diff --git a/URSABench/tasks/fgsm_attack.py b/URSABench/tasks/fgsm_attack.py
new file mode 100644
index 0000000..95c58fa
--- /dev/null
+++ b/URSABench/tasks/fgsm_attack.py
@@ -0,0 +1,84 @@
+import torch
+import torch.nn.functional as F
+
+from .task_base import _Task
+
+__all__ = ['FGSM_attack']
+
+
+class FGSM_attack(_Task):
+
+    def __init__(self, dataloader, num_classes, device, metric_list, l_inf_norm=0.1):
+        super(FGSM_attack, self).__init__(dataloader, num_classes, device)
+        self.l_inf_norm = 0.1
+        self.data_loader = dataloader['in_distribution_test']
+        self.num_classes = num_classes
+        self.device = device
+        self.num_samples_collected = 0
+        self.ensemble_proba = torch.zeros(len(self.data_loader.dataset), num_classes)
+        self.targets = list()
+        for batch_idx, (batch_data, batch_labels) in enumerate(self.data_loader):
+            self.targets.append(batch_labels)
+        self.targets = torch.cat(self.targets)
+
+    def reset(self):
+        self.num_samples_collected = 0
+        self.ensemble_proba = torch.zeros(len(self.data_loader.dataset), self.num_classes)
+
+    def generate_FGSM_adversarial_examples(self, models):
+        """
+        Input : Models
+        [Used Class Variables:
+            -  Input_example,
+            -  variables defining pgd attack
+        ]
+        Output : adeversarial examples
+        """
+
+        if isinstance(models, list):
+            if all(issubclass(model.__class__, torch.nn.Module) for model in models):
+                num_models = len(models)
+                self.num_samples_collected += num_models
+            else:
+                raise NotImplementedError
+        else:
+            if issubclass(models.__class__, torch.nn.Module):
+                self.num_samples_collected += 1
+            else:
+                raise NotImplementedError
+
+        output_adversarial_examples = list()
+        start_idx = 0
+
+        for batch_idx, (batch_data, batch_labels) in enumerate(self.data_loader):
+            end_idx = start_idx + len(batch_data)
+            batch_data = batch_data.to(self.device)
+            delta = torch.zeros_like(batch_data, requires_grad=True)
+
+            if isinstance(models, list):
+                for model_idx, model in enumerate(models):
+                    model.to(self.device)
+                    batch_logits = model(batch_data + delta)
+                    self.ensemble_proba[start_idx: end_idx] += F.log_softmax(batch_logits, dim=-1).exp_().cpu()
+                    model.to('cpu')
+            else:
+                # Here models indicates a single model.
+                models.to(self.device)
+                batch_logits = models(batch_data + delta)
+                self.ensemble_proba[start_idx: end_idx] += F.log_softmax(batch_logits, dim=-1).exp_().cpu()
+                models.to('cpu')
+
+            targets_this_batch = self.targets[start_idx: end_idx]
+
+            # Keeping reduction method 'none' to get loss
+            # contibution of every data-case individually so that gradient
+            # will be calculated properly as per formula of FGSM
+            log_likelihood = F.nll_loss(torch.log(self.ensemble_proba[start_idx: end_idx]/self.num_samples_collected), targets_this_batch, reduction='none')
+            log_likelihood.backward()
+            batch_data = batch_data + self.l_inf_norm * delta.grad.detach().sign()
+            output_adversarial_examples.append(batch_data)
+            start_idx = end_idx
+
+        output_adversarial_examples = torch.cat(output_adversarial_examples)
+
+        return output_adversarial_examples
diff --git a/URSABench/tasks/pgd_attack.py b/URSABench/tasks/pgd_attack.py
new file mode 100644
index 0000000..849c997
--- /dev/null
+++ b/URSABench/tasks/pgd_attack.py
@@ -0,0 +1,96 @@
+import torch
+import torch.nn.functional as F
+
+from .task_base import _Task
+
+__all__ = ['PGD_attack']
+
+
+class PGD_attack(_Task):
+
+    def __init__(self, dataloader, num_classes, device, metric_list, alpha=1e-2, attack_iters=40, l_inf_norm=0.1):
+        super(PGD_attack, self).__init__(dataloader, num_classes, device)
+        self.alpha = alpha
+        self.attack_iters = attack_iters
+        self.l_inf_norm = l_inf_norm
+        self.data_loader = dataloader['in_distribution_test']
+        self.num_classes = num_classes
+        self.device = device
+        self.num_samples_collected = 0
+        self.ensemble_proba = torch.zeros(len(self.data_loader.dataset), num_classes)
+        self.targets = list()
+        for batch_idx, (batch_data, batch_labels) in enumerate(self.data_loader):
+            self.targets.append(batch_labels)
+        self.targets = torch.cat(self.targets)
+
+    def reset(self):
+        self.num_samples_collected = 0
+        self.ensemble_proba = torch.zeros(len(self.data_loader.dataset), self.num_classes)
+
+    def generate_PGD_adversarial_examples(self, models):
+        """
+        Input : Models
+        [Used Class Variables:
+            -  Input_example,
+            -  variables defining pgd attack
+                - step_size(alpha)
+                - l_inf_norm_bound(l_inf_norm)
+                - number of iterations(attack_iters)
+        ]
+        Output : adeversarial examples
+        """
+
+        if isinstance(models, list):
+            if all(issubclass(model.__class__, torch.nn.Module) for model in models):
+                num_models = len(models)
+                self.num_samples_collected += num_models
+            else:
+                raise NotImplementedError
+        else:
+            if issubclass(models.__class__, torch.nn.Module):
+                self.num_samples_collected += 1
+            else:
+                raise NotImplementedError
+
+        output_adversarial_examples = list()
+        start_idx = 0
+
+        for batch_idx, (batch_data, batch_labels) in enumerate(self.data_loader):
+
+            delta = torch.zeros_like(batch_data, requires_grad=True)
+            # delta will be updated in every attack iteration
+
+            for t in range(self.attack_iters):
+                end_idx = start_idx + len(batch_data)
+                batch_data = batch_data.to(self.device)
+
+                if isinstance(models, list):
+                    for model_idx, model in enumerate(models):
+                        model.to(self.device)
+                        batch_logits = model(batch_data + delta)
+                        self.ensemble_proba[start_idx: end_idx] += F.log_softmax(batch_logits, dim=-1).exp_().cpu()
+                        model.to('cpu')
+                else:
+                    # Here models indicates a single model.
+                    models.to(self.device)
+                    batch_logits = models(batch_data + delta)
+                    self.ensemble_proba[start_idx: end_idx] += F.log_softmax(batch_logits, dim=-1).exp_().cpu()
+                    models.to('cpu')
+
+                targets_this_batch = self.targets[start_idx: end_idx]
+                # Keeping reduction method 'none' to get loss
+                # contibution of every data-case individually
+                log_likelihood = F.nll_loss(torch.log(self.ensemble_proba[start_idx: end_idx]/self.num_samples_collected), targets_this_batch, reduction='none')
+                log_likelihood.backward()
+                # Note: Rather than standard gradient descent, (normalized) steepest descent has been used here
+                delta.data = (delta + self.alpha*delta.grad.detach().sign()).clamp(-self.l_inf_norm, self.l_inf_norm)
+                delta.grad.zero_()
+
+            # Adding value of delta after all the attack iterations
+            batch_data = batch_data + delta.detach()
+            output_adversarial_examples.append(batch_data)
+            start_idx = end_idx
+
+        output_adversarial_examples = torch.cat(output_adversarial_examples)
+
+        return output_adversarial_examples