Agent Conversation: anthropic/claude-opus-4.6

Model: anthropic/claude-opus-4.6
Total steps: 5
Messages: 11
UserStep 0

Task: security-adversarial-attack-sparse-l0

Sparse Adversarial Attack (L0 Constraint)

Objective

Implement a stronger sparse attack in bench/custom_attack.py. Your method should maximize attack success rate (ASR) under a strict L0 perturbation budget:

  • Threat model: full model access for custom attack implementation.
  • Norm constraint: number of modified spatial pixels is bounded.
  • Budget: L0(x_adv, x) <= pixels, where pixels = 10.

Editable Interface

You must implement:

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

Inputs:

  • images: tensor of shape (N, C, H, W), values in [0, 1].
  • labels: tensor of shape (N,).
  • pixels: maximum number of modified spatial pixels per sample.
  • 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:

  1. Loads one pretrained model.
  2. Collects up to 1000 samples that are initially classified correctly.
  3. Runs your run_attack.
  4. Checks L0 validity (<= pixels modified spatial pixels).
  5. 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 budget) 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

  • onepixel: one-pixel differential evolution based sparse baseline.
  • sparsefool: gradient-based sparse perturbation baseline.
  • jsma: Jacobian saliency map based targeted sparse baseline.
  • pixle: pixel rearrangement based sparse baseline.

Your goal is to improve ASR while respecting the L0 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:     pixels: int,
    12:     device: torch.device,
    13:     n_classes: int,
    14: ) -> torch.Tensor:
    15:     """
    16:     Sparse L0 adversarial attack.
    17:     images: (N, C, H, W) in [0, 1] on device. labels: (N,) on device.
    18:     pixels: max number of modified spatial pixels (H, W) per sample.
    19:     n_classes: 10 for CIFAR-10, 100 for CIFAR-100.
    20:     Returns adv_images satisfying an L0 pixel budget validated by evaluator.
    21:     """
    22:     _ = (model, labels, pixels, device, n_classes)
    23:     return images.clone()
    24: 
    25: # =======================================================
AssistantStep 1edit
Tool Input
1{
2  "op": "replace",
3  "filename": "torchattacks/bench/custom_attack.py",
4  "start_line": 3,
5  "end_line": 26,
6  "content": "import torch.nn.functional as F\ndef run_attack(model, images, labels, pixels, device, n_classes):\n    model.eval(); N,C,H,W=images.shape; adv=images.clone()\n    for i in range(N):\n        x,y=images[i:i+1],labels[i:i+1]; done=False\n        with torch.no_grad(): lo=model(x)\n        if lo.argmax(1)!=y: continue\n        ts=[c.item() for c in lo[0].argsort(descending=True) if c.item()!=y.item()][:8]\n        for t in ts:\n            if done: break\n            xr=x.detach().requires_grad_(True);o=model(xr);(o[0,t]-o[0,y[0]]).backward();g=xr.grad.detach();sc=g.abs().sum(1).view(-1)\n            for r in range(4):\n                if done: break\n                sr=sc+(torch.randn(H*W,device=device)*sc.std()*r*0.3 if r else 0);_,ix=sr.topk(pixels)\n                mk=torch.zeros(1,1,H,W,device=device);mk.view(-1)[ix]=1;mk=mk.expand(1,C,H,W)\n                av=x.clone();av[mk>0]=(g.sign()[mk>0]>0).float()\n                with torch.no_grad():\n                    if model(av).argmax(1)!=y: adv[i:i+1]=av;done=True;continue\n                dl=(av-x).detach().requires_grad_(True);op=torch.optim.Adam([dl],lr=0.2)\n                for s in range(50): av2=torch.clamp(x+dl*mk,0,1);l2=model(av2);ls=l2[0,y[0]]-l2[0,t];op.zero_grad();ls.backward();op.step()\n                with torch.no_grad():\n                    ta=torch.clamp(x+dl*mk,0,1)\n                    if model(ta).argmax(1)!=y: adv[i:i+1]=ta;done=True\n    return adv\n"
7}
Tool ResultStep 1
OK: Replaced lines 3..26 with 24 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–26.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–26 | total: 27 lines]
     1| import torch
     2| import torch.nn as nn
     3  import torch.nn.functional as F
     4  def run_attack(model, images, labels, pixels, device, n_classes):
     5      model.eval(); N,C,H,W=images.shape; adv=images.clone()
     6      for i in range(N):
     7          x,y=images[i:i+1],labels[i:i+1]; done=False
     8          with torch.no_grad(): lo=model(x)
     9          if lo.argmax(1)!=y: continue
    10          ts=[c.item() for c in lo[0].argsort(descending=True) if c.item()!=y.item()][:8]
    11          for t in ts:
    12              if done: break
    13              xr=x.detach().requires_grad_(True);o=model(xr);(o[0,t]-o[0,y[0]]).backward();g=xr.grad.detach();sc=g.abs().sum(1).view(-1)
    14              for r in range(4):
    15                  if done: break
    16
AssistantStep 2test
Tool ResultStep 2
[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=0.9900, clean_acc=1.0000, robust_acc=0.0100

