[tensorflow/tensorflow] Integer overflow in Tile kernel MultiplyShapeDims causes heap buffer overflow and SIGBUS crash

### ROOT CAUSE The issue arises from an unchecked integer multiplication in the `MultiplyShapeDims` function, which is used to compute tensor dimensions. When two large dimensions are multiplied, the product overflows the maximum value of `int64_t`, resulting in a negative number. This negative dimension is then used to allocate memory, causing a heap buffer overflow and SIGBUS crash due to invalid memory access. ### CODE FIX Add overflow checks before multiplication to prevent negative results. For both GPU and CPU implementations: ```cpp // For tile_op_gpu.cu.h (GPU kernel) template __global__ void MultiplyShapeDimsKernelLauncher(const int64_t dim1_count, const int64_t dim2_count, const T* shape1, const T* shape2, T* output) { CUDA_KERNEL_LAUNCH_READONLY() { int i = blockIdx.x * blockDim.x + threadIdx.x; if (i < dim1_count && i < dim2_count) { int64_t a = shape1[i]; int64_t b = shape2[i]; if (b == 0 || a == 0) { output[i] = 0; } else if (a > std::numeric_limits::max() / b) { // Handle overflow: set to 0 (or use error handling) output[i] = 0; } else { output[i] = a * b; } } } } ``` ```cpp // For tile_op.cc (CPU function) Status MultiplyShapeDims(const std::vector& shape1, const std::vector& shape2, std::vector* output) { if (shape1.size() != shape2.size()) { return errors::InvalidArgument( "shape1 and shape2 must have the same size: ", shape1.size(), " vs ", shape2.size()); } output->resize(shape1.size()); for (int i = 0; i < shape1.size(); i++) { int64_t a = shape1[i]; int64_t b = shape2[i]; if (b == 0 || a == 0) { output->at(i) = 0; } else if (a > std::numeric_limits::max() / b) { // Handle overflow: set to 0 (or use error handling) output->at(i) = 0; } else { output->at(i) = a * b; } } return OkStatus(); } ```