HyFault: Targeted Fault Injection Attacks on Hyperdimensional Computing Accelerators

Edge AI accelerators are a critical building block of numerous AI-driven applications deployed in resource-constrained environments such as IoT, automotive systems, and wearable devices. Hyperdimensional Computing (HDC) has recently emerged as a promising lightweight AI model for these edge scenarios, offering efficiency, simplicity, and inherent robustness against random computational faults. However, despite its advantages, the security implications of deploying HDC accelerators, particularly their resilience against targeted fault injection attacks, remain insufficiently explored. Such attacks pose tangible security risks, including intentional misclassification leading to denial-of-service or reliability degradation in critical decision-making systems. In this work, we precisely attack FPGA-based HDC accelerators using profiling and advanced voltage-level fault injection methods to evaluate their vulnerability. Our experiments reveal significant susceptibility during the critical similarity computation phase of the HDC inference pipeline, achieving targeted misclassification rates of up to ≈89%. To address these security vulnerabilities, we propose XOR masking and query hypervector randomization as practical, hardware-friendly countermeasures. ... mehrExtensive real hardware evaluations confirm that these defenses substantially reduce misclassification rates to ≈2%, significantly enhancing the security and reliability of edge-deployed accelerators.