diff --git a/.github/workflows/ci_gpu.yml b/.github/workflows/ci_gpu.yml
index a908656f..7be73e94 100644
--- a/.github/workflows/ci_gpu.yml
+++ b/.github/workflows/ci_gpu.yml
@@ -84,9 +84,9 @@ jobs:
           mkdir -p runs/cifar10/test-reports
           pip install tensorboard
           python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda
-          python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
+          python -c "import torch; model = torch.load('model_best.pth.tar', weights_only=False); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
           python examples/cifar10.py --lr 0.1 --sigma 1.5 -c 10 --batch-size 2000 --epochs 10 --data-root runs/cifar10/data --log-dir runs/cifar10/logs --device cuda --grad_sample_mode no_op
-          python -c "import torch; model = torch.load('model_best.pth.tar'); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
+          python -c "import torch; model = torch.load('model_best.pth.tar', weights_only=False); exit(0) if (model['best_acc1']>0.4 and model['best_acc1']<0.49) else exit(1)"
 
       - name: Store CIFAR10 test results
         uses: actions/upload-artifact@v4
diff --git a/benchmarks/tests/test_utils.py b/benchmarks/tests/test_utils.py
index 37649ae8..3bf9d1a1 100644
--- a/benchmarks/tests/test_utils.py
+++ b/benchmarks/tests/test_utils.py
@@ -194,7 +194,7 @@ def pickle_data_and_config(
     ],
 )
 def test_save_results(
-    pickle_data_and_config: Tuple[Dict[str, Any], Dict[str, Any]]
+    pickle_data_and_config: Tuple[Dict[str, Any], Dict[str, Any]],
 ) -> None:
     """Tests saving benchmark results.
 
diff --git a/opacus/grad_sample/utils.py b/opacus/grad_sample/utils.py
index f4011361..9f8cbd7b 100644
--- a/opacus/grad_sample/utils.py
+++ b/opacus/grad_sample/utils.py
@@ -27,7 +27,7 @@
 
 
 def register_grad_sampler(
-    target_class_or_classes: Union[Type[nn.Module], Sequence[Type[nn.Module]]]
+    target_class_or_classes: Union[Type[nn.Module], Sequence[Type[nn.Module]]],
 ):
     """
     Registers the decorated function as the ``grad_sampler`` of ``target_class_or_classes``, which is
@@ -56,7 +56,7 @@ def decorator(f):
 
 
 def register_norm_sampler(
-    target_class_or_classes: Union[Type[nn.Module], Sequence[Type[nn.Module]]]
+    target_class_or_classes: Union[Type[nn.Module], Sequence[Type[nn.Module]]],
 ):
     """
     Registers the decorated function as the ``norm_sampler`` of ``target_class_or_classes``, which is
diff --git a/opacus/privacy_engine.py b/opacus/privacy_engine.py
index 558c8f8e..cacacee0 100644
--- a/opacus/privacy_engine.py
+++ b/opacus/privacy_engine.py
@@ -603,7 +603,7 @@ def load_checkpoint(
         module_load_dict_kwargs: Optional[Dict[str, Any]] = None,
         torch_load_kwargs: Optional[Dict[str, Any]] = None,
     ) -> Dict:
-        checkpoint = torch.load(path, **(torch_load_kwargs or {}))
+        checkpoint = torch.load(path, **(torch_load_kwargs or {}), weights_only=False)
         module.load_state_dict(
             checkpoint["module_state_dict"], **(module_load_dict_kwargs or {})
         )
diff --git a/opacus/utils/per_sample_gradients_utils.py b/opacus/utils/per_sample_gradients_utils.py
index 754d86d6..7703b77c 100644
--- a/opacus/utils/per_sample_gradients_utils.py
+++ b/opacus/utils/per_sample_gradients_utils.py
@@ -39,7 +39,7 @@ def clone_module(module: nn.Module) -> nn.Module:
     with io.BytesIO() as bytesio:
         torch.save(module, bytesio)
         bytesio.seek(0)
-        module_copy = torch.load(bytesio)
+        module_copy = torch.load(bytesio, weights_only=False)
     return module_copy