<p>Accurate vulnerability prediction is crucial for identifying potential security risks in software, especially in the context of imbalanced and complex real-world datasets. Traditional methods, such as single-task learning and ensemble approaches, often struggle with these challenges, particularly in detecting rare but critical vulnerabilities. To address this, we propose the MTLPT: Multi-Task Learning with Position Encoding and Lightweight Transformer for Vulnerability Prediction, a novel multi-task learning framework that leverages custom lightweight Transformer blocks and position encoding layers to effectively capture long-range dependencies and complex patterns in source code. The MTLPT model improves sensitivity to rare vulnerabilities and incorporates a dynamic weight loss function to adjust for imbalanced data. Our experiments on real-world vulnerability datasets demonstrate that MTLPT outperforms traditional methods in key performance metrics such as recall, F1-score, AUC, and MCC. Ablation studies further validate the contributions of the lightweight Transformer blocks, position encoding layers, and dynamic weight loss function, confirming their role in enhancing the model’s predictive accuracy and efficiency.</p>