diff --git a/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc b/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc
index b953785478..9f4ff8aa26 100644
--- a/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc
+++ b/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc
@@ -808,6 +808,20 @@ void UniDirectionalGru<T>::Compute(const gsl::span<const T>& inputs_arg,
     }
   }
 
+  // zero any values beyond the evaluated steps
+  if (output_sequence && max_sequence_length < seq_length_) {
+    if (output_step_length == batch_size_ * hidden_size_) {  // contiguous
+      const auto span_to_zero = outputs.subspan(
+          max_sequence_length * output_step_length, (seq_length_ - max_sequence_length) * output_step_length);
+      std::fill_n(span_to_zero.begin(), span_to_zero.size(), T{});
+    } else {
+      for (int i = max_sequence_length; i < seq_length_; ++i) {  // non-contiguous
+        const auto span_to_zero = outputs.subspan(i * output_step_length, batch_size_ * hidden_size_);
+        std::fill_n(span_to_zero.begin(), span_to_zero.size(), T{});
+      }
+    }
+  }
+
   if (output_sequence && direction_ == kReverse) {
     ReverseSequence<T>(outputs, original_outputs,
                        sequence_lengths, seq_length_,
diff --git a/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc b/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc
index c5d05f8e4f..d2876f161e 100644
--- a/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc
+++ b/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc
@@ -971,6 +971,20 @@ void UniDirectionalLstm<T>::Compute(const gsl::span<const T>& inputs_arg,
     }
   }
 
+  // zero any values beyond the evaluated steps
+  if (output_sequence && max_sequence_length < seq_length_) {
+    if (output_step_length == batch_size_ * hidden_size_) {  // contiguous
+      const auto span_to_zero = outputs.subspan(
+          max_sequence_length * output_step_length, (seq_length_ - max_sequence_length) * output_step_length);
+      std::fill_n(span_to_zero.begin(), span_to_zero.size(), T{});
+    } else {
+      for (int i = max_sequence_length; i < seq_length_; ++i) {  // non-contiguous
+        const auto span_to_zero = outputs.subspan(i * output_step_length, batch_size_ * hidden_size_);
+        std::fill_n(span_to_zero.begin(), span_to_zero.size(), T{});
+      }
+    }
+  }
+
   if (output_sequence && direction_ == Direction::kReverse)
     ReverseSequence<T>(outputs, original_outputs, sequence_lengths, seq_length_,
                        batch_size_, hidden_size_, num_directions);
diff --git a/onnxruntime/test/providers/cpu/rnn/deep_cpu_gru_op_test.cc b/onnxruntime/test/providers/cpu/rnn/deep_cpu_gru_op_test.cc
index e4143d529e..e0010423b1 100644
--- a/onnxruntime/test/providers/cpu/rnn/deep_cpu_gru_op_test.cc
+++ b/onnxruntime/test/providers/cpu/rnn/deep_cpu_gru_op_test.cc
@@ -241,7 +241,7 @@ void DefaultActivationsSimpleWeightsWithBias(std::string direction,
 
   RunGruTest(X_data, W_data, R_data, Y_data, {}, input_size, batch_size, hidden_size, seq_length,
              &B_data, nullptr, nullptr, direction, 999.f, /* output_sequence*/ true, linear_before_reset);
-}  // namespace test
+}
 
 TEST(GRUTest, ForwardDefaultActivationsSimpleWeightsWithBiasBatchParallel) {
   std::vector<float> Y_data{
@@ -336,7 +336,7 @@ class DeepCpuGruOpTestContext {
   std::vector<float> gru_input_weights_;
   std::vector<float> gru_recurrent_weights_;
   std::vector<float> gru_bias_;
-};  // namespace test
+};
 
