Agent Conversation: gpt-5.4-pro

Model: gpt-5.4-pro

Total steps: 5

Messages: 11

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  "op": "replace",
3  "filename": "torchattacks/bench/custom_attack.py",
4  "content": "\n# =====================================================================\n# EDITABLE: implement run_attack below\n# =====================================================================\ndef run_attack(\n    model: nn.Module,\n    images: torch.Tensor,\n    labels: torch.Tensor,\n    eps: float,\n    device: torch.device,\n    n_classes: int,\n) -> torch.Tensor:\n    import torch.nn.functional as F\n\n    _ = device\n    model.eval()\n    x = images.detach()\n\n    def proj(x_adv: torch.Tensor, x_ref: torch.Tensor) -> torch.Tensor:\n        return torch.clamp(torch.max(torch.min(x_adv, x_ref + eps), x_ref - eps), 0.0, 1.0)\n\n    def untarget_score(logits: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n        y_logit = logits.gather(1, y[:, None]).squeeze(1)\n        vals, idx = logits.sort(dim=1, descending=True)\n        other = torch.where(idx[:, 0].eq(y), vals[:, 1], vals[:, 0])\n        den_idx = min(3, logits.size(1) - 1)\n        denom = (vals[:, 0] - vals[:, den_idx]).clamp_min(1e-12)\n        return (other - y_logit) / denom + 0.05 * (other - y_logit)\n\n    with torch.enable_grad():\n        x0 = x.clone().requires_grad_(True)\n        g0 = torch.autograd.grad(F.cross_entropy(model(x0), labels), x0)[0]\n    x_adv = proj(x + 0.5 * eps * g0.sign() + 0.5 * torch.empty_like(x).uniform_(-eps, eps), x).detach()\n    best_x = x_adv.clone()\n    best_score = torch.full((x.size(0),), -1e9, device=x.device)\n    mom = torch.zeros_like(x)\n    prev_g = torch.zeros_like(x)\n    alpha = torch.full((x.size(0), 1, 1, 1), eps / 3.0, device=x.device)\n    ref_score = best_score.clone()\n\n    for t in range(24):\n        x_adv.requires_grad_(True)\n        logits = model(x_adv)\n        ce = F.cross_entropy(logits, labels, reduction=\"none\")\n        score = untarget_score(logits, labels)\n        obj = ((1.0 - (t + 1) / 24.0) * ce + ((t + 1) / 24.0) * score).sum()\n        grad = torch.autograd.grad(obj, x_adv)[0]\n        grad = grad / (grad.abs().mean(dim=(1, 2, 3), keepdim=True) + 1e-12)\n        mom = 0.8 * mom + grad + 0.2 * (grad - prev_g)\n        prev_g = grad.detach()\n        with torch.no_grad():\n            pred = logits.argmax(dim=1)\n            cur_score = (score + 0.1 * ce).detach()\n            succ = pred.ne(labels)\n            cur_score = torch.where(succ, cur_score + 1e6, cur_score)\n            better = cur_score > best_score\n            best_score = torch.where(better, cur_score, best_score)\n            best_x[better] = x_adv.detach()[better]\n            x_adv = proj(x_adv.detach() + alpha * mom.sign(), x)\n            if (t + 1) % 6 == 0 and t < 23:\n                stalled = best_score <= (ref_score + 1e-4)\n                alpha[stalled] = alpha[stalled] * 0.5\n                x_adv[stalled] = best_x[stalled]\n                mom[stalled] = 0\n                prev_g[stalled] = 0\n                ref_score = best_score.clone()\n\n    with torch.no_grad():\n        logits = model(x_adv)\n        ce = F.cross_entropy(logits, labels, reduction=\"none\")\n        score = untarget_score(logits, labels)\n        cur_score = score + 0.1 * ce\n        succ = logits.argmax(dim=1).ne(labels)\n        cur_score = torch.where(succ, cur_score + 1e6, cur_score)\n        better = cur_score > best_score\n        best_x[better] = x_adv[better]\n        best_score = torch.where(better, cur_score, best_score)\n        logits_best = model(best_x)\n        robust = logits_best.argmax(dim=1).eq(labels)\n\n    if robust.any():\n        xs, x_ref, ys = best_x[robust].clone(), x[robust], labels[robust]\n        logits_s = logits_best[robust]\n        xs_score = untarget_score(logits_s, ys)\n        order = logits_s.argsort(dim=1, descending=True)\n        targets = order[order.ne(ys[:, None])].view(xs.size(0), -1)[:, : (3 if n_classes <= 10 else 4)]\n        for j in range(targets.size(1)):\n            tgt = targets[:, j]\n            x_t = proj(xs + torch.empty_like(xs).uniform_(-0.25 * eps, 0.25 * eps), x_ref).detach()\n            mom = torch.zeros_like(xs)\n            prev_g = torch.zeros_like(xs)\n            alpha_t = torch.full((xs.size(0), 1, 1, 1), eps / 4.0, device=x.device)\n            local_best = x_t.clone()\n            local_score = torch.full((xs.size(0),), -1e9, device=x.device)\n            ref_local = local_score.clone()\n            for s in range(10):\n                x_t.requires_grad_(True)\n                logits_t = model(x_t)\n                y_logit = logits_t.gather(1, ys[:, None]).squeeze(1)\n                t_logit = logits_t.gather(1, tgt[:, None]).squeeze(1)\n                obj = (t_logit - y_logit - 0.25 * F.cross_entropy(logits_t, tgt, reduction=\"none\")).sum()\n                grad = torch.autograd.grad(obj, x_t)[0]\n                grad = grad / (grad.abs().mean(dim=(1, 2, 3), keepdim=True) + 1e-12)\n                mom = 0.8 * mom + grad + 0.2 * (grad - prev_g)\n                prev_g = grad.detach()\n                with torch.no_grad():\n                    score_t = (t_logit - y_logit).detach()\n                    succ_t = logits_t.argmax(dim=1).ne(ys)\n                    score_t = torch.where(succ_t, score_t + 1e6, score_t)\n                    better_t = score_t > local_score\n                    local_score = torch.where(better_t, score_t, local_score)\n                    local_best[better_t] = x_t.detach()[better_t]\n                    x_t = proj(x_t.detach() + alpha_t * mom.sign(), x_ref)\n                    if (s + 1) % 5 == 0 and s < 9:\n                        stalled = local_score <= (ref_local + 1e-4)\n                        alpha_t[stalled] = alpha_t[stalled] * 0.5\n                        x_t[stalled] = local_best[stalled]\n                        mom[stalled] = 0\n                        prev_g[stalled] = 0\n                        ref_local = local_score.clone()\n            with torch.no_grad():\n                logits_l = model(local_best)\n                score_l = untarget_score(logits_l, ys)\n                succ_l = logits_l.argmax(dim=1).ne(ys)\n                score_l = torch.where(succ_l, score_l + 1e6, score_l)\n                better_l = score_l > xs_score\n                xs_score = torch.where(better_l, score_l, xs_score)\n                xs[better_l] = local_best[better_l]\n        best_x[robust] = xs\n\n    return best_x.detach()\n\n# =====================================================================\n# END EDITABLE REGION\n# =====================================================================",
5  "after_line": 0,
6  "start_line": 3,
7  "end_line": 26
8}

