diff --git a/backends/xnnpack/operators/op_slice_copy.py b/backends/xnnpack/operators/op_slice_copy.py
index 40d8e5f04eb..d9056afa832 100644
--- a/backends/xnnpack/operators/op_slice_copy.py
+++ b/backends/xnnpack/operators/op_slice_copy.py
@@ -69,7 +69,9 @@ def define_node(
             output_shape = [output_shape[i] for i in PERM_NCHW_TO_NHWC]
             dim_of_slice = PERM_NHWC_TO_NCHW[dim_of_slice]
 
-        slice_begin_index = cast(int, node.args[2])
+        slice_begin_index = 0
+        if len(node.args) > 2 and node.args[2]:
+            slice_begin_index = cast(int, node.args[2])
         if slice_begin_index < 0:
             slice_begin_index = input_shape[dim_of_slice] + slice_begin_index
 
diff --git a/backends/xnnpack/test/ops/test_slice_copy.py b/backends/xnnpack/test/ops/test_slice_copy.py
index ea65571b1e8..857c78480ad 100644
--- a/backends/xnnpack/test/ops/test_slice_copy.py
+++ b/backends/xnnpack/test/ops/test_slice_copy.py
@@ -69,6 +69,18 @@ def forward(self, x):
         # Note that two of the slices are optimized away as they are identity.
         self._test_slice_copy(ConvSlice(), inputs, 4, 2)
 
+    def test_fp32_slice_copy_default_start(self):
+        """
+        XNNPACK supports default start in slice op.
+        """
+
+        class Slice(torch.nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.slice.Tensor(x, 0, None, 2)
+
+        inputs = (torch.randn(5, 5),)
+        self._test_slice_copy(Slice(), inputs, 1, 1)
+
     def test_fp32_slice_copy_stride_non_1(self):
         """
         XNNPACK does not support strided slicing.