Silent data thieves: Investigating malicious data exfiltration packages in PyPI
- 3 November 2026
- journal article
- research article
- Published by Springer Nature in Empirical Software Engineering
- Vol. 32 (2) , 44
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (62402342)
- Shanghai Rising-Star Program (24YF2749500)
- Shanghai Rising-Star Program (62472318)
This publication has 36 references indexed in Scilit:
- PyPIMalDet: A malicious PyPI package detection method combining code features and metadata featuresNeural Networks, 2025
- On the Correctness of Metadata-Based SBOM Generation: A Differential Analysis Approach2024
- A Needle is an Outlier in a Haystack: Hunting Malicious PyPI Packages with Code Clustering2023
- Detection of malicious javascript on an imbalanced datasetInternet of Things, 2021
- Defending Against Package Typosquatting2020
- Typosquatting and Combosquatting Attacks on the Python Ecosystem2020
- Backstabber’s Knife Collection: A Review of Open Source Software Supply Chain Attacks2020
- Evading android anti-malware by hiding malicious application inside imagesInternational Journal of System Assurance Engineering and Management, 2017
- Protecting data from malware threats using machine learning technique2017
- Note on the Sampling Error of the Difference Between Correlated Proportions or PercentagesPsychometrika, 1947