As the largest package registry, Node Package Manager (NPM) has become the prime target for various supply chain attacks recently and has been flooded with numerous malicious packages, posing significant security risks to end-users. Learning-based methods have demonstrated promising performance with good adaptability to various types of attacks. However, they suffer from two main limitations. First, they often utilize metadata features or coarse-grained code features extracted at the package level while overlooking complex code semantics. Second, the dataset used to train the model often suffers from a lack of variety both in quantity and diversity, and thus cannot detect significant types of attacks. To address these problems, we introduce Maltracker, a learningbased NPM malware tracker based on fine-grained features empowered by LLM-enhanced dataset. First, Maltracker constructs precise call graphs to extract suspicious functions that are reachable to a pre-defined set of sensitive APIs, and then utilizes community detection algorithm to identify suspicious code gadgets based on program dependency graph, from which fine-grained features are then extracted. To address the second limitation, we extend the dataset using advanced large language models (LLM) to translate malicious functions from other languages (e.g., C/C++, Python, and Go) into JavaScript. Evaluations shows that Maltracker can achieve an improvement of about 12.6% in terms of F1-score at the package level and 31.0% at the function level compared with the SOTA learning-based methods. Moreover, the key components of 𝑀𝑎𝑙𝑡𝑟𝑎𝑐𝑘𝑒𝑟 all contribute to the effectiveness of its performance. Finally, Maltracker has also detected 230 new malicious packages in NPM and received 61 thanks letters, among which some contain new malicious behaviors that cannot be detected by existing tools.

Open-source software (OSS) has profoundly transformed the software development paradigm by facilitating effortless code reuse. However, in recent years, there has been an alarming increase in disclosed vulnerabilities within OSS, posing significant security risks to downstream users. Therefore, analyzing existing vulnerabilities and precisely assessing their threats to downstream applications become pivotal. Plenty of efforts have been made recently towards this problem, such as vulnerability reachability analysis and vulnerability reproduction. The key to these tasks is identifying the vulnerable function (i.e., the function where the root cause of a vulnerability resides). However, public vulnerability datasets (e.g., NVD) rarely include this information as pinpointing the exact vulnerable functions remains to be a longstanding challenge.

Modern software systems are increasingly relying on dependencies from the ecosystem. A recent estimation shows that around 35% of an open-source project's code come from its depended libraries. Unfortunately, open-source libraries are often threatened by various vulnerability issues, and the number of disclosed vulnerabilities is increasing steadily over the years. Such vulnerabilities can pose significant security threats to the whole ecosystem, not only to the vulnerable libraries themselves, but also to the corresponding downstream projects. Many Software Composition Analysis (SCA) tools have been proposed, aiming to detect vulnerable libraries or components referring to existing vulnerability databases. However, recent studies report that such tools often generate a large number of false alerts. Particularly, up to 73.3% of the projects depending on vulnerable libraries are actually safe. Aiming to devise more precise tools, understanding the threats of vulnerabilities holistically in the ecosystem is significant, as already performed by a number of existing studies. However, previous researches either analyze at a very coarse granularity (e.g., without analyzing the source code) or are limited by the study scales. This study aims to bridge such gaps. In particular, we collect 44,450 instances of (CVE, upstream, downstream) relations and analyze around 50 million invocations made from downstream to upstream projects to understand the potential threats of upstream vulnerabilities to downstream projects in the Maven ecosystem. Our investigation makes interesting yet significant findings with respect to multiple aspects, including the reach-ability of vulnerabilities, the complexities of the reachable paths as well as how downstream projects and developers perceive upstream vulnerabilities. We believe such findings can not only provide a holistic understanding towards the threats of upstream vulnerabilities in the Maven ecosystem, but also can guide future researches in this field.