Tool ResultStep 1

OK: Replaced lines 3..26 with 135 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–137.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–137 | total: 137 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      _ = device
    18      model.eval()
    19      x = images.detach()
    20  
    21      def proj(x_adv: torch.Tensor, x_ref: torch.Tensor) -> torch.Tensor:
    22          return torch.clamp(torch.max(torch.min(x_adv, x_ref + eps), x_ref

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=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9520, clean_acc=1.0000, robust_acc=0.0480

### 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': 8, 'asr_VGG11BN_C10': 0.952, 'elapsed_VGG11BN-C10': 9, 'asr_MobileNetV2_C10': 1.0, 'elapsed_MobileNetV2-C10': 10, 'asr_ResNet20_C100': 1.0, 'elapsed_ResNet20-C100': 10, 'asr_VGG11BN

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "torchattacks/bench/custom_attack.py",
4  "content": "    if robust.any():\n        xs, x_ref, ys = best_x[robust].clone(), x[robust], labels[robust]\n        logits_s = logits_best[robust]\n        xs_score = untarget_score(logits_s, ys) + 0.1 * F.cross_entropy(logits_s, ys, reduction=\"none\")\n        top_m = 5 if n_classes <= 10 else 7\n        order = logits_s.argsort(dim=1, descending=True)\n        targets = order[order.ne(ys[:, None])].view(xs.size(0), -1)[:, :top_m]\n\n        with torch.no_grad():\n            base_t = logits_s.gather(1, targets)\n            weights = torch.softmax(base_t / 2.0, dim=1)\n\n        x_t = proj(xs + torch.empty_like(xs).uniform_(-0.35 * eps, 0.35 * eps), x_ref).detach()\n        mom = torch.zeros_like(xs)\n        prev_g = torch.zeros_like(xs)\n        alpha_t = torch.full((xs.size(0), 1, 1, 1), eps / 3.0, device=x.device)\n        local_best = xs.clone()\n        local_score = xs_score.clone()\n        ref_local = local_score.clone()\n        tau = 0.75\n\n        for s in range(12):\n            x_t.requires_grad_(True)\n            logits_t = model(x_t)\n            y_logit = logits_t.gather(1, ys[:, None]).squeeze(1)\n            tgt_logits = logits_t.gather(1, targets)\n            consensus = 0.5 * (weights * tgt_logits).sum(dim=1) + 0.5 * tau * torch.logsumexp(tgt_logits / tau, dim=1)\n            ce_y = F.cross_entropy(logits_t, ys, reduction=\"none\")\n            grad = torch.autograd.grad((consensus - y_logit + 0.2 * ce_y).sum(), x_t)[0]\n            grad = grad / (grad.abs().mean(dim=(1, 2, 3), keepdim=True) + 1e-12)\n            mom = 0.8 * mom + grad + 0.2 * (grad - prev_g)\n            prev_g = grad.detach()\n            with torch.no_grad():\n                score_t = untarget_score(logits_t, ys) + 0.1 * ce_y + 0.1 * (consensus - y_logit)\n                succ_t = logits_t.argmax(dim=1).ne(ys)\n                score_t = torch.where(succ_t, score_t + 1e6, score_t)\n                better_t = score_t > local_score\n                local_score = torch.where(better_t, score_t, local_score)\n                local_best[better_t] = x_t.detach()[better_t]\n                x_t = proj(x_t.detach() + alpha_t * (mom + 0.25 * grad).sign(), x_ref)\n                if (s + 1) % 4 == 0 and s < 11:\n                    snap = proj(x_ref + eps * (mom + grad).sign(), x_ref)\n                    logits_snap = model(snap)\n                    snap_cons = 0.5 * (weights * logits_snap.gather(1, targets)).sum(dim=1) + 0.5 * tau * torch.logsumexp(logits_snap.gather(1, targets) / tau, dim=1)\n                    snap_score = untarget_score(logits_snap, ys) + 0.1 * F.cross_entropy(logits_snap, ys, reduction=\"none\") + 0.1 * (snap_cons - logits_snap.gather(1, ys[:, None]).squeeze(1))\n                    snap_succ = logits_snap.argmax(dim=1).ne(ys)\n                    snap_score = torch.where(snap_succ, snap_score + 1e6, snap_score)\n                    better_snap = snap_score > local_score\n                    local_score = torch.where(better_snap, snap_score, local_score)\n                    local_best[better_snap] = snap[better_snap]\n                    stalled = local_score <= (ref_local + 1e-4)\n                    alpha_t[stalled] = alpha_t[stalled] * 0.5\n                    x_t[stalled] = snap[stalled]\n                    mom[stalled] = 0\n                    prev_g[stalled] = 