Agent Conversation: google/gemini-3.1-pro-preview

Model: google/gemini-3.1-pro-preview

Total steps: 7

Messages: 15

UserStep 0

Task: security-adversarial-attack-white-box-linf

White-Box Evasion Attack under Linf Constraint

Objective

Implement a stronger white-box L_inf attack in bench/custom_attack.py. Your method should maximize attack success rate (ASR) under a strict perturbation budget:

Threat model: white-box (full model access, including gradients).
Norm constraint: ||x_adv - x||_inf <= eps.
Budget: eps = 4/255.

Editable Interface

You must implement:

run_attack(model, images, labels, eps, device, n_classes) -> adv_images

Inputs:

images: tensor of shape (N, C, H, W), values in [0, 1].
labels: tensor of shape (N,).
n_classes: 10 for CIFAR-10, 100 for CIFAR-100.

Output:

adv_images: same shape as images, also in [0, 1].

Evaluation Protocol

Each evaluation script:

Loads one pretrained model.
Collects up to 1000 samples that are initially classified correctly.
Runs your run_attack.
Checks L_inf validity.
Reports:
- clean_acc
- robust_acc
- asr = 1 - robust_acc

Important:

ASR denominator is the number of initially correct samples.
Invalid adversarial outputs (shape mismatch or violated norm) are treated as failure.

Scenarios

Six scenarios are evaluated in parallel:

ResNet20 on CIFAR-10
VGG11-BN on CIFAR-10
MobileNetV2 on CIFAR-10
ResNet20 on CIFAR-100
VGG11-BN on CIFAR-100
MobileNetV2 on CIFAR-100

Baselines

fgsm: one-step FGSM baseline (simplest first-order attack).
pgd: iterative PGD baseline (strong first-order baseline).
mifgsm: momentum iterative FGSM.
autoattack: torchattacks.AutoAttack(version="standard") as a strong upper baseline.

Your goal is to improve ASR while respecting the Linf budget.

torchattacks/bench/custom_attack.py [EDITABLE — lines 3–26 only]

     1: import torch
     2: import torch.nn as nn
     3: 
     4: # =====================================================================
     5: # EDITABLE: implement run_attack below
     6: # =====================================================================
     7: def run_attack(
     8:     model: nn.Module,
     9:     images: torch.Tensor,
    10:     labels: torch.Tensor,
    11:     eps: float,
    12:     device: torch.device,
    13:     n_classes: int,
    14: ) -> torch.Tensor:
    15:     """
    16:     White-box L_inf adversarial attack.
    17:     images: (N, C, H, W) in [0, 1] on device. labels: (N,) on device.
    18:     n_classes: 10 for CIFAR-10, 100 for CIFAR-100.
    19:     Returns adv_images satisfying (adv_images - images).abs().max() <= eps.
    20:     """
    21:     _ = (model, labels, eps, device, n_classes)
    22:     return images.clone()
    23: 
    24: # =====================================================================
    25: # END EDITABLE REGION
    26: # =====================================================================

fgsm baseline — editable region [READ-ONLY — reference implementation]

undefined

AssistantStep 1edit

Tool Input

1{
2  "start_line": 15,
3  "op": "replace",
4  "content": "    import torch.nn.functional as F\n\n    model.eval()\n    x = images.detach()\n    \n    best_loss = torch.full((x.shape[0],), -float('inf'), device=device)\n    best_x_adv = x.clone()\n\n    restarts = 2\n    steps = 40\n    alpha = eps / 5.0\n    \n    for _ in range(restarts):\n        x_adv = x + torch.empty_like(x).uniform_(-eps, eps)\n        x_adv = torch.clamp(x_adv, 0.0, 1.0).detach()\n        momentum = torch.zeros_like(x)\n        \n        for _ in range(steps):\n            x_adv.requires_grad_(True)\n            with torch.enable_grad():\n                logits = model(x_adv)\n                \n                labels_onehot = F.one_hot(labels, num_classes=n_classes).bool()\n                z_y = logits[labels_onehot]\n                z_other = logits.masked_fill(labels_onehot, -float('inf')).max(dim=1)[0]\n                \n                loss_cw = (z_other - z_y).sum()\n                \n            grad = torch.autograd.grad(loss_cw, x_adv)[0]\n            \n            momentum = 1.0 * momentum + grad / (grad.abs().mean(dim=(1,2,3), keepdim=True) + 1e-12)\n            \n            with torch.no_grad():\n                x_adv = x_adv + alpha * momentum.sign()\n                delta = torch.clamp(x_adv - x, min=-eps, max=eps)\n                x_adv = torch.clamp(x + delta, 0.0, 1.0).detach()\n                \n        with torch.no_grad():\n            logits_final = model(x_adv)\n            labels_onehot = F.one_hot(labels, num_classes=n_classes).bool()\n            z_y_final = logits_final[labels_onehot]\n            z_other_final = logits_final.masked_fill(labels_onehot, -float('inf')).max(dim=1)[0]\n            current_loss = z_other_final - z_y_final\n            \n            improve = current_loss > best_loss\n            best_loss = torch.where(improve, current_loss, best_loss)\n            best_x_adv = torch.where(improve.view(-1, 1, 1, 1), x_adv, best_x_adv)\n\n    return best_x_adv",
5  "after_line": 14,
6  "end_line": 22,
7  "filename": "bench/custom_attack.py"
8}

