RTrace: Towards Better Visibility of Shared Library Execution
Huaifeng Zhang, Ahmed Ali-Eldin
Abstract
Software supply chain security has become a critical concern in recent years. Modern software systems increasingly depend on third-party dependencies to accelerate development. Shared libraries are the prevalent form of software sharing and hence, of third-party dependencies in modern software systems. As more attacks target the software supply chain, understanding the behavior of these dependencies is essential for identifying vulnerabilities and malicious code. Hence, accurately tracing function calls within shared libraries is critical for effective software security analysis. However, existing library function tracers often fail to meet this need. As we show in this work, state-of-the-art library function tracers are limited in effectiveness and scalability, missing a significant number of function calls and failing with more complex workloads, resulting in incomplete or misleading views of runtime behavior. In this paper, we present RTrace, a tracing tool designed to address the limitations of existing solutions. We analyze the root causes of why widely used tracers miss function calls and identify common pitfalls such as relying on incorrect symbol information and inability to monitor early or indirect function invocations. RTrace overcomes these challenges by incorporating comprehensive runtime monitoring, function boundary detection, and support for implicit and unconventional function calls. We compare RTrace to four state-of-the-art tracers, namely, emphltrace, emphdrltrace, emphldaudit, and emphIntelPT. Our evaluation across 21 applications and 92 shared libraries shows that RTrace significantly outperforms existing tools in detecting function call. RTrace achieves an F1-score of at least 0.92 on all benchmarks, whereas the best existing tracer reaches only 0.74, providing more accurate visibility into shared library runtime behavior. Finally, we show how RTrace can be used to assist in detecting malicious package and in vulnerability analysis by providing a more complete view of shared library function usage.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 12f9587f-61e1-49c5-b01b-fbb358fb5ee3Builds on13
- SOK: (State of) The Art of War: Offensive Techniques in Binary AnalysisYan Shoshitaishvili, Ruoyu Wang, Christopher Salls, Nick Stephens et al.S&P 2016 · 1,085 citations
- RAZOR: A Framework for Post-deployment Software DebloatingChenxiong Qian, Hong Hu, Mansour Alharthi, Simon Pak Ho Chung et al.USENIX Security 2019 · 132 citations
- DONAPI: Malicious NPM Packages Detector using Behavior Sequence Knowledge MappingCheng Huang, Nannan Wang, Ziyan Wang, Siqi Sun et al.USENIX Security 2024 · 38 citations
- A Broad Comparative Evaluation of Software Debloating ToolsMichael D. Brown, Adam Meily, Brian Fairservice, Akshay Sood et al.USENIX Security 2024 · 16 citations
- A Needle is an Outlier in a Haystack: Hunting Malicious PyPI Packages with Code ClusteringWentao Liang, Xiang Ling, Jingzheng Wu, Tianyue Luo et al.ASE 2023 · 15 citations
Related papers
- VulSCA: A Community-Level SCA Approach for Accurate C/C++ Supply Chain Vulnerability AnalysisYutao Hu, Chaofan Li, Yueming Wu, Yifeng Cai et al.NDSS 2026 · 1 citation
- Magneto: A Step-Wise Approach to Exploit Vulnerabilities in Dependent Libraries via LLM-Empowered Directed FuzzingZhuotong Zhou, Yongzhuo Yang, Susheng Wu, Yiheng Huang et al.ASE 2024 · 7 citations
- From Noise to Signal: Precisely Identify Affected Packages of Known Vulnerabilities in npm EcosystemYingyuan Pu, Lingyun Ying, Yacong GuNDSS 2026 · 4 citations
- Understanding the Limitations of C/C++ Binary Third-Party Library Detection Tool: An Empirical Study at ScaleChengyue Liu, Zhengzi Xu, Kaixuan Li, Jiahui Wu et al.FSE 2026
- ProgSCA: Software Composition Analysis via Program-Level ModelingPeihong Li, Cheng Li, Yuchen Gu, Yanzhe Hu et al.ISSTA 2026
