Friday 25 July 2025
The war on phishing is a constant and evolving battle, with cybercriminals constantly adapting their tactics to evade detection. In this cat-and-mouse game, researchers are always looking for new ways to stay one step ahead of the attackers. A recent paper proposes an innovative approach to phishing detection using large language models (LLMs) in a multi-agent framework.
Phishing websites have become increasingly sophisticated, often mimicking legitimate sites with convincing URLs and brand impersonation. Traditional methods relying on rule-based systems or machine learning algorithms are no longer sufficient. To combat this, the authors of this study developed PhishDebate, a novel approach that leverages LLMs to analyze different aspects of a webpage.
The framework consists of four specialized agents: URL structure, HTML composition, semantic content, and brand impersonation. Each agent analyzes its respective aspect of the page independently before presenting its findings to a Moderator. The Moderator then evaluates the evidence from each agent and makes a final determination about whether the website is phishing or legitimate.
In addition to the individual agent responses, PhishDebate also incorporates a Judge who reviews the entire debate history and makes a final decision. This multi-agent approach allows for more accurate and interpretable results, as the system can identify inconsistencies and contradictions between agents’ findings.
The authors evaluated PhishDebate using a real-world phishing dataset and compared its performance to single-agent classification models and Chain of Thought (CoT) baselines. The results showed that PhishDebate achieved an impressive 98.2% recall and 98.2% True Positive Rate (TPR), outperforming the other methods.
PhishDebate’s modular design also allows for agent-level configurability, enabling adaptation to varying resource and application requirements. This flexibility makes it a promising solution for organizations seeking to integrate advanced phishing detection into their security protocols.
The study highlights the potential of LLMs in combating sophisticated phishing attacks. By leveraging the strengths of multiple agents, PhishDebate demonstrates that even complex threats can be effectively identified using innovative machine learning approaches. As cybercriminals continue to evolve their tactics, researchers like those behind PhishDebate will need to stay ahead of the curve to keep users safe online.
The paper’s findings offer a glimpse into the future of phishing detection, where AI-powered systems can work in tandem to identify and prevent malicious activity.
Cite this article: “PhishDebate: A Multi-Agent Framework for Effective Phishing Detection Using Large Language Models”, The Science Archive, 2025.
Large Language Models, Phishing Detection, Multi-Agent Framework, Url Structure, Html Composition, Semantic Content, Brand Impersonation, Machine Learning Algorithms, Phishdebate, Artificial Intelligence