0\n                    ref_local = local_score.clone()\n\n        with torch.no_grad():\n            xs_better = local_score > xs_score\n            xs_score = torch.where(xs_better, local_score, xs_score)\n            xs[xs_better] = local_best[xs_better]\n            logits_r = model(xs)\n            rem = logits_r.argmax(dim=1).eq(ys)\n\n        if rem.any():\n            xs_r, x_ref_r, ys_r = xs[rem].clone(), x_ref[rem], ys[rem]\n            logits_r = logits_r[rem]\n            score_r = untarget_score(logits_r, ys_r) + 0.1 * F.cross_entropy(logits_r, ys_r, reduction=\"none\")\n            order_r = logits_r.argsort(dim=1, descending=True)\n            targets_r = order_r[order_r.ne(ys_r[:, None])].view(xs_r.size(0), -1)[:, : (4 if n_classes <= 10 else 5)]\n            for j in range(targets_r.size(1)):\n                tgt = targets_r[:, j]\n                x_p = proj(xs_r + torch.empty_like(xs_r).uniform_(-0.2 * eps, 0.2 * eps), x_ref_r).detach()\n                mom = torch.zeros_like(xs_r)\n                prev_g = torch.zeros_like(xs_r)\n                alpha_p = torch.full((xs_r.size(0), 1, 1, 1), eps / 4.0, device=x.device)\n                local_best = xs_r.clone()\n                local_score = score_r.clone()\n                ref_local = local_score.clone()\n                for s in range(8):\n                    x_p.requires_grad_(True)\n                    logits_p = model(x_p)\n                    y_logit = logits_p.gather(1, ys_r[:, None]).squeeze(1)\n                    t_logit = logits_p.gather(1, tgt[:, None]).squeeze(1)\n                    ce_y = F.cross_entropy(logits_p, ys_r, reduction=\"none\")\n                    ce_t = F.cross_entropy(logits_p, tgt, reduction=\"none\")\n                    grad = torch.autograd.grad((t_logit - y_logit + 0.2 * ce_y - 0.4 * ce_t).sum(), x_p)[0]\n                    grad = grad / (grad.abs().mean(dim=(1, 2, 3), keepdim=True) + 1e-12)\n                    mom = 0.75 * mom + grad + 0.25 * (grad - prev_g)\n                    prev_g = grad.detach()\n                    with torch.no_grad():\n                        score_p = untarget_score(logits_p, ys_r) + 0.1 * ce_y + 0.1 * (t_logit - y_logit)\n                        succ_p = logits_p.argmax(dim=1).ne(ys_r)\n                        score_p = torch.where(succ_p, score_p + 1e6, score_p)\n                        better_p = score_p > local_score\n                        local_score = torch.where(better_p, score_p, local_score)\n                        local_best[better_p] = x_p.detach()[better_p]\n                        x_p = proj(x_p.detach() + alpha_p * (mom + 0.25 * grad).sign(), x_ref_r)\n                        if (s + 1) % 4 == 0 and s < 7:\n                            snap = proj(x_ref_r + eps * (mom + grad).sign(), x_ref_r)\n                            logits_snap = model(snap)\n                            y_snap = logits_snap.gather(1, ys_r[:, None]).squeeze(1)\n                            t_snap = logits_snap.gather(1, tgt[:, None]).squeeze(1)\n                            snap_score = untarget_score(logits_snap, ys_r) + 0.1 * F.cross_entropy(logits_snap, ys_r, reduction=\"none\") + 0.1 * (t_snap - y_snap)\n                            snap_succ = logits_snap.argmax(dim=1).ne(ys_r)\n                            snap_score = torch.where(snap_succ, snap_score + 1e6, snap_score)\n                            better_snap = snap_score > local_score\n                            local_score = torch.where(better_snap, snap_score, local_score)\n                            local_best[better_snap] = snap[better_snap]\n                            stalled = local_score <= (ref_local + 1e-4)\n                            alpha_p[stalled] = alpha_p[stalled] * 0.5\n                            x_p[stalled] = snap[stalled]\n                            mom[stalled] = 0\n                            prev_g[stalled] = 0\n                            ref_local = local_score.clone()\n                with torch.no_grad():\n                    better_r = local_score > score_r\n                    score_r = torch.where(better_r, local_score, score_r)\n                    xs_r[better_r] = local_best[better_r]\n            xs[rem] = xs_r\n\n        best_x[robust] = xs",
5  "after_line": 82,
6  "start_line": 83,
7  "end_line": 131
8}