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9700, clean_acc=1.0000, robust_acc=0.0300

### MobileNetV2-C10 (scripts/eval_mobilenetv2_cifar10.sh)
Attack results (MobileNetV2-C10): ASR=0.9600, clean_acc=1.0000, robust_acc=0.0400

### ResNet20-C100 (scripts/eval_resnet20_cifar100.sh)
Attack results (ResNet20-C100): ASR=0.9100, clean_acc=1.0000, robust_acc=0.0900

### VGG11BN-C100 (scripts/eval_vgg11bn_cifar100.sh)
Attack results (VGG11BN-C100): ASR=0.9000, clean_acc=1.0000, robust_acc=0.1000

[Leaderboard] Results saved: {'asr_ResNet20_C10': 0.99, 'elapsed_ResNet20-C10': 50, 'asr_VGG11BN_C10': 0.97, 'elapsed_VGG11BN-C10': 50, 'asr_MobileNetV2_C10': 0.96, 'elapsed_MobileNetV2-C10': 136, 'asr_ResNet20_C100': 0.91, 'elapsed_ResNet20-C100': 136, 'asr_V
AssistantStep 3edit
Tool Input
1{
2  "op": "replace",
3  "filename": "torchattacks/bench/custom_attack.py",
4  "start_line": 3,
5  "end_line": 26,
6  "content": "import torch.nn.functional as F\ndef run_attack(model, images, labels, pixels, device, n_classes):\n    model.eval(); N,C,H,W=images.shape; adv=images.clone()\n    for i in range(N):\n        x,y=images[i:i+1],labels[i:i+1]; done=False\n        with torch.no_grad(): lo=model(x)\n        if lo.argmax(1)!=y: continue\n        ts=[c.item() for c in lo[0].argsort(descending=True) if c.item()!=y.item()][:8]\n        for t in ts:\n            if done: break\n            xr=x.detach().requires_grad_(True);o=model(xr);(o[0,t]-o[0,y[0]]).backward();g=xr.grad.detach();sc=g.abs().sum(1).view(-1)\n            for r in range(5):\n                if done: break\n                sr=sc+(torch.randn(H*W,device=device)*(sc.std()+1e-8)*r*0.3 if r else 0);_,ix=sr.topk(pixels)\n                mk=torch.zeros(1,1,H,W,device=device);mk.view(-1)[ix]=1;mk=mk.expand(1,C,H,W)\n                av=x.clone();av[mk>0]=(g.sign()[mk>0]>0).float()\n                with torch.no_grad():\n                    if model(av).argmax(1)!=y: adv[i:i+1]=av;done=True;continue\n                dl=(av-x).detach().requires_grad_(True);op=torch.optim.Adam([dl],lr=0.2)\n                for s in range(50): av2=torch.clamp(x+dl*mk,0,1);l2=model(av2);ls=l2[0,y[0]]-l2[0,t]-0.5*F.cross_entropy(l2,y);op.zero_grad();ls.backward();op.step()\n                with torch.no_grad():\n                    ta=torch.clamp(x+dl*mk,0,1)\n                    if model(ta).argmax(1)!=y: adv[i:i+1]=ta;done=True\n    return adv\n"
7}
Tool ResultStep 3
OK: Replaced lines 3..26 with 24 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–26.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–26 | total: 27 lines]
     1| import torch
     2| import torch.nn as nn
     3  import torch.nn.functional as F
     4  def run_attack(model, images, labels, pixels, device, n_classes):
     5      model.eval(); N,C,H,W=images.shape; adv=images.clone()
     6      for i in range(N):
     7          x,y=images[i:i+1],labels[i:i+1]; done=False
     8          with torch.no_grad(): lo=model(x)
     9          if lo.argmax(1)!=y: continue
    10          ts=[c.item() for c in lo[0].argsort(descending=True) if c.item()!=y.item()][:8]
    11          for t in ts:
    12              if done: break
    13              xr=x.detach().requires_grad_(True);o=model(xr);(o[0,t]-o[0,y[0]]).backward();g=xr.grad.detach();sc=g.abs().sum(1).view(-1)
    14              for r in range(5):
    15                  if done: break
    16
AssistantStep 4test
Tool ResultStep 4
[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=0.9900, clean_acc=1.0000, robust_acc=0.0100

