As an increasing number of reusable packages are available in software development, package ecosystems are becoming more mature. Python is one of the most popular programming languages today. PyPI, as the primary repository of Python packages, serves a critical role in Python software development. While PyPI enables registered users to publish open-source packages, attackers can disguise themselves as de velopers to distribute malicious packages. We present a hybrid malicious python package detection framework named PCPD, which combines static anomaly detection and dynamic running analysis to detect lurking malicious packages. Using PCPD, we analyze a real-world dataset containing 414,880 PyPI packages and successfully narrow the inspection scope to 324 suspicious packages, reducing the manual review workload for repository managers by 99.48%. We have identified 121 packages with malicious behavior using PCPD and made all samples publicly available for security researchers to help mitigate these risks and protect the community. Additionally, we discover potential factors that enable malicious packages to evade scrutiny from repositories and associated threat signals.

In recent years, software supply chain attacks have become increasingly prevalent, prompting considerable research into detecting malicious packages within relevant repositories. With the popularity bolstered by the widespread adoption of open-source practices, Java become one of the preferred languages among modern developers. However, the issue of malware detection in Java components remains unresolved. Most prior approaches suffer from insufficient code coverage and coarse-grained representation, making them unsuitable for Java components.In this paper, we propose an innovative solution calledSheartailored for detecting malicious Java components.Shearfirstly analyzes all methods in the component and locates potential malicious code snippets based on sensitive calls, as slice-level analysis provides a better understanding of the specific malicious activities. Secondly, statements depending on sensitive call sites are extracted and embedded into vectors for further detection instead of function-level representation which is coarse-grained facing the dynamic features in Java. The corresponding experimental results show thatSheareffectively identifies the malicious semantics hidden in the code slices by leveraging the neural network model, outperforming currently available tools to a great extent. Through real-world validation,Sheardetected 51 components with malicious characteristics out of 68,273, demonstrating its practical feasibility. This study introduces the first Java malicious component detection method suitable for real-world scenarios, carrying considerable practical significance in bolstering defenses within the software supply chain.

Python has gradually become one of the most important programming languages through artificial intelligence's development. PIP, a package management tool for Python, offers one-click installation, allowing developers to utilize other people's code to speed up development. However, any registered member can easily upload packages to the repository that stores third-party packages. This functionality is used by attackers to poison the package index, i.e., to publish enormous malicious pip packages for installing backdoor, gathering information, etc. To know the situation of third-party packages in the Python community ecosystem, we establish the criteria for judging packages' suspicious or malicious behavior by analyzing the code logic in disclosed malicious packages. With the gained findings, we propose and implement Pip Poisoning Detector (PPD), an approach based on anomaly detection. PPD evaluated 228,723 packages, and after human inspection, we found 63 malicious and 238 suspicious ones among the output 5,699 results. The experimental results prove that our approach is effective and can significantly reduce review workload by 97.51%.