As biometric systems become ubiquitous in the domain of personal authentication, it is of utmost importance that these systems are secured against attacks. Among various types of attacks on biometric systems, the presentation attack, which involves presenting a fake copy (artefact) of the real biometric to the biometric sensor to gain illegitimate access, is the most common one. Despite the serious threat posed by these attacks, not much work has been done to address this vulnerability in palmprint-based biometric systems. This paper demonstrates the vulnerability of a palmprint verification system to presentation attacks and proposes a novel presentation attack detection (PAD) approach to discriminating between real biometric samples and artefacts. The proposed PAD approach is inspired by a work that established relationship between the surface reflectance and a set of statistical features extracted from the image. Specifically, statistical features computed from the distributions of pixel intensities, sub-band wavelet coefficients and the grey-level co-occurrence matrix form the original feature set, and CFS-based feature selection approach selects the most discriminating feature subset. A trained binary classifier utilizes the selected feature subset to determine whether the acquired image is of real hand or an artefact. For performance evaluation, an antispoofing database—PALMspoof has been developed. This database comprises left- and right-hand images of 104 subjects, and three kinds of artefacts generated from these images. In addition to PALMspoof database, the biometric system’s vulnerability has been assessed on display and print artefacts generated from two publicly available palmprint datasets. Our experimental results show that 1) the palmprint verification system is highly vulnerable with spoof acceptance of 84.56%; 2) the proposed PAD approach is effective against both print and display attacks, in both same-device and cross-device scenarios; and 3) the proposed approach for PAD provides an average improvement of 12.73 percentage points in classification error rate over local binary pattern (LBP)-based PAD approach.