关于恶意Java包的检测

Piergiorgio Ladisa, H. Plate, Matias Martinez, Olivier Barais, Serena Elisa Ponta
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引用次数: 4

摘要

开源软件供应链攻击的目的是通过毒害开源软件包来感染下游用户。使用这类工件的常见方法是通过包存储库,检测这类攻击的审查策略的开发正在进行研究。尽管Java很受欢迎,但在供应链攻击的背景下,Java生态系统是较少被探索的一个。在本文中,我们提出了简单而有效的恶意行为指标,可以通过分析Java字节码静态地观察到。然后我们评估这些指标及其组合在检测恶意代码注入时的表现。我们通过在libraries.io的前10个最流行的Java库中注入三个来自真实世界示例的恶意有效负载来实现这一点。我们发现,对常量池中的字符串和字节码指令中的敏感api的分析有助于通过显著减少信息来检测恶意Java包,从而使手动分类成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards the Detection of Malicious Java Packages
Open-source software supply chain attacks aim at infecting downstream users by poisoning open-source packages. The common way of consuming such artifacts is through package repositories and the development of vetting strategies to detect such attacks is ongoing research. Despite its popularity, the Java ecosystem is the less explored one in the context of supply chain attacks. In this paper, we present simple-yet-effective indicators of malicious behavior that can be observed statically through the analysis of Java bytecode. Then we evaluate how such indicators and their combinations perform when detecting malicious code injections. We do so by injecting three malicious payloads taken from real-world examples into the Top-10 most popular Java libraries from libraries.io. We found that the analysis of strings in the constant pool and of sensitive APIs in the bytecode instructions aid in the task of detecting malicious Java packages by significantly reducing the information, thus, making also manual triage possible.
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