diff --git a/onnxruntime/core/providers/nuphar/scripts/rnn_benchmark.py b/onnxruntime/core/providers/nuphar/scripts/rnn_benchmark.py
index baa16888c6..821a02cbb3 100644
--- a/onnxruntime/core/providers/nuphar/scripts/rnn_benchmark.py
+++ b/onnxruntime/core/providers/nuphar/scripts/rnn_benchmark.py
@@ -121,7 +121,7 @@ def perf_test(rnn_type, num_threads, input_dim, hidden_dim, bidirectional, layer
         convert_to_scan_model(model_name, scan_model_name)
         # note that symbolic shape inference is needed because model has symbolic batch dim, thus init_state is ConstantOfShape
         onnx.save(SymbolicShapeInference.infer_shapes(onnx.load(scan_model_name)), scan_model_name)
-        sess = onnxruntime.InferenceSession(scan_model_name)
+        sess = onnxruntime.InferenceSession(scan_model_name, providers=onnxruntime.get_available_providers())
         count, duration, per_iter_cost = perf_run(sess, feeds, min_counts=top_n, min_duration_seconds=min_duration_seconds)
         avg_scan = top_n_avg(per_iter_cost, top_n)
         print('perf_scan (with {} threads) {}: run for {} iterations, top {} avg {:.3f} ms'.format(num_threads, scan_model_name, count, top_n, avg_scan))
@@ -131,7 +131,7 @@ def perf_test(rnn_type, num_threads, input_dim, hidden_dim, bidirectional, layer
         int8_model_name = os.path.splitext(model_name)[0] + '_int8.onnx'
         convert_matmul_model(scan_model_name, int8_model_name)
         onnx.save(SymbolicShapeInference.infer_shapes(onnx.load(int8_model_name)), int8_model_name)
-        sess = onnxruntime.InferenceSession(int8_model_name)
+        sess = onnxruntime.InferenceSession(int8_model_name, providers=onnxruntime.get_available_providers())
         count, duration, per_iter_cost = perf_run(sess, feeds, min_counts=top_n, min_duration_seconds=min_duration_seconds)
         avg_int8 = top_n_avg(per_iter_cost, top_n)
         print('perf_int8 (with {} threads) {}: run for {} iterations, top {} avg {:.3f} ms'.format(num_threads, int8_model_name, count, top_n, avg_int8))
diff --git a/onnxruntime/python/onnxruntime_inference_collection.py b/onnxruntime/python/onnxruntime_inference_collection.py
index b0a86bce64..f753ccff31 100644
--- a/onnxruntime/python/onnxruntime_inference_collection.py
+++ b/onnxruntime/python/onnxruntime_inference_collection.py
@@ -357,6 +357,7 @@ class InferenceSession(Session):
                                                                         provider_options,
                                                                         available_providers)
         if providers == [] and len(available_providers) > 1:
+            self.disable_fallback()
             raise ValueError("This ORT build has {} enabled. ".format(available_providers) +
                              "Since ORT 1.9, you are required to explicitly set " +
                              "the providers parameter when instantiating InferenceSession. For example, "
diff --git a/onnxruntime/python/tools/onnxruntime_test.py b/onnxruntime/python/tools/onnxruntime_test.py
index c1f809074c..0d4cc22be3 100644
--- a/onnxruntime/python/tools/onnxruntime_test.py
+++ b/onnxruntime/python/tools/onnxruntime_test.py
@@ -71,7 +71,7 @@ def run_model(model_path,
         sess_options.enable_profiling = True
         sess_options.profile_file_prefix = os.path.basename(model_path)
 
-    sess = onnxrt.InferenceSession(model_path, sess_options)
+    sess = onnxrt.InferenceSession(model_path, sess_options=sess_options, providers=onnxrt.get_available_providers())
     meta = sess.get_modelmeta()
 
     if not feeds: