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As phishing emails pose a growing threat to individuals and organizations alike, there is an urgent need to develop more accurate detection methods. Large Language Models (LLMs) have recently garnered major attention in this line of research; however, they often require large-scale data for fine-tuning, which is impractical in real-world application scenarios. This paper proposes DRILL, a new simple and efficient mechanism, for dual-reasoning LLMs to detect phishing emails with extremely small data. DRILL distills the reasoning ability from an LLM into a target small LM model, while integrating trainable perturbations to manipulate the inputs, which in turn adaptively enhances the inference ability of the target LM. Extensive experiments are conducted on multiple real-world email datasets, and the evaluation results demonstrate that DRILL can benefit from dual LMs, which significantly reduces training parameters and data required, while maintaining state-of-the-art performance in phishing email detection with limited data.