diff --git a/tensorflow/core/kernels/sparse/sparse_matrix.h b/tensorflow/core/kernels/sparse/sparse_matrix.h
index b2ec20acc658489c81dd04a6ccee98e272af80cb..15fbe5df6ebe2936d65f373cb2f7711c8b735336 100644
--- a/tensorflow/core/kernels/sparse/sparse_matrix.h
+++ b/tensorflow/core/kernels/sparse/sparse_matrix.h
@@ -25,10 +25,12 @@ limitations under the License.
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/variant.h"
 #include "tensorflow/core/framework/variant_encode_decode.h"
 #include "tensorflow/core/framework/variant_op_registry.h"
+#include "tensorflow/core/platform/errors.h"
 
 namespace tensorflow {
 
@@ -633,6 +635,11 @@ template <typename T>
 Status ExtractVariantFromInput(OpKernelContext* ctx, int index,
                                const T** value) {
   const Tensor& input_t = ctx->input(index);
+  if (!TensorShapeUtils::IsScalar(input_t.shape())) {
+    return errors::InvalidArgument(
+        "Invalid input matrix: Shape must be rank 0 but is rank ",
+        input_t.dims());
+  }
   const Variant& input_variant = input_t.scalar<Variant>()();
   *value = input_variant.get<T>();
   if (*value == nullptr) {
diff --git a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
index 035791ce0a5bf545d5cb05e9def9ac0398336a12..d129bea768e85fb493e16ef7c1fc3848eac6e6ab 100644
--- a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
+++ b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
@@ -1313,6 +1313,16 @@ class CSRSparseMatrixOpsTest(test.TestCase):
       self.assertLess(cholesky_with_amd_nnz_value,
                       cholesky_without_ordering_nnz_value)
 
+  @test_util.run_in_graph_and_eager_modes
+  def testNoMatrixNoCrash(self):
+    # Round-about way of creating an empty variant tensor that works in both
+    # graph and eager modes.
+    no_matrix = array_ops.reshape(dense_to_csr_sparse_matrix([[0.0]]), [1])[0:0]
+    with self.assertRaisesRegex(
+        (ValueError, errors.InvalidArgumentError),
+        "(Invalid input matrix)|(Shape must be rank 0)"):
+      sparse_csr_matrix_ops.sparse_matrix_nnz(no_matrix)
+
 
 class CSRSparseMatrixOpsBenchmark(test.Benchmark):