diff --git a/aten/src/ATen/native/AveragePool2d.cpp b/aten/src/ATen/native/AveragePool2d.cpp
index 368dc02c283..035228285bc 100644
--- a/aten/src/ATen/native/AveragePool2d.cpp
+++ b/aten/src/ATen/native/AveragePool2d.cpp
@@ -25,18 +25,19 @@ TORCH_PRECOMPUTE_META_FUNC(avg_pool2d)
   // #20866, #22032: Guarantee this for the official C++ API?
   TORCH_CHECK(kernel_size.size() == 1 || kernel_size.size() == 2,
     "avg_pool2d: kernel_size must either be a single int, or a tuple of two ints");
-  const int64_t kH = kernel_size[0];
-  const int64_t kW = kernel_size.size() == 1 ? kH : kernel_size[1];
+  const int kH = safe_downcast<int, int64_t>(kernel_size[0]);
+  const int kW = kernel_size.size() == 1 ? kH : safe_downcast<int, int64_t>(kernel_size[1]);
 
   TORCH_CHECK(stride.empty() || stride.size() == 1 || stride.size() == 2,
     "avg_pool2d: stride must either be omitted, a single int, or a tuple of two ints");
-  const int64_t dH = stride.empty() ? kH : stride[0];
-  const int64_t dW = stride.empty() ? kW : stride.size() == 1 ? dH : stride[1];
+  const int dH = stride.empty() ? kH : safe_downcast<int, int64_t>(stride[0]);
+  const int dW = stride.empty() ? kW :
+                 stride.size() == 1 ? dH : safe_downcast<int, int64_t>(stride[1]);
 
   TORCH_CHECK(padding.size() == 1 || padding.size() == 2,
     "avg_pool2d: padding must either be a single int, or a tuple of two ints");
-  const int64_t padH = padding[0];
-  const int64_t padW = padding.size() == 1 ? padH : padding[1];
+  const int padH = safe_downcast<int, int64_t>(padding[0]);
+  const int padW = padding.size() == 1 ? padH : safe_downcast<int, int64_t>(padding[1]);
 
   TORCH_CHECK(!divisor_override.has_value() || divisor_override.value() != 0,
     "divisor must be not zero");
diff --git a/test/nn/test_pooling.py b/test/nn/test_pooling.py
index c3a7b829b2b..f20ee2a29d5 100644
--- a/test/nn/test_pooling.py
+++ b/test/nn/test_pooling.py
@@ -898,6 +898,16 @@ torch.cuda.synchronize()
             inp = torch.ones(1, 0, 50, 44, 31, device=device)
             mod(inp)
 
+    @onlyCPU
+    def test_LPPool1d_kernel_size_overflow_large(self, device):
+        avgpool = torch.nn.LPPool1d(
+            -1.38119e150, 7879455037536781369, ceil_mode=True
+        ).to(device)
+        inp = torch.randn(3, 15, device=device)
+
+        with self.assertRaisesRegex(RuntimeError, "integer out of range"):
+            avgpool(inp)
+
     @onlyNativeDeviceTypes
     def test_AvgPool2d_empty(self, device):
         avgpool = torch.nn.AvgPool2d(3, stride=2).to(device)