In recent years, organisations have increasingly transitioned their workloads from on-premise infrastructures to cloud environments, while leveraging edge computing to meet the rising demand for scalable and distributed applications. This shift has accelerated the adoption of IoT devices, which play a key role in enabling these systems. As a result, ensuring the security of sensitive IoT applications has become critical, motivating the use of Trusted Execution Environments (TEEs) to provide isolated execution even in the presence of potentially compromised operating systems. This work focuses on the IoT-oriented Keystone Enclave framework, an open-source TEE built on the RISC-V Instruction Set Architecture. Among its security features, Keystone implements a binary measurement mechanism during the enclave-loading phase. However, this approach guarantees application integrity only at load time, leaving the TEE’s confidentiality and integrity vulnerable to runtime exploitation of software vulnerabilities. To address this limitation, we propose an integrity verification mechanism that provides evidence about the state of sensitive memory regions throughout enclave execution. Compared to traditional load-time measurement techniques, our approach reduces per-execution measurement overhead by 57.5%, while requiring minimal extensions to the Trusted Computing Base. Furthermore, it overcomes key limitations of the existing framework by decoupling enclave applications from the attestation logic.
Ciravegna et al. (Fri,) studied this question.