diff --git a/experiments/attack_defense_metric_test.py b/experiments/attack_defense_metric_test.py
index 7bdc547..e2bd79e 100644
--- a/experiments/attack_defense_metric_test.py
+++ b/experiments/attack_defense_metric_test.py
@@ -1,3 +1,4 @@
+import copy
 import warnings
 
 import torch
@@ -140,7 +141,7 @@ def attack_defense_metrics():
     # print(metric_loc)
 
     adm = FrameworkAttackDefenseManager(
-        gen_dataset=dataset,
+        gen_dataset=copy.deepcopy(dataset),
         gnn_manager=gnn_model_manager,
     )
     # adm.evasion_attack_pipeline(
diff --git a/src/models_builder/attack_defense_manager.py b/src/models_builder/attack_defense_manager.py
index 3b70097..4e0f98d 100644
--- a/src/models_builder/attack_defense_manager.py
+++ b/src/models_builder/attack_defense_manager.py
@@ -1,3 +1,4 @@
+import copy
 import json
 import os
 import warnings
@@ -89,14 +90,15 @@ def evasion_attack_pipeline(
             self.gnn_manager.modification.epochs = 0
             self.gnn_manager.gnn.reset_parameters()
             from models_builder.gnn_models import Metric
+            local_gen_dataset_copy = copy.deepcopy(self.gen_dataset)
             self.gnn_manager.train_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 steps=steps,
-                save_model_flag=save_model_flag,
+                save_model_flag=False,
                 metrics=[Metric("F1", mask='train', average=None)]
             )
             y_predict_clean = self.gnn_manager.run_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 mask=mask,
                 out='logits',
             )
@@ -105,17 +107,17 @@ def evasion_attack_pipeline(
             self.gnn_manager.modification.epochs = 0
             self.gnn_manager.gnn.reset_parameters()
             self.gnn_manager.train_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 steps=steps,
                 save_model_flag=save_model_flag,
                 metrics=[Metric("F1", mask='train', average=None)]
             )
             self.gnn_manager.call_evasion_attack(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 mask=mask,
             )
             y_predict_attack = self.gnn_manager.run_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 mask=mask,
                 out='logits',
             )
@@ -152,14 +154,15 @@ def poison_attack_pipeline(
             self.gnn_manager.modification.epochs = 0
             self.gnn_manager.gnn.reset_parameters()
             from models_builder.gnn_models import Metric
+            local_gen_dataset_copy = copy.deepcopy(self.gen_dataset)
             self.gnn_manager.train_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 steps=steps,
                 save_model_flag=False,
                 metrics=[Metric("F1", mask='train', average=None)]
             )
             y_predict_clean = self.gnn_manager.run_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 mask=mask,
                 out='logits',
             )
@@ -168,13 +171,13 @@ def poison_attack_pipeline(
             self.gnn_manager.modification.epochs = 0
             self.gnn_manager.gnn.reset_parameters()
             self.gnn_manager.train_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 steps=steps,
                 save_model_flag=save_model_flag,
                 metrics=[Metric("F1", mask='train', average=None)]
             )
             y_predict_attack = self.gnn_manager.run_model(
-                gen_dataset=self.gen_dataset,
+                gen_dataset=local_gen_dataset_copy,
                 mask=mask,
                 out='logits',
             )