diff --git a/onnx/defs/data_propagators.h b/onnx/defs/data_propagators.h
index f87379b44f8..8948953708b 100644
--- a/onnx/defs/data_propagators.h
+++ b/onnx/defs/data_propagators.h
@@ -33,18 +33,21 @@ inline bool axisIsZero(DataPropagationContext& ctx, bool defaultZero = false) {
     }
   }
   int axis = static_cast<int>(axisAttr->i());
-  auto input_data_0 = ctx.getInputData(0);
-  if (input_data_0 == nullptr) {
+  if (axis >= 0) {
+    return axis == 0;
+  }
+  // For negative axes, we need rank information to determine if it is equivalent to axis 0
+  const TypeProto* type = ctx.getInputType(0);
+  if ((type == nullptr) || (!type->has_tensor_type()) || (!type->tensor_type().has_shape())) {
     return false;
   }
-  int rank = input_data_0->dim_size();
+
+  int rank = type->tensor_type().shape().dim_size();
   if (axis < -rank || axis >= rank) {
     fail_shape_inference("axis must be in [-rank, rank-1].");
     return false;
   }
-  if (axis < 0) {
-    axis += rank;
-  }
+  axis += rank;
   // Only supports axis = 0 since the data comes from Shape
   return axis == 0;
 }
diff --git a/onnx/test/data_propagation_test.py b/onnx/test/data_propagation_test.py
index 83f7fa7396b..3638c12598b 100644
--- a/onnx/test/data_propagation_test.py
+++ b/onnx/test/data_propagation_test.py
@@ -212,6 +212,40 @@ def test_shape_arithmetic_with_zero_broadcast(self) -> None:
             data_prop=True,
         )  # type: ignore
 
+    def test_empty_tensor(self) -> None:
+        """Test that a Concat with an empty tensor as input is handled correctly by data-propagation."""
+        model = onnx.parser.parse_model(
+            """
+            <ir_version: 7, opset_import: [ "" : 17]>
+            agraph (float[256] y) => (float[N] z)
+            <float[0] x = {}>
+            {
+                z = Concat <axis=0> (x, y)
+            }
+        """
+        )
+        inferred_model = onnx.shape_inference.infer_shapes(model, True, True, True)
+        output = inferred_model.graph.output[0]
+        self.assertEqual(output.type.tensor_type.shape.dim[0].dim_value, 256)
+
+    def test_empty_tensor_negative_axis(self) -> None:
+        """Test that a Concat with an empty tensor as input is handled correctly by data-propagation.
+        This time with a negative axis.
+        """
+        model = onnx.parser.parse_model(
+            """
+            <ir_version: 7, opset_import: [ "" : 17]>
+            agraph (float[256] y) => (float[N] z)
+            <float[0] x = {}>
+            {
+                z = Concat <axis=-1> (x, y)
+            }
+        """
+        )
+        inferred_model = onnx.shape_inference.infer_shapes(model, True, True, True)
+        output = inferred_model.graph.output[0]
+        self.assertEqual(output.type.tensor_type.shape.dim[0].dim_value, 256)
+
 
 if __name__ == "__main__":
     unittest.main()