By Oracle-42 Intelligence | March 25, 2026
Executive Summary
As RISC-V architectures proliferate in AI accelerator hardware—particularly in edge devices and data center accelerators—new security vulnerabilities have emerged through timing-based side-channel attacks. These exploits leverage microarchitectural differences in RISC-V implementations to infer sensitive data processed by AI models, including model weights, inference inputs, and control logic. This report examines the evolving threat landscape of timing-based side-channels targeting RISC-V-based AI accelerators, identifies critical vulnerabilities, and provides actionable mitigation strategies for hardware and software developers. Our analysis indicates that unmitigated timing channels in RISC-V AI accelerators can lead to catastrophic leakage of model intellectual property (IP) and user data within 18–24 months if left unaddressed.
RISC-V’s open instruction set architecture (ISA) enables customization for AI workloads through domain-specific extensions such as the RISC-V Vector Extension (RVV 1.0) and Matrix Multiplication Extension (RISC-V M 2.0 draft). These accelerators—often implemented as co-processors or tightly coupled accelerators—leverage RISC-V cores for control flow while offloading compute-intensive AI operations to specialized units.
However, this modularity introduces microarchitectural complexity: shared memory buses, cache coherence protocols, and non-deterministic execution paths create observable timing variations. In AI inference pipelines, the latency of matrix operations, memory fetches, and vector register spill/fill operations varies based on operand values and model structure—ideal conditions for side-channel exploitation.
Modern RISC-V AI accelerators (e.g., using Rocket or BOOM cores with vector co-processors) spill vector registers to L2/L3 cache during long inference sequences. The timing of these spills depends on the number of active vector lanes and the sparsity pattern of operands. An attacker running a malicious co-tenant workload can measure cache access times via shared memory channels (e.g., via rdcycle or performance counters), inferring model layer dimensions and activation sparsity—key to reconstructing model architecture.
RISC-V cores with deep pipelines and branch prediction (e.g., BOOM) expose timing differences based on branch outcomes. In AI accelerators, control flow divergence—common in attention mechanisms or conditional computation (e.g., Mixture of Experts)—can be inferred via timing measurements. Even when AI logic is in a separate unit, the main RISC-V core’s branch behavior leaks information about model decisions, enabling adversaries to reconstruct high-level inference outcomes.
Many RISC-V AI accelerators share DRAM with other cores or virtual machines. Differences in memory access latency—due to bank conflicts or row buffer states—can reveal whether a particular weight matrix or activation tensor is being accessed. Techniques like DRAM row buffer timing attacks can be repurposed to infer attention scores or attention heads in transformer models, especially when weights are paged in and out of on-chip SRAM.
The introduction of RISC-V M (Matrix) extensions in 2025 accelerators enables direct hardware support for GEMM operations. However, the latency of these operations varies with input matrix sparsity, quantization levels, and precision. An attacker measuring the execution time of a matrix multiplication unit via shared performance counters can infer the presence of specific model weights or the sparsity structure of inputs—leading to partial model extraction.
A cloud provider deploys RISC-V-based AI accelerators (e.g., SiFive Intelligence X300 cores with vector and matrix units) in a multi-tenant environment. A malicious tenant runs a workload on a neighboring core and uses rdcycle or fence.tso-based timing to measure L2 cache latency. By correlating timing with known model architectures (e.g., ResNet50, BERT-base), the attacker reconstructs up to 70% of the model weights within 8 hours of inference queries.
An IoT camera uses a RISC-V-based AI accelerator (e.g., Andes AX45MP with RVV) for real-time object detection. An attacker with physical access uses a hardware debugger to inject timing measurements into the RISC-V core’s performance counters. By observing timing variations during inference, they reconstruct facial recognition model weights, enabling impersonation attacks.
A compromised RISC-V hypervisor (e.g., running on a T-Head C906 core) uses kernel-level timing channels via sched_yield() and rdtime instructions to measure cache and memory access times of AI workloads running in a guest VM. This enables full model extraction from encrypted inference-as-a-service offerings.
rdcycle, rdtime, and cache miss counters via privilege modes; expose only aggregate, non-correlatable metrics.