diff --git a/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h b/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h
index 7dfb2770a6979..4c7f2268310ff 100644
--- a/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h
+++ b/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h
@@ -216,6 +216,16 @@ class MaxpoolWithMask : public OpKernel, public PoolBase {
                         "Mask and input spatial dimensions mismatch at dimension ", i,
                         ": mask=", m_shape[i], " input=", x_shape[i]);
     }
+    // x_shape.NumDimensions() >= 3 is guaranteed above, so this subtraction cannot underflow.
+    const size_t input_spatial_rank = x_shape.NumDimensions() - 2;
+    // The pooling kernel rank drives the 1D/2D/3D dispatch below, which reads x_shape[2..4] and
+    // output_dims[2..4]. Require it to match the input spatial rank so those reads stay in bounds.
+    ORT_RETURN_IF_NOT(pool_attrs_.kernel_shape.size() == input_spatial_rank,
+                      "Pooling kernel rank must equal input spatial rank. Got kernel rank: ",
+                      pool_attrs_.kernel_shape.size(), " input spatial rank: ", input_spatial_rank);
+    // Only 1D/2D/3D pooling is implemented by the dispatch below; a larger rank would match no case.
+    ORT_RETURN_IF_NOT(input_spatial_rank >= 1 && input_spatial_rank <= 3,
+                      "Only 1D, 2D, and 3D pooling are supported. Got input spatial rank: ", input_spatial_rank);
 
     TensorShapeVector pads = pool_attrs_.pads;
     TensorShapeVector kernel_shape = pool_attrs_.kernel_shape;
diff --git a/onnxruntime/test/contrib_ops/maxpool_mask_test.cc b/onnxruntime/test/contrib_ops/maxpool_mask_test.cc
index ed65700ccd336..af095a141dfcd 100644
--- a/onnxruntime/test/contrib_ops/maxpool_mask_test.cc
+++ b/onnxruntime/test/contrib_ops/maxpool_mask_test.cc
@@ -161,5 +161,70 @@ TEST(ContribOpTest, MaxPoolWithMask_MaskEmptyBatchDim) {
            "Mask N and C dimensions must be greater than 0");
 }
 
+TEST(ContribOpTest, MaxPoolWithMask_KernelRankMismatch) {
+  OpTester test("MaxpoolWithMask", 1, onnxruntime::kMSDomain);
+
+  // AddShapeToTensorData(false) omits input shape from the graph so ONNX shape inference is bypassed
+  // (convPoolShapeInference returns early when hasInputShape is false). This lets the model pass
+  // Graph::Resolve() and reach Compute() where the kernel-rank guard fires.
+  test.AddShapeToTensorData(false);
+
+  test.AddAttribute("auto_pad", "");
+  test.AddAttribute("strides", std::vector<int64_t>{1, 1});
+  test.AddAttribute("pads", std::vector<int64_t>{0, 0, 0, 0});
+  // 2D kernel_shape, but X has only one spatial dimension (rank 3).
+  test.AddAttribute("kernel_shape", std::vector<int64_t>{8, 8});
+
+  // Input X has shape {1, 1, 8} (rank 3 => one spatial dim)
+  std::vector<int64_t> x_dims = {1, 1, 8};
+  std::vector<float> x_vals(8, 1.0f);
+
+  // Mask M matches X shape so the earlier spatial/rank guards pass and we reach the kernel-rank guard.
+  std::vector<int64_t> m_dims = {1, 1, 8};
+  std::vector<int32_t> m_vals(8, 1);
+
+  // Placeholder output shape and values (not validated since we expect failure)
+  std::vector<int64_t> expected_dims = {1, 1, 1, 1};
+  std::vector<float> expected_vals = {1.0f};
+
+  test.AddInput<float>("X", x_dims, x_vals);
+  test.AddInput<int32_t>("M", m_dims, m_vals);
+  test.AddOutput<float>("Y", expected_dims, expected_vals);
+  test.Run(BaseTester::ExpectResult::kExpectFailure,
+           "Pooling kernel rank must equal input spatial rank");
+}
+
+TEST(ContribOpTest, MaxPoolWithMask_KernelRankTooLarge) {
+  OpTester test("MaxpoolWithMask", 1, onnxruntime::kMSDomain);
+
+  // Bypass ONNX shape inference (see MaxPoolWithMask_KernelRankMismatch) so the model reaches Compute().
+  test.AddShapeToTensorData(false);
+
+  test.AddAttribute("auto_pad", "");
+  test.AddAttribute("strides", std::vector<int64_t>{1, 1, 1, 1});
+  test.AddAttribute("pads", std::vector<int64_t>{0, 0, 0, 0, 0, 0, 0, 0});
+  // 4D kernel_shape with a matching rank-6 X (4 spatial dims): passes the kernel-rank equality guard
+  // but exceeds the supported 1D/2D/3D pooling ranks.
+  test.AddAttribute("kernel_shape", std::vector<int64_t>{2, 2, 2, 2});
+
+  // Input X has shape {1, 1, 2, 2, 2, 2} (rank 6 => four spatial dims)
+  std::vector<int64_t> x_dims = {1, 1, 2, 2, 2, 2};
+  std::vector<float> x_vals(16, 1.0f);
+
+  // Mask M matches X so the earlier guards and the kernel-rank equality guard all pass.
+  std::vector<int64_t> m_dims = {1, 1, 2, 2, 2, 2};
+  std::vector<int32_t> m_vals(16, 1);
+
+  // Placeholder output shape and values (not validated since we expect failure)
+  std::vector<int64_t> expected_dims = {1, 1, 1, 1};
+  std::vector<float> expected_vals = {1.0f};
+
+  test.AddInput<float>("X", x_dims, x_vals);
+  test.AddInput<int32_t>("M", m_dims, m_vals);
+  test.AddOutput<float>("Y", expected_dims, expected_vals);
+  test.Run(BaseTester::ExpectResult::kExpectFailure,
+           "Only 1D, 2D, and 3D pooling are supported");
+}
+
 }  // namespace test
 }  // namespace onnxruntime