The field of software supply chain security and large language model ecosystems is undergoing significant developments, with a growing focus on understanding and mitigating risks associated with complex supply chains. Researchers are working to develop novel approaches for analyzing and benchmarking supply chain security, including the use of empirical studies and automated tools. One key area of innovation is in the detection of license incompatibilities and vulnerabilities in software dependency networks. Another area of focus is on the development of comprehensive datasets and benchmarks for evaluating the security of large language model applications. These advances have the potential to significantly improve the security and trustworthiness of software systems and large language model-enabled applications. Notable papers in this area include: HuggingGraph, which introduces a method for systematically collecting and analyzing large language model supply chain data, and PyPitfall, which provides a quantitative analysis of vulnerable dependencies across the PyPI ecosystem. Additionally, the paper 'A first look at License Variants in the PyPI Ecosystem' introduces a novel approach for efficient license variant analysis and automated pipeline for detecting license incompatibilities, while 'Understanding the Supply Chain and Risks of Large Language Model Applications' presents a comprehensive dataset for analyzing and benchmarking LLM supply chain security.