Adaptive Linguistic Prompting ( ALP ) Enhances Phishing Webpage Detection in Multimodal Large Language Models (2025)

Abstract: Phishing attacks represent a significant cybersecurity threat, necessitating adaptive detection techniques. This study explores few-shot Adaptive Linguistic Prompting (ALP) in detecting phishing webpages through the multimodal capabilities of state-of-the-art large language models (LLMs) such as GPT-4o and Gemini 1.5 Pro. ALP is a structured semantic reasoning method that guides LLMs to analyze textual deception by breaking down linguistic patterns, detecting urgency cues, and identifying manipulative diction commonly found in phishing content. By integrating textual, visual, and URL-based analysis, we propose a unified model capable of identifying sophisticated phishing attempts. Our experiments demonstrate that ALP significantly enhances phishing detection accuracy by guiding LLMs through structured reasoning and contextual analysis. The findings highlight the potential of ALP-integrated multimodal LLMs to advance phishing detection frameworks, achieving an F1-score of 0.93—surpassing traditional approaches. These results establish a foundation for more robust, interpretable, and adaptive linguistic-based phishing detection systems using LLMs.

## introduction with over 1.2 million attempts blocked in 2024 alone @xcite , phishing remains a persistent cybersecurity threat as attackers continuously refine their tactics to evade detection @xcite . traditional detection methods, such as heuristic url matching and brand verification, often falter against novel evasion tactics @xcite . while earlier work @xcite compared modality contributions using zero-shot brand + domain verification, we instead focus on how prompt engineering alone can enhance phishing * lead author † senior author detection performance using multimodal large language models (mllms). the emergence of large language models (llms) and multimodal large language models (mllms), represents a paradigm shift in phishing detection @xcite , offering fine-grained semantic analysis. these mllms, including gpt-4o and gemini 1.5 pro, enable joint reasoning over html, screenshots, and urls @xcite . in this paper, we introduce adaptive linguistic prompting (alp), a method inspired by fewshot prompting @xcite and structured reasoning @xcite . it is a few-shot prompting framework that guides llms to perform structured, modality-specific reasoning using curated exemplars rather than zeroshot templates. our contributions include: (1) a refined 8-shot prompting framework (prompt-enhanced alp) for html + screenshot and url analysis, and (2) design insights-such as a "suspicious-first" url heuristic-that improve f1 (0.91 vs. 0.93), demonstrating the impact of prompt tuning independent of model changes. ## related works traditional phishing detection relies on heuristics like url analysis, html structure analysis, and blacklisting @xcite . while effective for known threats, these methods fail against zero-day attacks and sophisticated mimicry tactics @xcite . machine learning (ml)-based models extract statistical patterns to improve detection @xcite , but adversarial perturbations and dynamic content limit their effectiveness @xcite . recent phishing detection methods use computer vision to spot brand imitation via logos and page layout @xcite @xcite , however these models require continuous retraining. @xcite proposed a multimodal llm approach integrating brand identification and domain verification to detect phishing inconsistencies. their findings show llms outperform traditional methods in accuracy and robustness but face challenges like prompt injection and high computational costs (divakaran and peddinti 2024). other models, like chatspamdetector @xcite , also leverage llms. our research advances this field by integrating few-shot alp prompting to refine linguistic feature extraction and reasoning, reducing dependence on continuous retraining while strengthening phishing detection. ## methodology for the experiments, we selected the gpt-4o and the gemini 1.5 pro models for their advanced multi-modal analysis architectures, both of which effectively correlate verbose textual content and visual inputs @xcite . this is crucial for phishing detection, where identifying malicious sites often requires analyzing a combination of html content, syntactic patterns, and screenshots. ## results and discussion the results demonstrate the efficacy of multimodal phishing detection modified by adaptive linguistic prompting (alp) over heuristic zeroshot prompting methods seen in our baseline @xcite . the performance assessment revealed different strengths between gpt-4o and gemini 1.5 pro across mwa, usa, and the combined analysis. mwa outperformed static approaches by detecting advanced deception tactics, particularly brand mimicking. gemini 1.5 pro performed well in identifying brand representation anomalies due to its comprehensive analysis of visual elements and textual consistency markers. usa demonstrated complementary strengths, with gpt-4o excelling in domain authenticity and url pattern analysis, identifying suspicious structures and security protocol inconsistencies. the gpt-4o's combined analysis achieved a precision of 91.67%, recall of 94.12%, and an f1-score of 0.93, while gemini 1.5 pro achieved 91% precision, 92% recall, and an f1-score of 0.91. these results suggest an optimized phishing detection framework could integrate both models, with gpt-4o for usa and gemini for mwa to maximize accuracy. while testing usa, we incorporated a "suspicious-first" prompting strategy into usa, as seen below. ## conclusion our study demonstrates that adaptive linguistic prompting (alp) enhances phishing detection by guiding multimodal llms to systematically analyze brand, linguistic, visual, and url-based deception tactics. by integrating semantic reasoning and few-shot prompting, alp addresses critical gaps in traditional heuristic methods, which struggle with nuanced phishing cues (examples in appendix a.1). the combined analysis func-tion achieved an f1-score of 0.93, a significant improvement over the baseline and past literature, highlighting alp's ability to harmonize detection modalities while prioritizing risk-averse classification. our findings establish alp as a robust, interpretable, and scalable solution, reducing reliance on continuous retraining.