Tool ResultStep 1

ERROR: Package 'bench' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "op": "replace",
3  "end_line": 22,
4  "content": "    import torch.nn.functional as F\n\n    model.eval()\n    x = images.detach()\n    \n    best_loss = torch.full((x.shape[0],), -float('inf'), device=device)\n    best_x_adv = x.clone()\n\n    restarts = 2\n    steps = 40\n    alpha = eps / 5.0\n    \n    for _ in range(restarts):\n        x_adv = x + torch.empty_like(x).uniform_(-eps, eps)\n        x_adv = torch.clamp(x_adv, 0.0, 1.0).detach()\n        momentum = torch.zeros_like(x)\n        \n        for _ in range(steps):\n            x_adv.requires_grad_(True)\n            with torch.enable_grad():\n                logits = model(x_adv)\n                \n                labels_onehot = F.one_hot(labels, num_classes=n_classes).bool()\n                z_y = logits[labels_onehot]\n                z_other = logits.masked_fill(labels_onehot, -float('inf')).max(dim=1)[0]\n                \n                loss_cw = (z_other - z_y).sum()\n                \n            grad = torch.autograd.grad(loss_cw, x_adv)[0]\n            \n            momentum = 1.0 * momentum + grad / (grad.abs().mean(dim=(1,2,3), keepdim=True) + 1e-12)\n            \n            with torch.no_grad():\n                x_adv = x_adv + alpha * momentum.sign()\n                delta = torch.clamp(x_adv - x, min=-eps, max=eps)\n                x_adv = torch.clamp(x + delta, 0.0, 1.0).detach()\n                \n        with torch.no_grad():\n            logits_final = model(x_adv)\n            labels_onehot = F.one_hot(labels, num_classes=n_classes).bool()\n            z_y_final = logits_final[labels_onehot]\n            z_other_final = logits_final.masked_fill(labels_onehot, -float('inf')).max(dim=1)[0]\n            current_loss = z_other_final - z_y_final\n            \n            improve = current_loss > best_loss\n            best_loss = torch.where(improve, current_loss, best_loss)\n            best_x_adv = torch.where(improve.view(-1, 1, 1, 1), x_adv, best_x_adv)\n\n    return best_x_adv",
5  "filename": "torchattacks/bench/custom_attack.py",
6  "after_line": 14,
7  "start_line": 15
8}

Tool ResultStep 2

OK: Replaced lines 15..22 with 49 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–67.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–67 | total: 67 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      eps: float,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      import torch.nn.functional as F
    16  
    17      model.eval()
    18      x = images.detach()
    19      
    20      best_loss = torch.full((x.shape[0],), -float('inf'), device=device)
    21      best_x_adv = x.clone()
    22  
    23      restarts = 2
    24      steps = 40
    25

AssistantStep 3test

Tool ResultStep 3

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Attack results (ResNet20-C10): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9480, clean_acc=1.0000, robust_acc=0.0520

### MobileNetV2-C10 (scripts/eval_mobilenetv2_cifar10.sh)
Attack results (MobileNetV2-C10): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### ResNet20-C100 (scripts/eval_resnet20_cifar100.sh)
Attack results (ResNet20-C100): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C100 (scripts/eval_vgg11bn_cifar100.sh)
Attack results (VGG11BN-C100): ASR=0.9400, clean_acc=1.0000, robust_acc=0.0600

[Leaderboard] Results saved: {'asr_ResNet20_C10': 1.0, 'elapsed_ResNet20-C10': 10, 'asr_VGG11BN_C10': 0.948, 'elapsed_VGG11BN-C10': 10, 'asr_MobileNetV2_C10': 1.0, 'elapsed_MobileNetV2-C10': 15, 'asr_ResNet20_C100': 1.0, 'elapsed_ResNet20-C100': 15, 'asr_VGG11

