Vertical Federated Learning (VFL) enables an orchestrating active party to perform a machine learning task by cooperating with passive parties that provide additional task-related features for the same training data entities. While prior research has leveraged the privacy vulnerability of VFL to compromise its integrity through a combination of label inference and backdoor attacks, their effectiveness is constrained by the low label inference precision and suboptimal backdoor injection conditions. To facilitate a more rigorous security evaluation on VFL without these limitations, we propose HASSLE, a hijacking attack framework composed of a gradient-direction-based label inference module and an adversarial embedding generation algorithm enhanced by self-supervised learning. HASSLE accurately identifies private samples associated with a targeted label using only a single known instance of that label. In the two-party scenario, it demonstrates strong performance with an attack success rate (ASR) of over 99% across four datasets, including both image and tabular modalities, and achieves 85% ASR on the more complex CIFAR-100 dataset. Evaluation of HASSLE against 8 potential defenses further highlights its significant threat while providing new insights into building a trustworthy VFL system.