trusted execution environments

This work addresses the vulnerability of Trusted Execution Environments (TEEs) to sensitive data leakage stemming from enclave code flaws and hardware-level exploits, which undermines their resilience against real-world threats. To bridge the gap between idealized TEE security models and practical robustness, the authors propose a RISC-V-based hardware-enhanced architecture that enables fine-grained tracking of sensitive data flows and enforces boundary-aware access control directly at the hardware level. Notably, the design incorporates, for the first time, a controlled declassification mechanism that systematically monitors intra-enclave data propagation and securely releases information when appropriate. FPGA-based prototype evaluation demonstrates that the proposed solution incurs only a 10.8% area overhead and a 5.69% performance penalty while effectively preventing unauthorized data exfiltration.

TEE containers face systemic security risks—including information leakage, rollback attacks, denial-of-service (DoS), and Iago attacks—due to ambiguously defined trust boundaries and isolation failures. This paper introduces the first automated boundary identification framework that jointly leverages static and dynamic analysis to reverse-engineer and formally verify isolation policies of mainstream TEE containers (e.g., SCONE, Gramine). Our analysis uncovers critical trust boundary misalignments in multiple production-deployed containers, empirically reproduces four classes of high-severity attacks, and quantifies their exploitability and impact scope. The work establishes a reusable trust boundary modeling paradigm and provides concrete hardening guidelines for TEE middleware design. By enabling rigorous, artifact-based boundary validation, it advances trusted execution environments from opaque “black-box” encapsulation toward verifiable, architecture-aware isolation.

This work addresses the lack of comprehensive lifecycle management support for trusted execution environments (TEEs) on RISC-V, particularly the absence of mechanisms for secure enclaves’ updates and migration. The authors propose the first modular lifecycle management framework tailored for RISC-V TEEs, introducing three lightweight extensions at the security monitor layer to enable enclave state continuity, secure migration, and trusted time services. The design is compatible with mainstream RISC-V TEE frameworks such as Keystone and CURE, requiring only minimal interface adaptations. Experimental evaluation demonstrates that the overhead for state continuity is below 1.5%, and enclave downtime during migration is merely 0.8% for a 1KB state, meeting the stringent requirements of safety-critical domains including IoT and automotive systems.

To address critical security challenges in IoT—including severe resource constraints, expansive attack surfaces, and imminent quantum threats—this paper proposes an end-to-end, full-stack security framework spanning the chip, firmware, communication, and application layers to ensure confidentiality, integrity, and availability across the device lifecycle. Methodologically, it innovatively integrates a hardware-based root of trust, CBOR-encoded lightweight digital certificates, an optimized TLS 1.3 protocol stack, and a seamless post-quantum cryptographic migration mechanism, enabling cross-layer coordinated defense and resource-adaptive security provisioning. Experimental evaluation demonstrates over 30% reduction in communication overhead, millisecond-scale secure boot latency, and certificate sizes compressed to sub-kilobyte levels. This work establishes the first verifiable, scalable, and quantum-resistant lightweight security paradigm for resource-constrained IoT devices.

This work addresses the security risks associated with executing industrial control software on unauthorized hardware, a challenge inadequately mitigated by conventional protection mechanisms that often fail to balance security and functional correctness. The authors propose a novel hardware-software binding approach that integrates Physical Unclonable Functions (PUFs) with symbolic execution to enforce program behavior constraints and verify critical security properties. This method ensures that the software operates correctly only on authorized target devices while maintaining secure behavior—even in the presence of unauthorized execution environments or PUF failures. Notably, this study is the first to leverage symbolic execution for preserving software security properties under anomalous execution conditions, thereby achieving a robust combination of strong anti-reverse-engineering capabilities and high reliability.

AMD SEV lacks formal verification of its core security properties, including confidentiality, integrity, and availability. This work presents the first systematic formal framework for rigorously verifying these critical security attributes by introducing design-level and property-level abstractions of the SEV architecture, combined with formal modeling and model checking techniques. By establishing a mathematically grounded analysis of SEV’s security guarantees, the study addresses a significant gap in the trusted execution environment literature, where prior evaluations have largely relied on informal or empirical methods. The proposed approach substantially enhances the reliability and trustworthiness of SEV as a confidential computing platform in cloud environments, providing a foundation for future formal analyses of hardware-based security mechanisms.

Current Trusted Execution Environment (TEE) solutions exhibit high heterogeneity and lack a unified abstraction layer, hindering the generality and development efficiency of confidential computing. Method: This paper systematically surveys the TEE technology landscape and proposes, for the first time, a design-oriented knowledge framework for TEE abstraction layers. Through multidimensional comparative analysis of mainstream architectures—including Intel SGX, ARM TrustZone, and AMD SEV—it identifies WebAssembly as the most capable cross-platform abstraction pathway. A comprehensive, full-stack classification framework for TEE abstraction layers is then constructed to precisely characterize capability boundaries and interoperability across implementations. Contribution/Results: The work delivers a practical abstraction modeling methodology and security interface design guidelines for heterogeneous TEE ecosystems, significantly enhancing the portability of confidential applications and improving development productivity.

Embedded systems face multifaceted security threats originating from applications, kernels, and peripherals, yet existing defenses often fail to provide comprehensive protection. This work proposes a novel capability-based architecture that, without requiring modifications to peripheral hardware, integrates tokenized capabilities with hardware-enforced isolation to decompose the Zephyr RTOS into mutually isolated components. For the first time, this approach realizes a soft real-time operating system that is fully untrusted at runtime, thereby eliminating reliance on the trustworthiness of conventional kernel components—including the scheduler, memory allocator, and DMA drivers. The resulting prototype system features no software trusted computing base (TCB) at runtime, establishing a scalable and formally verifiable security foundation for high-assurance embedded devices.

This work addresses the conflict between the periodic SysTick interrupts relied upon by real-time operating systems (RTOS) and the atomic execution requirements of trusted computing, which can lead to timing inaccuracies and degraded real-time performance. To resolve this issue without modifying existing RTOS implementations, the authors propose a TrustZone-M-based secure-world-driven time synchronization mechanism. By precisely measuring elapsed time and transparently compensating for missed clock ticks in the non-secure world, the approach restores a monotonic and consistent time view across both worlds. This method achieves, for the first time, temporal consistency and secure coexistence between trusted services and a non-secure RTOS sharing an interrupt controller, preserving both the atomicity of trusted execution and the RTOS’s timing accuracy and real-time behavior, with negligible runtime overhead.

This work addresses the challenge of detecting input validation vulnerabilities in Trusted Execution Environments (TEEs), which stem from their complex configuration and hardware-enforced isolation. To overcome this, the authors propose SymTEE, a novel symbolic execution framework that uniquely integrates abstract syntax tree (AST)-based static analysis with large language models (specifically GPT-5). The approach first identifies suspicious code segments and then leverages the LLM to automatically generate lightweight, KLEE-compatible mock execution environments, enabling symbolic execution without requiring actual TEE deployment. This significantly lowers the barrier to TEE security analysis while enhancing scalability. Evaluated on 26 known vulnerability cases, SymTEE achieves 100% precision and 92.3% recall, with an average analysis cost of merely $0.05 per instance.