-Based Categorization of Phishing Emails](https://mlsanity.com/paper/2506.14337/) (2025)
- [PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants](https://mlsanity.com/paper/2507.15393/) (2025)
- [MeAJOR Corpus: A Multi-Source Dataset for Phishing Email Detection](https://mlsanity.com/paper/2507.17978/) (2025)
- [Small Language Models for Phishing Website Detection: Cost, Performance, and Privacy Trade-Offs](https://mlsanity.com/paper/2511.15434/) (2025)
- [AdaPhish: AI-Powered Adaptive Defense and Education Resource Against Deceptive Emails](https://mlsanity.com/paper/2502.03622/) (2025)
- [MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection](https://mlsanity.com/paper/2505.23803/) (2025)
- [In-context learning for the classification of manipulation techniques in phishing emails](https://mlsanity.com/paper/2506.22515/) (2025)
- [Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models](https://mlsanity.com/paper/2507.13357/) (2025)
- [PhishLumos: An Adaptive Multi-Agent System for Proactive Phishing Campaign Mitigation](https://mlsanity.com/paper/2509.21772/) (2025)
- [Phishing Email Detection Using Large Language Models](https://mlsanity.com/paper/2512.10104/) (2025)
- [SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System](https://mlsanity.com/paper/2601.13581/) (2026)
- [CyberCane: Neuro-Symbolic RAG for Privacy-Preserving Phishing Detection with Formal Ontology Reasoning](https://mlsanity.com/paper/2604.23563/) (2026)
- [AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection](https://mlsanity.com/paper/2609.19873/) (2026)

## Abstract (arXiv)

The proliferation of phishing sites and emails poses significant challenges to existing cybersecurity efforts. Despite advances in malicious email filters and email security protocols, problems with oversight and false positives persist. Users often struggle to understand why emails are flagged as potentially fraudulent, risking the possibility of missing important communications or mistakenly trusting deceptive phishing emails. This study introduces ChatSpamDetector, a system that uses large language models (LLMs) to detect phishing emails. By converting email data into a prompt suitable for LLM analysis, the system provides a highly accurate determination of whether an email is phishing or not. Importantly, it offers detailed reasoning for its phishing determinations, assisting users in making informed decisions about how to handle suspicious emails. We conducted an evaluation using a comprehensive phishing email dataset and compared our system to several LLMs and baseline systems. We confirmed that our system using GPT-4 has superior detection capabilities with an accuracy of 99.70%. Advanced contextual interpretation by LLMs enables the identification of various phishing tactics and impersonations, making them a potentially powerful tool in the fight against email-based phishing threats.