Self-supervised learning (SSL) has emerged as a powerful deep learning paradigm for human activity recognition (HAR) systems. By leveraging large amounts of unlabeled data, SSL enables the development of pre-trained models (PTMs) that can be efficiently fine-tuned with limited labeled data for downstream HAR tasks, reducing labeling costs while improving generalization. Despite these advantages, the potential vulnerabilities of SSL-based PTMs in IoT sensing systems have not been sufficiently explored. This paper investigates data-free backdoor attacks on these PTMs, focusing on a practical scenario where attackers cannot access downstream task data. To realize these attacks, we design a set of triggers and predefined output representations (PORs). By mapping triggers to PORs through backdoor training, we can implant backdoor behaviors into the PTMs, thereby introducing vulnerabilities across different downstream sensing tasks without requiring prior knowledge. Extensive experiments show our attack applies broadly across various sensing modalities, data, and PTM architectures.
