On Securing the Software Development Lifecycle in IoT RISC-V Trusted Execution Environments

Computer Science > Cryptography and Security [Submitted on 18 Mar 2026] On Securing the Software Development Lifecycle in IoT RISC-V Trusted Execution Environments Annika Wilde, Samira Briongos, Claudio Soriente, Ghassan Karame RISC-V-based Trusted Execution Environments (TEEs) are gaining traction in the automotive and IoT sectors as a foundation for protecting sensitive computations. However, the supporting infrastructure around these TEEs remains immature. In particular, mechanisms for secure enclave updates and migrations - essential for complete enclave lifecycle management - are largely absent from the evolving RISC-V ecosystem. In this paper, we address this limitation by introducing a novel toolkit that enables RISC-V TEEs to support critical aspects of the software development lifecycle. Our toolkit provides broad compatibility with existing and emerging RISC-V TEE implementations (e.g., Keystone and CURE), which are particularly promising for integration in the automotive industry. It extends the Security Monitor (SM) - the trusted firmware layer of RISC-V TEEs - with three modular extensions that enable secure enclave update, secure migration, state continuity, and trusted time. Our implementation demonstrates that the toolkit requires only minimal interface adaptation to accommodate TEE-specific naming conventions. Our evaluation results confirm that our proposal introduces negligible performance overhead: our state continuity solution incurs less than 1.5% overhead, and enclave downtime remains as low as 0.8% for realistic applications with a 1 KB state, which conforms with the requirements of most IoT and automotive applications. Comments: To appear in the Proceedings of the International Conference on Embedded Artificial Intelligence and Sensing Systems (SenSys) 2026 Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.17757 [cs.CR]   (or arXiv:2603.17757v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.17757 Focus to learn more Submission history From: Annika Wilde [view email] [v1] Wed, 18 Mar 2026 14:19:26 UTC (154 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)