The Internet of Things (IoT) has expanded beyond consumer smart homes to sectors, such as industrial systems, healthcare, and smart cities, broadening the IoT attack surface. Prior empirical work has focused on studying aggregate IoT vulnerability characteristics or specific protocols, so patching is directed by aggregate security guidance. However, if dominant weaknesses differ substantially across sectors or products, such uniform guidance misdirects the remediation effort. To address this gap, we present a fine-grained, data-driven analysis of IoT vulnerabilities using a dataset of more than 15,000 IoT specific CVEs. We design a pipeline to enrich noisy CPE (Common Platform Enumeration) metadata, normalizing vendors and products, then assigning product type and sector labels using lexical matching, fuzzy clustering, and LLM-assisted classification. We find meaningful differences in the weakness profile of IoT sectors. For example, healthcare is dominated by authentication and credential weaknesses, while automotive faces memory issues. More importantly, we find that weakness profiles are more similar across different product types from the same vendor than across the same product type built by different vendors. This suggests vendor-level factors such as shared code and development practices dominate weakness inheritance, making a vendor's disclosure history informative about its other products. Our findings suggest weakness classes should be used to prioritize remediation efforts.

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