FirmUp

Author:

David Yaniv1,Partush Nimrod1,Yahav Eran1

Affiliation:

1. Technion, Haifa, Israel

Abstract

We present a static, precise, and scalable technique for finding CVEs (Common Vulnerabilities and Exposures) in stripped firmware images. Our technique is able to efficiently find vulnerabilities in real-world firmware with high accuracy. Given a vulnerable procedure in an executable binary and a firmware image containing multiple stripped binaries, our goal is to detect possible occurrences of the vulnerable procedure in the firmware image. Due to the variety of architectures and unique tool chains used by vendors, as well as the highly customized nature of firmware, identifying procedures in stripped firmware is extremely challenging. Vulnerability detection requires not only pairwise similarity between procedures but also information about the relationships between procedures in the surrounding executable. This observation serves as the foundation for a novel technique that establishes a partial correspondence between procedures in the two binaries. We implemented our technique in a tool called FirmUp and performed an extensive evaluation over 40 million procedures, over 4 different prevalent architectures, crawled from public vendor firmware images. We discovered 373 vulnerabilities affecting publicly available firmware, 147 of them in the latest available firmware version for the device. A thorough comparison of FirmUp to previous methods shows that it accurately and effectively finds vulnerabilities in firmware, while outperforming the detection rate of the state of the art by 45% on average.

Funder

European Union's Seventh Framework Programme

Israel Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Review of IoT Firmware Vulnerabilities and Auditing Techniques;Sensors;2024-01-22

2. Binary Representation Embedding and Deep Learning For Binary Code Similarity Detection in Software Security Domain;Proceedings of the 12th International Symposium on Information and Communication Technology;2023-12-07

3. Scalable Program Clone Search through Spectral Analysis;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

4. Asteria-Pro: Enhancing Deep Learning-based Binary Code Similarity Detection by Incorporating Domain Knowledge;ACM Transactions on Software Engineering and Methodology;2023-11-24

5. A survey on IoT & embedded device firmware security: architecture, extraction techniques, and vulnerability analysis frameworks;Discover Internet of Things;2023-10-31

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