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9900, clean_acc=1.0000, robust_acc=0.0100

### MobileNetV2-C10 (scripts/eval_mobilenetv2_cifar10.sh)
Attack results (MobileNetV2-C10): ASR=0.9400, clean_acc=1.0000, robust_acc=0.0600

### ResNet20-C100 (scripts/eval_resnet20_cifar100.sh)
Attack results (ResNet20-C100): ASR=0.9300, clean_acc=1.0000, robust_acc=0.0700

### VGG11BN-C100 (scripts/eval_vgg11bn_cifar100.sh)
Attack results (VGG11BN-C100): ASR=0.9200, clean_acc=1.0000, robust_acc=0.0800

[Leaderboard] Results saved: {'asr_ResNet20_C10': 0.99, 'elapsed_ResNet20-C10': 49, 'asr_VGG11BN_C10': 0.99, 'elapsed_VGG11BN-C10': 49, 'asr_MobileNetV2_C10': 0.94, 'elapsed_MobileNetV2-C10': 187, 'asr_ResNet20_C100': 0.93, 'elapsed_ResNet20-C100': 187, 'asr_VG
AssistantStep 5submit
Tool Input
1{
2  "n": 2
3}
Tool ResultStep 5
[submit] Submitting result from test #2 as final.

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Attack results (ResNet20-C10): ASR=0.9900, clean_acc=1.0000, robust_acc=0.0100

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9900, clean_acc=1.0000, robust_acc=0.0100

### MobileNetV2-C10 (scripts/eval_mobilenetv2_cifar10.sh)
Attack results (MobileNetV2-C10): ASR=0.9400, clean_acc=1.0000, robust_acc=0.0600

### ResNet20-C100 (scripts/eval_resnet20_cifar100.sh)
Attack results (ResNet20-C100): ASR=0.9300, clean_acc=1.0000, robust_acc=0.0700

### VGG11BN-C100 (scripts/eval_vgg11bn_cifar100.sh)
Attack results (VGG11BN-C100): ASR=0.9200, clean_acc=1.0000, robust_acc=0.0800

### MobileNetV2-C100 (scripts/eval_mobilenetv2_cifar100.sh)
Attack results (MobileNetV2-C100): ASR=0.9500, clean_acc=1.0000, robust_acc=0.0500

[Leaderboard] Results saved: {'asr_ResNet20_C10': 0.99, 'elapsed_ResNet20-C10': 49, 'asr_VGG11BN_C10': 0.99, 'elapsed_VGG11BN-C10': 49,