diff --git a/tensorflow/core/kernels/broadcast_to_op.cc b/tensorflow/core/kernels/broadcast_to_op.cc
index a1e5025ee87..ab9961ced9c 100644
--- a/tensorflow/core/kernels/broadcast_to_op.cc
+++ b/tensorflow/core/kernels/broadcast_to_op.cc
@@ -80,6 +80,10 @@ class BroadcastToOp : public OpKernel {
                 errors::InvalidArgument(
                     "Incompatible shapes: ", input_shape.DebugString(), " vs. ",
                     output_shape.DebugString()));
+    OP_REQUIRES(ctx, BCast::ToShape(bcast.output_shape()) == output_shape,
+                errors::InvalidArgument("Unable to broadcast tensor of shape ",
+                                        input_shape, " to tensor of shape ",
+                                        output_shape));
 
     functor::BroadcastTo<Device, T>()(device, ctx, *output_tensor, output_shape,
                                       input_tensor, input_shape, bcast);
diff --git a/tensorflow/python/kernel_tests/broadcast_to_ops_test.py b/tensorflow/python/kernel_tests/broadcast_to_ops_test.py
index 3c5433cb899..f478ee9f643 100644
--- a/tensorflow/python/kernel_tests/broadcast_to_ops_test.py
+++ b/tensorflow/python/kernel_tests/broadcast_to_ops_test.py
@@ -19,8 +19,10 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
@@ -127,6 +129,14 @@ class BroadcastToTest(test_util.TensorFlowTestCase):
         # check shape inference when shape input is constant
         self.assertAllEqual(shape, v_np.shape)
 
+  def testBroadcastToBadOutputShape(self):
+    with context.eager_mode():
+      with self.assertRaisesRegexp(errors.InvalidArgumentError,
+                                   "Unable to broadcast tensor of shape"):
+        self.evaluate(
+            array_ops.broadcast_to(
+                constant_op.constant([0, 1]), constant_op.constant([2, 1])))
+
   @test_util.run_deprecated_v1
   def testGradientForScalar(self):
     x = constant_op.constant(1, dtype=dtypes.float32)