M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification
Large language model (LLM) inference systems rely on CUDA kernels for core GPU computations, yet the interface between models and kernels is implicit and poorly specified. Models and kernels evolve independently and often make incompatible assumptions about tensor shapes and input sizes, leading to subtle memory bugs in CUDA kernels. These bugs can crash inference services, corrupt model weights, or be exploited by remote adversaries. Existing techniques either incur prohibitive runtime overhead, require specialized hardware, or fail to handle dynamic tensor shapes and variable kernel launch configurations, leaving the CUDA memory bugs largely unaddressed. This paper presents M2K, a fully...
| Authors | Mengting He, Shihao Xia, Haomin Jia et al. |
|---|---|
| Primary category | cs.PL |
| Published | 6 March 2026 |
| Last updated at source | 29 August 2026 |
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