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.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.