diff --git a/onnxscript/rewriter/collapse_slices.py b/onnxscript/rewriter/collapse_slices.py
index 2615432e7..689557af1 100644
--- a/onnxscript/rewriter/collapse_slices.py
+++ b/onnxscript/rewriter/collapse_slices.py
@@ -56,7 +56,7 @@ def _check_if_redundant_slice(
     # In case data.shape is not statically known, we still can tell the slice is redundant if ends is sys.maxsize
     if ends_const.numpy().item() == _INT64_MAX:
         return True
-    if data.shape is None:
+    if data.shape is None or data.shape.is_dynamic(axes_const.numpy().item()):
         logger.info("The value 'data' shape is not statically known.")
         return False
     if ends_const.numpy().item() < data.shape[axes_const.numpy().item()]:
diff --git a/onnxscript/rewriter/collapse_slices_test.py b/onnxscript/rewriter/collapse_slices_test.py
index 8632f61ca..6a11bd202 100644
--- a/onnxscript/rewriter/collapse_slices_test.py
+++ b/onnxscript/rewriter/collapse_slices_test.py
@@ -65,6 +65,24 @@ def test_slice_is_redundant_when_ends_reaches_int64_max(self):
             (np.random.rand(512, 16, 112).astype(np.float32),),
         )
 
+    def test_slice_pattern_is_not_matched_when_input_is_dynamic(self):
+        model_proto = onnx.parser.parse_model(
+            f"""
+            <ir_version: 7, opset_import: [ "" : 17]>
+            agraph (float[L, M, N] data) => (float[L, M, N] output)
+            {{
+                starts = Constant<value: tensor = int64[1] {{0}}>()
+                ends = Constant<value: tensor = int64[1] {{{9}}}>()
+                axes = Constant<value: tensor = int64[1] {{0}}>()
+                steps = Constant<value: tensor = int64[1] {{1}}>()
+                output = Slice (data, starts, ends, axes, steps)
+            }}
+        """
+        )
+        model = ir.serde.deserialize_model(model_proto)
+        count = collapse_slices.rules.apply_to_model(model)
+        self.assertEqual(count, 0)
+
     def test_scatternd_is_redundant_when_it_is_updating_the_whole_input_in_order(self):
         model_proto = onnx.parser.parse_model(
             """