AssistantStep 4edit

Tool Input

1{
2  "start_line": 15,
3  "end_line": 63,
4  "after_line": 14,
5  "content": "    import torch.nn.functional as F\n\n    model.eval()\n    x = images.detach()\n    \n    best_margin = torch.full((x.shape[0],), -float('inf'), device=device)\n    best_x_adv = x.clone()\n\n    restarts = 8\n    steps = 60\n    alpha = eps / 5.0\n    \n    with torch.no_grad():\n        labels_onehot = F.one_hot(labels, num_classes=n_classes).bool()\n        clean_logits = model(x)\n        clean_logits_other = clean_logits.masked_fill(labels_onehot, -float('inf'))\n        _, top_classes = clean_logits_other.topk(6, dim=1)\n    \n    for restart in range(restarts):\n        x_adv = x + torch.empty_like(x).uniform_(-eps, eps)\n        x_adv = torch.clamp(x_adv, 0.0, 1.0).detach()\n        momentum = torch.zeros_like(x)\n        \n        for _ in range(steps):\n            x_adv.requires_grad_(True)\n            with torch.enable_grad():\n                logits = model(x_adv)\n                \n                z_y = logits[labels_onehot]\n                z_other_max = logits.masked_fill(labels_onehot, -float('inf')).max(dim=1)[0]\n                \n                if restart == 0:\n                    loss = (z_other_max - z_y).sum() # CW\n                elif restart == 1:\n                    loss = F.cross_entropy(logits, labels, reduction='sum') # CE\n                else:\n                    target_class = top_classes[:, restart - 2]\n                    target_onehot = F.one_hot(target_class, num_classes=n_classes).bool()\n                    z_t = logits[target_onehot]\n                    loss = (z_t - z_y).sum()\n                    \n            grad = torch.autograd.grad(loss, x_adv)[0]\n            \n            momentum = 1.0 * momentum + grad / (grad.abs().mean(dim=(1,2,3), keepdim=True) + 1e-12)\n            \n            with torch.no_grad():\n                x_adv = x_adv + alpha * momentum.sign()\n                delta = torch.clamp(x_adv - x, min=-eps, max=eps)\n                x_adv = torch.clamp(x + delta, 0.0, 1.0).detach()\n                \n        with torch.no_grad():\n            logits_final = model(x_adv)\n            z_y_final = logits_final[labels_onehot]\n            z_other_max_final = logits_final.masked_fill(labels_onehot, -float('inf')).max(dim=1)[0]\n            margin = z_other_max_final - z_y_final\n            \n            improve = margin > best_margin\n            best_margin = torch.where(improve, margin, best_margin)\n            best_x_adv = torch.where(improve.view(-1, 1, 1, 1), x_adv, best_x_adv)\n\n    return best_x_adv",
6  "op": "replace",
7  "filename": "torchattacks/bench/custom_attack.py"
8}

Tool ResultStep 4

OK: Replaced lines 15..63 with 61 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–79.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–79 | total: 79 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      eps: float,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      import torch.nn.functional as F
    16  
    17      model.eval()
    18      x = images.detach()
    19      
    20      best_margin = torch.full((x.shape[0],), -float('inf'), device=device)
    21      best_x_adv = x.clone()
    22  
    23      restarts = 8
    24      steps = 60
    25

AssistantStep 5test

Tool ResultStep 5

[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Attack results (ResNet20-C10): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9530, clean_acc=1.0000, robust_acc=0.0470

### MobileNetV2-C10 (scripts/eval_mobilenetv2_cifar10.sh)
Attack results (MobileNetV2-C10): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### ResNet20-C100 (scripts/eval_resnet20_cifar100.sh)
Attack results (ResNet20-C100): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C100 (scripts/eval_vgg11bn_cifar100.sh)
Attack results (VGG11BN-C100): ASR=0.9540, clean_acc=1.0000, robust_acc=0.0460

[Leaderboard] Results saved: {'asr_ResNet20_C10': 1.0, 'elapsed_ResNet20-C10': 33, 'asr_VGG11BN_C10': 0.953, 'elapsed_VGG11BN-C10': 33, 'asr_MobileNetV2_C10': 1.0, 'elapsed_MobileNetV2-C10': 63, 'asr_ResNet20_C100': 1.0, 'elapsed_ResNet20-C100': 63, 'asr_VGG11B

