#Research
"How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-based Phishing Detection Models", 2025.
// This study investigates how to effectively leverage LLMs for phishing detection by examining the impact of input modalities (screenshots, logos, HTML, URLs), temperature settings, and prompt engineering strategies. We evaluate seven LLMs - two commercial models (GPT 4.1, Gemini 2.0 flash) and five open-source models (Qwen, Llama, Janus, DeepSeek-VL2, R1) - alongside two DL-based baselines (PhishIntention and Phishpedia). Our findings reveal that commercial LLMs generally outperform open-source models in phishing detection, while DL models demonstrate better performance on benign samples
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