arXiv AI
Jun 6
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning
arXiv:2503. 01734v3 Announce Type: replace-cross Abstract: Attacks on machine learning models have been extensively studied through stateless optimization.
Efficient Adversarial Attacks on High-dimensional Offline Bandits
arXiv:2602. 01658v2 Announce Type: replace-cross Abstract: Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large language models, by efficiently identifying top-performing candidates without exhaustive comparisons.
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arXiv AI
Aug 6
Stabilizing Multi-Attack Adversarial Training via Bandit Optimization
arXiv:2511. 12265v2 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) remain vulnerable to diverse adversarial perturbations, motivating multi-attack adversarial training (AT) for improved robustness.
arXiv AI
Jun 11
Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization
arXiv:2606. 12251v1 Announce Type: cross Abstract: Gradient-based adversarial attacks remain a dominant threat to deep neural networks (DNNs), as they exploit gradient information to efficiently optimize adversarial perturbations.
arXiv Computer Vision
3d ago
Opportunistic Target Selection: Early Directional Commitment for Query-Efficient Black-Box Adversarial Attacks
arXiv:2605.25663v2 Announce Type: replace-cross Abstract: Black-box adversarial attacks that minimize only the ground-truth confidence suffer from class drift: perturbations wander through the featur...
arXiv Machine Learning
Sep 22
Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision
The paper introduces RIBA, a reinforcement‑learning inspired black‑box adversarial attack that generates perturbations for neural networks with fewer queries than existing methods. RIBA achieves a 25.4% reduction in median queries on ResNet‑18/Cifar10 and a 22.5% reduction on Vit‑B/16/ImageNet, while matching white‑box attack performance on an adversarially trained model.
arXiv AI
Sep 18
Information-Geometric Inverse Distillation for Enhancing Adversarial Transferability
The paper introduces Inverse Knowledge Distillation (IKD), an attack‑agnostic technique that enhances adversarial transferability by maximizing the discrepancy between benign and adversarial prediction distributions on a surrogate model. IKD employs a CE/KL‑equivalent soft‑label objective to push adversarial predictions away from a fixed benign anchor, leveraging Fisher‑sensitive surrogate directions. The authors provide theoretical analysis showing CE and KL induce identical gradients, derive a lower bound on Fisher‑subspace overlap, and demonstrate through extensive ImageNet experiments that IKD consistently improves black‑box attack performance across CNN, ViT, and defended models.
arXiv Machine Learning
Sep 10
Approaching the Harm of Gradient Attacks While Only Flipping Labels
The paper investigates the impact of label‑flipping attacks on distributed machine learning, where an adversary can only flip a limited number of training labels. It formalizes the attack as a per‑round constrained optimization problem, derives a greedy label‑selection rule for logistic regression, and shows that this rule is provably optimal under mean aggregation. Experiments demonstrate that optimized label flipping can significantly degrade model accuracy, outperforming random flips, and that the attack transfers to other robust aggregators such as coordinate‑wise median and trimmed mean.
arXiv Machine Learning
Jun 26
Learning from Equivalence Queries, Revisited
arXiv:2604. 04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates.
arXiv Machine Learning
Sep 10
WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents
arXiv:2605.23220v2 Announce Type: replace Abstract: Despite the growing use of world models as decision-making agents, their adversarial robustness remains underexplored due to the lack of dedicated...
arXiv Machine Learning
Aug 17
Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack
arXiv:2605. 25194v2 Announce Type: replace Abstract: Adversarial images pose a severe security threat to multimodal large language models through prompt injection.
arXiv Machine Learning
Jul 7
Binary Iterative Method for Non-targeted Adversarial Attack
arXiv:2607. 04145v1 Announce Type: new Abstract: Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the 'piecewise linearity' of deep learning based models.
arXiv Machine Learning
Aug 21
Merge Now, Regret Later: The Hidden Cost of Model Merging Is Adversarial Transferability
arXiv:2509. 23689v2 Announce Type: replace Abstract: Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into one model that retains performance across different tasks.