A Segmented Stack Randomization for bare-metal IoT devices

Abstract

Bare-metal IoT devices, lacking memory management features such as virtual memory and Memory Management Units (MMUs), are increasingly vulnerable to memory corruption attacks like buffer overflow and Return-Oriented Programming (ROP). To address these challenges, this paper proposes the Segmented Stack Randomization (SSR) scheme, a novel approach that enhances security by randomly allocating stack space across multiple segments during function calls. Designed to operate without additional hardware, the proposed SSR is highly suitable for resource-constrained IoT environments, particularly those requiring predictable execution times for real-time applications. The proposed SSR involves Low Level Virtual Machine (LLVM)-based code instrumentation, enabling seamless integration into finalized firmware without introducing debugging complexities. A proof-of-concept implementation on an ARM Cortex-M4 platform demonstrated that SSR provides robust protection against stack-based attacks with minimal performance overhead, averaging $1.591\mu s$ec per function call. Additionally, the proposed SSR offers tunable trade-offs between memory usage and randomization entropy, ensuring adaptability to various application requirements. These results highlight the proposed SSR as a practical and efficient security solution for safeguarding bare-metal IoT devices against evolving threats.