diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
--- a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
@@ -1438,6 +1438,7 @@
 
   let arguments = (ins PDL_Operation:$target,
                        Variadic<PDL_Operation>:$vector_sizes,
+                       UnitAttr:$vectorize_nd_extract,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:
                           $static_vector_sizes);
   let results = (outs);
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -2442,11 +2442,10 @@
       return DiagnosedSilenceableFailure::silenceableFailure(std::move(diag));
     }
 
-    if (failed(linalg::vectorize(rewriter, linalgOp, vectorSizes))) {
-      Diagnostic diag(target->getLoc(), DiagnosticSeverity::Error);
-      diag << "failed to vectorize op";
-      return DiagnosedSilenceableFailure::silenceableFailure(std::move(diag));
-    }
+    if (failed(linalg::vectorize(rewriter, linalgOp, vectorSizes,
+                                 getVectorizeNdExtract()))) {
+      return DiagnosedSilenceableFailure::success();
+      }
   }
 
   return DiagnosedSilenceableFailure::success();
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -1936,6 +1936,35 @@
 
 // -----
 
+// Masked vectorisation requires of `tensor.extract` requires the `{ vectorize_nd_extract }` attribute.
+// TOOD: Implement masked vectorization for `tensor.extract`
+
+#map1 = affine_map<(d0, d1) -> (d0, d1)>
+func.func @extract_masked_vectorize(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %c0 = arith.constant 1 : index
+  %c1 = arith.constant 2 : index
+  %2 = linalg.generic {
+    indexing_maps = [#map1],
+    iterator_types = ["parallel", "parallel"]
+  } outs(%arg1 : tensor<?x?xf32>) {
+  ^bb0(%arg3: f32):
+    %7 = tensor.extract %arg0[%c0, %c1] : tensor<?x?xf32>
+    linalg.yield %7 : f32
+  } -> tensor<?x?xf32>
+  return %2 : tensor<?x?xf32>
+}
+
+// CHECK-LABEL: @extract_masked_vectorize
+// CHECK-NOT: vector
+
+transform.sequence failures(propagate) {
+ ^bb1(%arg1: !pdl.operation):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1
+   transform.structured.masked_vectorize %0 vector_sizes [3, 3]
+ }
+
+// -----
+
 // This is a regression test. This IR cannot be vectorized, but
 // structured.vectorize should nevertheless succeed.