Tool ResultStep 3

OK: Replaced lines 83..131 with 122 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–210.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–210 | total: 210 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      _ = device
    18      model.eval()
    19      x = images.detach()
    20  
    21      def proj(x_adv: torch.Tensor, x_ref: torch.Tensor) -> torch.Tensor:
    22          return torch.clamp(torch.max(torch.min(x_adv, x_ref + eps), x_r

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=1.0000, clean_acc=1.0000, robust_acc=0.0000

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9510, clean_acc=1.0000, robust_acc=0.0490

### 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=0.9520, clean_acc=1.0000, robust_acc=0.0480

[Leaderboard] Results saved: {'asr_ResNet20_C10': 1.0, 'elapsed_ResNet20-C10': 8, 'asr_VGG11BN_C10': 0.951, 'elapsed_VGG11BN-C10': 10, 'asr_MobileNetV2_C10': 1.0, 'elapsed_MobileNetV2-C10': 10, 'asr_ResNet20_C100': 1.0, 'elapsed_ResNet20-C100': 10, 'asr_VGG11BN

AssistantStep 5submit

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 5

[submit] Submitting result from test #1 as final.

### 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=0.9520, clean_acc=1.0000, robust_acc=0.0480

### 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

### 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': 8, 'asr_VGG11BN_C10': 0.952, 'elapsed_VGG11BN-C10': 9, 'a