Phishing emails continue to pose a persistent challenge to online communication, exploiting human trust and evading automated filters through realistic language and adaptive tactics. While large language models (LLMs) such as GPT-4 and LLaMA-3-8B achieve strong accuracy in text classification, their deployment in security systems requires assessing reliability beyond benchmark performance. To address this, this study introduces the Trustworthiness Calibration Framework (TCF), a reproducible methodology for evaluating phishing detectors across three dimensions: calibration, consistency, and robustness. These components are integrated into a bounded index, the Trustworthiness Calibration Index (TCI), and complemented by the Cross-Dataset Stability (CDS) metric that quantifies stability of trustworthiness across datasets. Experiments conducted on five corpora, such as SecureMail 2025, Phishing Validation 2024, CSDMC2010, Enron-Spam, and Nazario, using DeBERTa-v3-base, LLaMA-3-8B, and GPT-4 demonstrate that GPT-4 achieves the strongest overall trust profile, followed by LLaMA-3-8B and DeBERTa-v3-base. Statistical analysis confirms that reliability varies independently of raw accuracy, underscoring the importance of trust-aware evaluation for real-world deployment. The proposed framework establishes a transparent and reproducible foundation for assessing model dependability in LLM-based phishing detection.

翻译：钓鱼邮件通过使用逼真的语言和自适应策略，持续利用人类信任并规避自动过滤器，对在线通信构成长期挑战。尽管大语言模型（LLMs）如GPT-4和LLaMA-3-8B在文本分类中表现出高准确率，但其在安全系统中的部署需要评估超越基准性能的可靠性。为此，本研究提出了可信度校准框架（TCF），这是一种可复现的方法论，用于从三个维度评估钓鱼检测器：校准度、一致性和鲁棒性。这些组件被整合为一个有界指数——可信度校准指数（TCI），并辅以跨数据集稳定性（CDS）指标，用于量化跨数据集的可信度稳定性。在SecureMail 2025、Phishing Validation 2024、CSDMC2010、Enron-Spam和Nazario等五个语料库上使用DeBERTa-v3-base、LLaMA-3-8B和GPT-4进行的实验表明，GPT-4具有最强的整体可信度表现，其次是LLaMA-3-8B和DeBERTa-v3-base。统计分析证实，可靠性与原始准确率独立变化，强调了在实际部署中进行可信度感知评估的重要性。所提出的框架为评估基于LLM的钓鱼检测模型的可依赖性建立了一个透明且可复现的基础。