MATEE: Efficiently Bridging the Semantic Gap in TrustZone via Arm Pointer Authentication

• URL: http://arxiv.org/abs/2608.20583v1
• Date: Thu, 20 Aug 2026 21:33:06 GMT
• Title: MATEE: Efficiently Bridging the Semantic Gap in TrustZone via Arm Pointer Authentication
• Abstract summary: Arm TrustZone partitions the system into the secure and normal (non-secure) worlds.<n>This results in the secure world having very limited visibility into the operating information of the normal world.<n>We propose a mate system for the TEE called MATEE to defend against Semantic Gap Vulnerabilities (SGVs)
• Score: 5.630589990430752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trusted Execution Environments (TEEs) employ hardware-based isolation mechanisms to safeguard the confidentiality and integrity of sensitive code and data. One such prevalent implementation is Arm TrustZone, which partitions the system into the secure and normal (non-secure) worlds. However, this partitioning results in the secure world having very limited visibility into the operating information of the normal world, creating a semantic gap between these two worlds. Specifically, the secure world lacks an effective user identity authentication when receiving data requests from the normal world. Consequently, malicious Client Applications (CAs) in the normal world can deceive Trusted Applications (TAs) in the secure world by utilizing elaborate request parameters, compromising the sensitive data stored by other CAs. We systematically classify these Semantic Gap Vulnerabilities (SGVs) and propose a mate system for the TEE called MATEE to defend against SGVs. MATEE utilizes Arm Pointer Authentication (PA) to bind each request to the corresponding CA's identity and then verifies the identity when the CA accesses sensitive data, thereby preventing malicious request forgery. In particular, MATEE isolates sensitive data of different CAs without modifying existing CAs and TAs. Our evaluation demonstrates that MATEE successfully defends against SGVs with a minimal runtime overhead (2.19%).

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