<p>The growing prevalence of software vulnerabilities has increased the need for effective detection methods, particularly in cross-project settings where domain differences create significant challenges. Existing vulnerability detection models often struggle to generalise across projects due to variations in coding styles, feature distributions, and the absence of labelled target data. This paper presents ZSVulD, a zero-shot, cross-project vulnerability detection framework designed to operate without target-domain labels. ZSVulD uses domain-agnostic CodeBERT embeddings to capture both syntactic and semantic features of source code, enabling knowledge transfer between projects. The framework applies an iterative pseudo-labelling process in which a neural network and XGBoost classifier collaboratively refine predictions for the target domain. Feature alignment is incorporated as a diagnostic technique to assess and visualise distributional differences between source and target datasets. Experiments on the Devign and REVEAL datasets show that ZSVulD achieves higher recall, F1, and F2 scores compared to existing methods, with an emphasis on reducing false negatives. These findings indicate that ZSVulD can support automated vulnerability detection pipelines, contributing to more reliable security assessments across different software projects.</p>