 DeepCpuGruOpTestContext::DeepCpuGruOpTestContext(const std::string direction,
                                                  const std::vector<std::string>& activations,
@@ -459,7 +459,7 @@ void DeepCpuGruOpTestContext::RunTest(const std::vector<float>& X,
                                       const std::vector<float>& expected_Y,
                                       const std::vector<float>& expected_Y_h,
                                       const bool linear_before_reset) {
-   //run with and without output_sequence
+  //run with and without output_sequence
   ::onnxruntime::test::RunGruTest(X, gru_input_weights_, gru_recurrent_weights_,
                                   expected_Y, expected_Y_h,
                                   input_size_, batch_size, hidden_dim_, seq_length,
@@ -469,7 +469,7 @@ void DeepCpuGruOpTestContext::RunTest(const std::vector<float>& X,
                                   direction_,
                                   9999999999.f,
                                   /*output_sequence*/ true,
-                                   linear_before_reset,
+                                  linear_before_reset,
                                   activation_func_names_,
                                   alphas_,
                                   betas_);
@@ -819,6 +819,35 @@ TEST(GRUTest, ONNXRuntime_TestGRUOpSequenceLengthWithPartialZero) {
   ctx.RunTest(X, batch_size, seq_length, sequence_length, &initial_h, expected_Y, expected_Y_h);
 }
 
+TEST(GRUTest, ONNXRuntime_TestGRUOpSequenceLengthShorterThanInputSequenceLength) {
+  const std::string direction = "bidirectional";
+  const std::vector<std::string> activations = {"sigmoid", "tanh", "sigmoid", "tanh"};
+
+  DeepCpuGruOpTestContext ctx(direction, activations);
+
+  const int batch = 1;
+  const int seq_length = 2;
+
+  std::vector<float> X = {-0.455351f, -0.276391f,
+                          -0.185934f, -0.269585f};
+
+  std::vector<int> sequence_lengths = {1};
+
+  std::vector<float> initial_h = {0.0f, 0.0f,
+                                  -0.04566499f, 0.04621252f};
+
+  std::vector<float> expected_Y = {-0.03255286f, 0.0774838f,
+                                   -0.05469977f, 0.1004222f,
+
+                                   0.0f, 0.0f,
+                                   0.0f, 0.0f};
+
+  std::vector<float> expected_Y_h = {-0.03255286f, 0.0774838f,
+                                     -0.05469977f, 0.1004222f};
+
+  ctx.RunTest(X, batch, seq_length, sequence_lengths, &initial_h, expected_Y, expected_Y_h);
+}
+
 TEST(GRUTest, ONNXRuntime_TestGRUOpSequenceLengthAllZeros) {
   const std::string direction = "forward";
   const std::vector<std::string> activations = {"sigmoid", "tanh"};
diff --git a/onnxruntime/test/providers/cpu/rnn/deep_cpu_lstm_op_test.cc b/onnxruntime/test/providers/cpu/rnn/deep_cpu_lstm_op_test.cc
index 62de9ab143..eaa0cc649f 100644
--- a/onnxruntime/test/providers/cpu/rnn/deep_cpu_lstm_op_test.cc
+++ b/onnxruntime/test/providers/cpu/rnn/deep_cpu_lstm_op_test.cc
@@ -1059,5 +1059,38 @@ TEST(LSTMTest, ONNXRuntime_TestLSTMSequenceLengthPartialZeros) {
                   &sequence_length, use_bias, use_peepholes);
 }
 
+// TODO this test fails for nGraph - need to investigate why
+#ifndef USE_NGRAPH
+TEST(LSTMTest, ONNXRuntime_TestLSTMSequenceLengthShorterThanInputSequenceLength) {
+  const int seq_len = 2;
+  const int batch_size = 1;
+
+  std::vector<float> X_data = {-0.455351f, -0.276391f,
+                               -0.185934f, -0.269585f};
+
+  std::vector<int> sequence_length = {1};
+
+  std::vector<float> initial_h = {0.0f, 0.0f,
+                                  -0.0306872f, 0.028035f};
+
+  std::vector<float> initial_c = {0.0f, 0.0f,
+                                  -0.07243599f, 0.0467052f};
+
+  std::vector<float> Y_data = {-0.0251062f, 0.0561262f,
+                               -0.0318928f, 0.0762679f,
+
+                               0.0f, 0.0f,
+                               0.0f, 0.0f};
+
+  std::vector<float> Y_h_data = {-0.0251062f, 0.0561262f,
+                                 -0.0318928f, 0.0762679f};
+
+  std::string direction = "bidirectional";
+
+  LstmOpContext2x1x2x2 context(direction);
+  context.RunTest(X_data, batch_size, seq_len, &initial_h, &initial_c, Y_data, Y_h_data, {}, &sequence_length);
+}
+#endif // USE_NGRAPH
+
 }  // namespace test
 }  // namespace onnxruntime