AssistantStep 6edit

Tool Input

1{
2  "content": "    import torch.nn.functional as F\n    import math\n\n    model.eval()\n    x = images.detach()\n    \n    best_margin = torch.full((x.shape[0],), -float('inf'), device=device)\n    best_x_adv = x.clone()\n\n    restarts = 12\n    steps = 60\n    \n    with torch.no_grad():\n        labels_onehot = F.one_hot(labels, num_classes=n_classes).bool()\n        clean_logits = model(x)\n        clean_logits_other = clean_logits.masked_fill(labels_onehot, -float('inf'))\n        _, top_classes = clean_logits_other.topk(6, dim=1)\n    \n    for restart in range(restarts):\n        x_adv = x + torch.empty_like(x).uniform_(-eps, eps)\n        x_adv = torch.clamp(x_adv, 0.0, 1.0).detach()\n        momentum = torch.zeros_like(x)\n        \n        for step in range(steps):\n            x_adv.requires_grad_(True)\n            with torch.enable_grad():\n                logits = model(x_adv)\n                \n                z_y = logits[labels_onehot]\n                z_other = logits.masked_fill(labels_onehot, -float('inf'))\n                z_other_max = z_other.max(dim=1)[0]\n                \n                if restart == 0 or restart == 1:\n                    z_sorted = logits.sort(dim=1, descending=True)[0]\n                    loss = ((z_other_max - z_y) / (z_sorted[:, 0] - z_sorted[:, 2] + 1e-5)).sum()\n                elif restart == 2 or restart == 3:\n                    loss = F.cross_entropy(logits, labels, reduction='sum')\n                elif restart == 4 or restart == 5:\n                    loss = (z_other_max - z_y).sum()\n                else:\n                    target_class = top_classes[:, restart - 6]\n                    target_onehot = F.one_hot(target_class, num_classes=n_classes).bool()\n                    z_t = logits[target_onehot]\n                    loss = (z_t - z_y).sum()\n                    \n            grad = torch.autograd.grad(loss, x_adv)[0]\n            \n            momentum = 1.0 * momentum + grad / (grad.abs().mean(dim=(1,2,3), keepdim=True) + 1e-12)\n            \n            with torch.no_grad():\n                if restart % 2 == 1 and restart < 6:\n                    current_alpha = eps / 5.0\n                else:\n                    current_alpha = eps / 2.0 * (0.1 + 0.9 * 0.5 * (1 + math.cos(step / steps * math.pi)))\n                \n                x_adv = x_adv + current_alpha * momentum.sign()\n                delta = torch.clamp(x_adv - x, min=-eps, max=eps)\n                x_adv = torch.clamp(x + delta, 0.0, 1.0).detach()\n                \n        with torch.no_grad():\n            logits_final = model(x_adv)\n            z_y_final = logits_final[labels_onehot]\n            z_other_max_final = logits_final.masked_fill(labels_onehot, -float('inf')).max(dim=1)[0]\n            margin = z_other_max_final - z_y_final\n            \n            improve = margin > best_margin\n            best_margin = torch.where(improve, margin, best_margin)\n            best_x_adv = torch.where(improve.view(-1, 1, 1, 1), x_adv, best_x_adv)\n\n    return best_x_adv",
3  "after_line": 14,
4  "op": "replace",
5  "end_line": 75,
6  "filename": "torchattacks/bench/custom_attack.py",
7  "start_line": 15
8}

Tool ResultStep 6

OK: Replaced lines 15..75 with 70 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–88.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–88 | total: 88 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      eps: float,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      import torch.nn.functional as F
    16      import math
    17  
    18      model.eval()
    19      x = images.detach()
    20      
    21      best_margin = torch.full((x.shape[0],), -float('inf'), device=device)
    22      best_x_adv = x.clone()
    23  
    24      restarts = 12
    25

AssistantStep 7test

Tool ResultStep 7

[Test #3] (0 tests remaining)

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Attack results (ResNet20-C10): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9560, clean_acc=1.0000, robust_acc=0.0440

### MobileNetV2-C10 (scripts/eval_mobilenetv2_cifar10.sh)
Attack results (MobileNetV2-C10): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### ResNet20-C100 (scripts/eval_resnet20_cifar100.sh)
Attack results (ResNet20-C100): ASR=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C100 (scripts/eval_vgg11bn_cifar100.sh)
Attack results (VGG11BN-C100): ASR=0.9550, clean_acc=1.0000, robust_acc=0.0450

### MobileNetV2-C100 (scripts/eval_mobilenetv2_cifar100.sh)
Attack results (MobileNetV2-C100): ASR=0.9990, clean_acc=1.0000, robust_acc=0.0010

[Leaderboard] Results saved: {'asr_ResNet20_C10': 1.0, 'elapsed_ResNet20-C10': 43, 'asr_VGG11BN_C10': 0.956, 'elapsed_VGG11BN-C10': 43, 'asr_MobileNetV2_C10