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.
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
SEBA is a sample‑efficient framework for black‑box adversarial attacks on visual reinforcement learning agents. It combines a shadow Q model, a generative adversarial network for imperceptible perturbations, and a world model to simulate dynamics, reducing real‑world queries. Experiments on MuJoCo and Atari show SEBA significantly lowers cumulative rewards while preserving visual fidelity and requiring far fewer environment interactions than previous methods.
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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 Computer Vision
Oct 1
Exploiting Vulnerabilities: Universal Adversarial Attacks on Vision-Language-Action Models in Robotics
arXiv:2609.39178v1 Announce Type: cross Abstract: Recently, Vision-Language-Action (VLA) models have revolutionized robotic manipulation by seamlessly integrating visual perception, language understa...
arXiv AI
Aug 12
Hidden in Plain Sight: Diffusion-Based Unrestricted Robotic Attacks on Vision-Language-Action Models
arXiv:2608. 10393v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in controlling robots across diverse manipulation tasks.
arXiv Machine Learning
Oct 8
Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning
arXiv:2610.06083v2 Announce Type: replace Abstract: Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This pa...
arXiv AI
Jul 14
AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation
arXiv:2607. 11063v1 Announce Type: new Abstract: Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances.
arXiv Machine Learning
Oct 5
Demonstration-Guided Observation Attacks on Black-Box Safe Reinforcement Learning Controllers for Robotic Systems
arXiv:2602.16543v2 Announce Type: replace Abstract: Safe reinforcement learning (Safe RL) learns robotic controllers that optimize task rewards under safety constraints, yet observation perturbations...
arXiv AI
Jun 2
Boosting RL-Based Visual Reasoning with Selective Adversarial Entropy Intervention
arXiv:2512. 10414v2 Announce Type: replace Abstract: Recently, reinforcement learning (RL) has become a common choice in enhancing the reasoning capabilities of vision-language models (VLMs).
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 AI
Jun 10
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
arXiv:2403. 00420v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments.
arXiv AI
Jun 16
ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
arXiv:2606. 16605v1 Announce Type: new Abstract: World models are widely used in robotic and agentic engineering control systems due to their ability to learn latent dynamics for planning and decision-making.
arXiv AI
Oct 5
Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models
The paper presents a mechanistic defense for Vision‑Language‑Action (VLA) models against adversarial patches. By using a sparse autoencoder, the authors identify a feature whose activation correlates strongly with the presence of an adversarial patch and suppress this feature only when a linear probe detects an attack. This conditional intervention improves robustness on the LIBERO‑10 benchmark while avoiding the performance degradation that occurs with continuous suppression.