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P2 RAICC: Revealing Atypical Inter-Component Communication in Android Apps

RAICC:揭示 Android 应用中的非典型组件间通信
ICSE 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)2021匹配度 2/10 · 该论文属于传统的 Android 移动安全静态分析,研究 ICC 建模与漏洞检测,不涉及 LLM/Agentic 程序、ML 系统静态分析、浮点程序分析或贝叶斯程序分析等用户核心方向。引用 49 inbox
Jordan Samhi, Alexandre Bartel, Tegawende F. Bissyande, Jacques Klein
crossref: 2026-09-12dblp: 2026-09-12arxiv: 2026-09-12

AI 速览

一句话:提出静态分析方法 RAICC,识别 Android 框架中未文档化的非典型 ICC 方式,补全组件间通信模型并提升安全分析效果。

组件间通信(ICC)是 Android 的关键机制,但现有静态分析工具(如 EPICC、ICCTA、AMANDROID)仅基于框架文档化的 ICC 方法(如 startActivity)建模,可能遗漏非典型 ICC 方式,导致应用 ICC 模型不完整。本文提出 RAICC,一种静态分析方法,用于识别并建模这些非典型 ICC 链接,从而增强 ICC 漏洞检测、隐私泄漏检测、恶意软件检测等下游任务。作者在 20 个基准应用上评估,证明 RAICC 提升了现有工具发现泄漏的精度与召回率;大规模实证研究表明非典型 ICC 方法在 Android 应用中被广泛使用,尽管不一定用于数据传输;在真实恶意应用数据集上,RAICC 使发现的 ICC 链接数量增加 61.6%,并能够检测出新的 ICC 漏洞。

问题
现有 Android 静态分析工具只依赖框架文档化的 ICC 方法建模组件间通信,忽略了框架提供的非典型 ICC 方式,导致 ICC 模型不完整、下游安全分析精度不足。
方法
提出 RAICC 静态分析方法,识别并建模非典型 ICC 方法所建立的组件间通信链接,以补全或增强既有 ICC 分析。
评估
在 20 个基准应用上验证 RAICC 提升现有工具的泄漏检测精度与召回率,并通过大规模实证研究及真实恶意应用数据集评估其覆盖非典型 ICC 方法的能力,显示 ICC 链接增加 61.6% 且能发现新的 ICC 漏洞。
原文摘要(Abstract)

Inter-Component Communication (ICC) is a key mechanism in Android. It enables developers to compose rich functionalities and explore reuse within and across apps. Unfortunately, as reported by a large body of literature, ICC is rather "complex and largely unconstrained", leaving room to a lack of precision in apps modeling. To address the challenge of tracking ICCs within apps, state of the art static approaches such as EPICC, ICCTA and AMANDROID have focused on the documented framework ICC methods (e.g., startActivity) to build their approaches. In this work we show that ICC models inferred in these state of the art tools may actually be incomplete: the framework provides other atypical ways of performing ICCs. To address this limitation in the state of the art, we propose RAICC a static approach for modeling new ICC links and thus boosting previous analysis tasks such as ICC vulnerability detection, privacy leaks detection, malware detection, etc. We have evaluated RAICC on 20 benchmark apps, demonstrating that it improves the precision and recall of uncovered leaks in state of the art tools. We have also performed a large empirical investigation showing that Atypical ICC methods are largely used in Android apps, although not necessarily for data transfer. We also show that RAICC increases the number of ICC links found by 61.6% on a dataset of real-world malicious apps, and that RAICC enables the detection of new ICC vulnerabilities.

链接与引用

DOI 原文 · arXiv · PDF(开放获取) · DBLP

BibTeX
@inproceedings{SamhiBBK21,
  title = {RAICC: Revealing Atypical Inter-Component Communication in Android Apps},
  author = {Jordan Samhi and Alexandre Bartel and Tegawende F. Bissyande and Jacques Klein},
  booktitle = {ICSE},
  year = {2021},
  doi = {10.1109/icse43902.2021.00126}
}