arXiv AI
Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Oct 1
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 AI
Jul 17
Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
arXiv:2607. 14921v1 Announce Type: cross Abstract: Machine learning models are increasingly adapted in various domains.
arXiv Machine Learning
Oct 7
ATLAS-AL: Adaptive Trust-Region for Latent Adversarial Searches via Active Learning
arXiv:2610.07323v1 Announce Type: new Abstract: Security evaluation of learning-based systems requires more than just testing the system against a fixed collection of attacks. It requires adaptive me...
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.
Hugging Face Trending Papers
Jun 13
Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment
Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses report near-zero attack success rate on static benchmarks, yet recent adaptive evaluations show that these results collapse once the attacker is allowed to optimize against the deployed defense.
arXiv AI
Jun 2
Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
arXiv:2603. 24511v2 Announce Type: replace-cross Abstract: We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on white-box jailbreaking and prompt injection evaluations.