CPscan: Detecting Bugs Caused by Code Pruning in IoT Kernels
Lirong Fu, Shouling Ji, Kangjie Lu, Peiyu Liu, Xuhong Zhang, Yuxuan Duan, Zihui Zhang, Wenzhi Chen, Yanjun Wu
Abstract
To reduce the development costs, IoT vendors tend to construct IoT kernels by customizing the Linux kernel. Code pruning is common in this customization process. However, due to the intrinsic complexity of the Linux kernel and the lack of long-term effective maintenance, IoT vendors may mistakenly delete necessary security operations in the pruning process, which leads to various bugs such as memory leakage and NULL pointer dereference. Yet detecting bugs caused by code pruning in IoT kernels is difficult. Specifically, (1) a significant structural change makes precisely locating the deleted security operations (DSO ) difficult, and (2) inferring the security impact of a DSO is not trivial since it requires complex semantic understanding, including the developing logic and the context of the corresponding IoT kernel. In this paper, we present CPscan, a system for automatically detecting bugs caused by code pruning in IoT kernels. First, using a new graph-based approach that iteratively conducts a structure-aware basic block matching, CPscan can precisely and efficiently identify theDSOs in IoT kernels. Then, CPscan infers the security impact of a DSO by comparing the bounded use chains (where and how a variable is used within potentially influenced code segments) of the security-critical variable associated with it. Specifically, CPscan reports the deletion of a security operation as vulnerable if the bounded use chain of the associated security-critical variable remains the same before and after the deletion. This is because the unchanged uses of a security-critical variable likely need the security operation, and removing it may have security impacts. The experimental results on 28 IoT kernels from 10 popular IoT vendors show that CPscan is able to identify 3,193DSO s and detect 114 new bugs with a reasonably low false-positive rate. Many such bugs tend to have a long latent period (up to 9 years and 5 months). We believe CPscan paves a way for eliminating the bugs introduced by code pruning in IoT kernels. We will open-source CPscan to facilitate further research.
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.
Cited by top-tier papers3
- GNNIC: Finding Long-Lost Sibling Functions with Abstract SimilarityQiushi Wu, Zhongshu Gu, Hani Jamjoom, Kangjie LuNDSS 2024
- AURC: Detecting Errors in Program Code and DocumentationPeiwei Hu, Ruigang Liang, Ying Cao, Kai Chen et al.USENIX Security 2023
- Mens Sana In Corpore Sano: Sound Firmware Corpora for Vulnerability ResearchRené Helmke, Elmar Padilla, Nils AschenbruckNDSS 2025
Builds on25
- Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity DetectionXiaojun Xu, Chang Liu, Qian Feng, Heng Yin et al.CCS 2017 · 682 citations
- Scalable Graph-based Bug Search for Firmware ImagesQian Feng, Rundong Zhou, Chengcheng Xu, Yao Cheng et al.CCS 2016 · 456 citations
- Towards Automated Dynamic Analysis for Linux-based Embedded FirmwareDaming D. Chen, Maverick Woo, David Brumley, Manuel EgeleNDSS 2016 · 428 citations
- CollAFL: Path Sensitive FuzzingShuitao Gan, Chao Zhang, Xiaojun Qin, Xuwen Tu et al.S&P 2018 · 426 citations
- IoTFuzzer: Discovering Memory Corruptions in IoT Through App-based FuzzingJiongyi Chen, Wenrui Diao, Qingchuan Zhao, Chaoshun Zuo et al.NDSS 2018 · 311 citations
Related papers
- Check It Again: Detecting Lacking-Recheck Bugs in OS KernelsWenwen Wang, Kangjie Lu, Pen-Chung YewCCS 2018 · 49 citations
- Detecting Missing-Check Bugs via Semantic- and Context-Aware Criticalness and Constraints InferencesKangjie Lu, Aditya Pakki, Qiushi WuUSENIX Security 2019 · 97 citations
- Manta: Hybrid-Sensitive Type Inference Toward Type-Assisted Bug Detection for Stripped BinariesChengfeng Ye, Yuandao Cai, Anshunkang Zhou, Heqing Huang et al.ASPLOS 2024 · 3 citations
- Bond: Constraint-Directed Fuzzing for Automated Validation of Taint Analysis Results in Linux-based IoT FirmwareJiaqian Peng, Puzhuo Liu, Kai Cheng, Zhaoteng Yan et al.USENIX Security 2026
- Securing Retrieval-Augmented Code Generation via Contextual Knowledge Injection: A Case for Embedded IoT ApplicationsTong Sun, Jingyi Su, Yi Gao, Wei DongUSENIX Security 2026
