# Debate-Driven Multi-Agent LLMs for Phishing Email Detection

arXiv: https://arxiv.org/abs/2503.22038 · HTML: https://mlsanity.com/paper/2503.22038/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 6 of 6)

- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://mlsanity.com/paper/2402.18272/) (2024)
- [ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection](https://mlsanity.com/paper/2402.18093/) (2024)
- [Enhancing Phishing Email Identification with Large Language Models](https://mlsanity.com/paper/2502.04759/) (2025)
- [Phishing Attacks Detection -- A Machine Learning-Based Approach](https://mlsanity.com/paper/2201.10752/) (2022)

## Built on by (most cited first, 9 of 9)

- [A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes](https://mlsanity.com/paper/2601.05293/) (2026)
- [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026)
- [PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection](https://mlsanity.com/paper/2506.15656/) (2025)
- [Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection](https://mlsanity.com/paper/2601.07232/) (2026)
- [Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges](https://mlsanity.com/paper/2607.26212/) (2026)
- [Improving Phishing Email Detection Performance of Small Large Language Models](https://mlsanity.com/paper/2505.00034/) (2025)
- [Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models](https://mlsanity.com/paper/2511.04728/) (2025)
- [Prompt Injection Detection for Email Agents Through Attack Chain Modeling](https://mlsanity.com/paper/2609.30657/) (2026)
- [Collaborative Principle Evolution via Evidence Transfer for Scientific Discovery](https://mlsanity.com/paper/2609.35315/) (2026)

## Abstract (arXiv)

Phishing attacks remain a critical cybersecurity threat. Attackers constantly refine their methods, making phishing emails harder to detect. Traditional detection methods, including rule-based systems and supervised machine learning models, either rely on predefined patterns like blacklists, which can be bypassed with slight modifications, or require large datasets for training and still can generate false positives and false negatives. In this work, we propose a multi-agent large language model (LLM) prompting technique that simulates debates among agents to detect whether the content presented on an email is phishing. Our approach uses two LLM agents to present arguments for or against the classification task, with a judge agent adjudicating the final verdict based on the quality of reasoning provided. This debate mechanism enables the models to critically analyze contextual cue and deceptive patterns in text, which leads to improved classification accuracy. The proposed framework is evaluated on multiple phishing email datasets and demonstrate that mixed-agent configurations consistently outperform homogeneous configurations. Results also show that the debate structure itself is sufficient to yield accurate decisions without extra prompting strategies.