In this paper, we apply formal dialogue methods to recognize and analyze phishing in interactions. Phishing attacks exploit human vulnerabilities, typically through deceptive and manipulative messages, leading victims to disclose sensitive information. Existing machine learning-based detection methods often lack transparency, making it difficult to trace manipulation tactics. We utilize the so-called Goal-Hiding Dialogue (GHD) framework, originally designed for reasoning about non-collaborative agents in information-seeking dialogues. The framework employs Quantitative Bipolar Argumentation Frameworks (QBAFs) to model how a seeker agent strategically influences a target’s willingness to engage with certain topics. Our approach provides a mathematically grounded method for identifying key conversational shifts where manipulation occurs, contributing to phishing detection and evidence analysis.