diff --git a/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp b/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
index 834c02126fa53..f24310ecd7beb 100644
--- a/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
+++ b/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
@@ -455,6 +455,8 @@ static LogicalResult generateLoopNestUsingForOp(
     rewriter.setInsertionPointToEnd(loop.getBody());
     destinationTensors = loop.getRegionIterArgs();
   }
+  if (loops.empty())
+    return success();
 
   SmallVector<Value> tiledResults;
   SmallVector<SmallVector<OpFoldResult>> resultOffsets, resultSizes;
@@ -463,9 +465,6 @@ static LogicalResult generateLoopNestUsingForOp(
     return rewriter.notifyMatchFailure(
         loc, "failed to generate inner tile loop body");
   }
-  if (loops.empty())
-    return success();
-
   assert(tiledResults.size() == destinationTensors.size() &&
          "Number of results of body should be equal to number of iter args");
 
diff --git a/mlir/test/Dialect/Linalg/transform-op-tile.mlir b/mlir/test/Dialect/Linalg/transform-op-tile.mlir
index 7bac850d0b7fe..0466a7ba3e2ea 100644
--- a/mlir/test/Dialect/Linalg/transform-op-tile.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-tile.mlir
@@ -266,3 +266,23 @@ func.func @tile_linalg_matmul(
     -> tensor<128x128xf32>
   return %0 : tensor<128x128xf32>
 }
+
+// -----
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    // expected-error @below {{op expected number of loops to tile (0) to match number of `loops` results (1)}}
+    %1, %loops = transform.structured.tile_using_for %0 tile_sizes [0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.yield
+  }
+}
+
+func.func @tile_linalg_matmul(
+  %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>)
+    -> tensor<128x128xf32> {
+  %0 = linalg.matmul  ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>)
+                     outs(%arg2: tensor<128x128xf32>)
+    -> tensor<128x128xf32>
+  return %0 : tensor<128x128xf32>
+}