arXiv AI June 17, 2026 By Edward Stevinson, Lucas Prieto, Melih Barsbey, Tolga Birdal

Adversarial Attacks Leverage Interference Between Features in Superposition

arXiv:2510. 11709v2 Announce Type: replace-cross Abstract: Why do adversarial examples exist, and why do they transfer between models?

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

arXiv Machine Learning

Jun 26

The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examples

arXiv:2606. 26207v1 Announce Type: cross Abstract: Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional geometry.

By Nasrin Malekzadeh Goradel, Niccolo Pancino, Yaser Gholizade Atani, Benedetta Tondi, Giovanni Bellettini, Mauro Barni

safety

arXiv AI

1d ago

Fast Test-Time Refinement for Robust Learned Image Compression

arXiv:2608. 15113v1 Announce Type: cross Abstract: Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings.

By Jiaming Liang, Chi-Man Pun, Weisi Lin

safety

arXiv Machine Learning

Jul 15

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.

By Rasmus Torp, Shailen K. Smith, Adam Breuer

benchmarks safety

arXiv Machine Learning

Jun 2

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.

By Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati, Pau Vilimelis Aceituno

benchmarks safety

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.

By Xinhai Zou, Chang Zhao, Alireza Aghabagherloo, Dave Singel\'ee, Robin Degraeve, Bart Preneel

reinforcement-learning safety

arXiv Machine Learning

Aug 6

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP

arXiv:2505. 19840v3 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) have achieved widespread success yet remain prone to adversarial attacks.

By Binyan Xu, Xilin Dai, Di Tang, Kehuan Zhang

llms multimodal safety

Related stories

The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examples

Fast Test-Time Refinement for Robust Learned Image Compression

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization

One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP