Vertical federated learning (VFL) has made significant strides in enhancing data privacy and security for cross-silo applications. However, despite its benefits, VFL remains vulnerable to emerging security threats, particularly backdoor attacks. While most existing research on VFL backdoor attacks has focused on image and natural language processing tasks, the security of tabular data—commonly used in high-risk domains such as finance and healthcare—has been largely overlooked. In this paper, we introduce chamaeleon, a novel backdoor attack targeting VFL for tabular data. Our approach achieves two key advancements. First, to address the challenge of restricted label access in VFL, chamaeleon employs a two-step inference method to extract label information. This method combines a label classifier with a top-kconfidence filtering mechanism, enabling the precise identification of target-label samples (i.e., backdoored samples) with a precision of approximately 99.85%. Second, to overcome the limitations of fixed trigger patterns, which can disrupt the semantic integrity of tabular data (e.g., altering “male” to “pregnant”), chamaeleon introduces a dynamic trigger design. Each backdoored sample is injected with a unique trigger, generated by a transformer-based model inspired by large language models, ensuring semantic consistency. Additionally, a one-on-two adversarial game is implemented to optimize the generator’s performance with limited training data. Extensive evaluations across six models and six datasets demonstrate the effectiveness of our proposed attack. We also examine various factors that could influence the attack success and systematically analyze potential defense mechanisms to mitigate this newly identified threat.

Text-based person search is a challenging crossmodal retrieval task. Existing works reduce the inter-modality and intra-class gaps by aligning local features extracted from image and text modalities, which easily lead to mismatching problems due to the lack of annotation information. Besides, it is sub-optimal to reduce two gaps simultaneously in the same feature space. This work proposes a novel joint token and feature alignment framework to reduce the inter-modality and intraclass gaps progressively. Specifically, we first build a dual-path feature learning network to extract features and conduct feature alignment to reduce the inter-modality gap. Second, we design a text generation module to generate token sequences using visual features, and then token alignment is performed to reduce the intra-class gap. Last, a fusion interaction module is introduced to further eliminate the modality heterogeneity using the strategy of multi-stage feature fusion. Extensive experiments on the CUHKPEDES dataset demonstrate the effectiveness of our model, which significantly outperforms previous state-of-the-art methods.