Adversarial Transferability

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Period ending 2026-09-14

4 new papers

A weekly snapshot of new work published in Adversarial Transferability.

Period ending 2026-09-07

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A weekly snapshot of new work published in Adversarial Transferability.

119 papers

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Harnessing non-adversarial robustness in large language models
May 28, 2026Qinghua Zhou, Ellina Aleshina, Andrey Lovyagin +6Perturbations Adversarial Transferability
VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking
May 27, 2026Jiyuan Fu, Kaixun Jiang, Jingkai Jia +7Vision-Language-Action Models Adversarial Transferability
Enhancing Ultra-low-field MRI with Segmentation-guided Adversarial Learning
May 27, 2026James Grover, Andrew Phair, Michael Ferraro +1Magnetic Resonance Imaging U-Net
Bridging the Generalization Gap in Adverse Weather Segmentation: A Training Recipe Perspective
May 27, 2026Cong Xu, Pu Luo, Yumei Li +1Dense Semantic Segmentation Adverse Weather
Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models
May 25, 2026Xiao Liu, Jiaxiang Liu, Boci Peng +6Vision-Language Model Adaptation Adversarial Transferability
CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents
May 25, 2026Bowen Wang, Dunjie Lu, Junli Wang +11Reinforcement Learning With Verifiable Reward Computer-Use Agents
Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces
May 25, 2026Konstantinos Emmanouilidis, Tianjiao Ding, Nghia Nguyen +2Adversarial Transferability Gaussian Mixture Models
SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack
May 24, 2026Han Liu, Zhi Xu, Xiaotong Zhang +5Adversarial Examples Adversarial Transferability
Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems
May 23, 2026Imesh Ekanayake, Elham Naghizade, Jeffrey ChanFairness-Aware Learning Fairness Interventions
Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment
May 22, 2026Haoyuan Wang, Xiaohao Liu, Jiajie Su +2Multimodal Knowledge Editing Adversarial Transferability
Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs
May 20, 2026Leitao Yuan, Qinghua Mao, Daizong Liu +5Adversarial Transferability Large Language Model Adaptation
Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?
May 20, 2026Muquan Li, Yingyi Ma, Yihong Huang +5Adversarial Transferability Chain-Of-Thought Distillation
Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
May 18, 2026Sunwoo Lee, Mingu Kang, Yonghyeon Jo +1Multi-Agent Reinforcement Learning Adversarial Transferability
AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps
May 16, 2026Chia-Ying Hsieh, Hsin-Yuan Fang, Chun-Shu WeiSaliency Adversarial Transferability
Cross-Domain Molecular Relational Learning: Leveraging Chemical Structure-Activity Analysis
May 16, 2026Peiliang Zhang, Jingling Yuan, Shiqing Wu +4Structure-Based Drug Design Relational Deep Learning
Learning Disentangled Representations for Generalized Multi-view Clustering
May 15, 2026Xin Zou, Ruimeng Liu, Chang Tang +4Multi-View Adversarial Transferability
AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models
May 15, 2026Zhiwei Li, Jiacheng Xue, Weining Wang +4Adversarial Transferability Frozen Contrastive Language-Image Pre-Training Visual Encoder
Enabling Adversarial Robustness in AI Models through Kubeflow MLOps
May 14, 2026Stavros Bouras, Ioannis Korontanis, Antonios Makris +1Kubernetes Adversarial Transferability
Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation
May 13, 2026Lilin Zhang, Yimo Guo, Yue Li +2Adversarial Transferability Adversarial Training
Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
May 11, 2026Li Shen, Xiaolei Hao, Qinglun Li +3Federated Learning Vision Transformer
Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
May 9, 2026Hira Nasir, Eiman Javed, Balawal Shabir +2Intrusion Detection Adversarial Transferability
Improving Generative Adversarial Networks with Self-Distillation
May 9, 2026Antoni Nowinowski, Krzysztof KrawiecDual-Domain Equivariant Generative Adversarial Network Generative Adversarial Network
Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms
May 8, 2026Linh Le, David Williams-King, Mohamed Amine Merzouk +2Adversarial Transferability Harms
TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers
May 8, 2026Daniel Wesego, Pedram RooshenasAdversarial Transferability Rectification
Dual-Agent Co-Training for Health Coaching via Implicit Adversarial Preference Optimization
May 7, 2026Da Long, Lingyi Fu, Diya Michelle Rao +3Iterative Co-Training Conversational Artificial Intelligence
Streaming Adversarial Robustness in Fuzzy ARTMAP: Mechanism-Aligned Evaluation, Progressive Training, and Interpretable Diagnostics
May 7, 2026Shane Cairns, Leonardo Enzo Brito da Silva, Sasha Petrenko +2Adversarial Transferability Gumbel-Softmax Relaxation
Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks
May 7, 2026Guanmeng Xian, Ning Yang, Philip S. YuAdversarial Transferability Adversarial Training
Information Theoretic Adversarial Training of Large Language Models
May 6, 2026Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3Language Model Evasion Attacks Adversarial Transferability
Laundering AI Authority with Adversarial Examples
May 5, 2026Jie Zhang, Pura Peetathawatchai, Florian Tramèr +1Adversarial Examples Adversarial Transferability
Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment
May 4, 2026Guanmeng Xian, Ning Yang, Philip S. YuAdversarial Transferability Adversarial Training
ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs
May 3, 2026Marah Halawa, Olaf HellwichFairness-Aware Learning Contrastive Loss
Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA
Apr 30, 2026Feeza Khan Khanzada, Jaerock KwonLane Geometry Adversarial Transferability
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis
Apr 30, 2026David Fernandez, Pedram MohajerAnsari, Amir Salarpour +1Autonomous Driving Large Vision Language Models
Instruction Complexity Induces Positional Collapse in Adversarial LLM Evaluation
Apr 29, 2026Jon-Paul CacioliLanguage Model Evasion Attacks Collapse
Robust Alignment: Harmonizing Clean Accuracy and Adversarial Robustness in Adversarial Training
Apr 29, 2026Yanyun Wang, Qingqing Ye, Li Liu +2Adversarial Transferability Adversarial Training
The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches
Apr 29, 2026Vishesh Kumar, Akshay AgarwalAdversarial Examples Adversarial Transferability
Improving Diversity in Black-box Few-shot Knowledge Distillation
Apr 28, 2026Tri-Nhan Vo, Dang Nguyen, Kien Do +1Knowledge Distillation Few-Shot Learning
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
Apr 27, 2026Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2Adversarial Transferability Overfitting
Mitigating Error Amplification in Fast Adversarial Training
Apr 27, 2026Mengnan Zhao, Lihe Zhang, Bo Wang +3Adversarial Transferability Attention-Guided Transunet
Discriminator-Guided Adaptive Diffusion for Source-Free Test-Time Adaptation under Image Corruptions
Apr 26, 2026Francesco Olivato, Cigdem Beyan, Vittorio MurinoDomain Adaptation Adversarial Transferability
Useful nonrobust features are ubiquitous in biomedical images
Apr 24, 2026Coenraad Mouton, Randle Rabe, Niklas C. Koser +4Medical Image Classification Medical Imaging
AttDiff-GAN: A Hybrid Diffusion-GAN Framework for Facial Attribute Editing
Apr 23, 2026Wenmin Huang, Weiqi Luo, Xiaochun Cao +1Diffusion Editing Discrete Diffusion Model
Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
Apr 22, 2026Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis +2Language Model Evasion Attacks Hijacking
FastAT Benchmark: A Comprehensive Framework for Fair Evaluation of Fast Adversarial Training Methods
Apr 22, 2026Chao Pan, Xin YaoAdversarial Transferability Adversarial Training
RoIt-XMASA: Multi-Domain Multilingual Sentiment Analysis Dataset for Romanian and Italian
Apr 18, 2026Andrei-Marius Avram, Aureliu Valentin Antonie, Cosmin-Mircea Croitoru +2Sentiment Analysis Adversarial Transferability
Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models
Apr 16, 2026Emily Curl, Kofi Ampomah, Md Erfan +1Adversarial Transferability Medical Imaging Datasets
Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification
Apr 16, 2026Weiwei Zhuang, Wangze Xie, Qi Zhang +9Fog Adversarial Transferability
Weak Adversarial Neural Pushforward Method for the Wigner Transport Equation
Apr 9, 2026Andrew Qing He, Wei Cai, Sihong ShaoPhase Space Quantum Neural Networks
LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings
Feb 21, 2026Abdullah Caglar Oksuz, Anisa Halimi, Erman AydayMembership Inference Attacks Membership Inference
MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis
Feb 19, 2026Junkai Liu, Ling Shao, Le ZhangMedical Image Generation 3D Latent Diffusion Model
Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection
Feb 16, 2026Chanhui Lee, Donggyu Choi, Seunghyun Shin +2Diffusion Editing Adversarial Transferability
Enhancing Adversarial Transferability through Block Stretch and Shrink
Nov 21, 2025Quan Liu, Feng Ye, Chenhao Lu +4Adversarial Transferability Transformation-Based Attacks
Rethinking Robust Adversarial Concept Erasure in Diffusion Models
Oct 31, 2025Qinghong Yin, Yu Tian, Heming Yang +5Concept Erasure Adversarial Transferability
Solver-Integrated Adversarial Attacking and Training of Neural Operators
Oct 21, 2025Yifei SunNeural Operators Adversarial Transferability
Information-Geometric Inverse Distillation for Enhancing Adversarial Transferability
Feb 24, 2025Wenyuan Wu, Yuan Sun, Yingke Chen +4Adversarial Transferability Chain-Of-Thought Distillation
Exploiting Vision Encoder Vulnerabilities for Universal Adversarial Perturbations on Large Vision-Language Models
Dec 11, 2024Hee-Seon Kim, Minbeom Kim, Seokil Ham +1Large Vision Language Models Adversarial Examples
Expected flow networks in stochastic environments and two-player zero-sum games
Oct 4, 2023Marco Jiralerspong, Bilun Sun, Danilo Vucetic +4Generative Flow Networks Self-Play
Guided Adversarial Robust Transfer Learning with Source Mixing
Date pendingXin Xiong, Zijian Guo, Tianxi CaiTransfer Learning Adversarial Transferability
MRI-based Deep Radiomic Phenotyping of Neuromuscular Disorders: A Topology-driven Characterization
Date pendingMartyna Żur, \Lukasz Piórecki, Marek Socha +5Key Vertebral Morphometry Variables Biomarker

May 28, 2026 · cs.AIJ/K move · Enter open · S save

Harnessing non-adversarial robustness in large language models

Qinghua Zhou, Ellina Aleshina, Andrey Lovyagin, Oleg Somov+5

The work presents an approach for addressing the challenge of robustness in Large Language Models (LLMs) to alterations and potential errors caused by semantically similar but textually different prompts. Recent works have shown that these kinds of prompt variations can significantly impact the performance of LLMs on tasks. The central question is: can LLMs' robustness to semantically-neutral prompt alterations be acquired without expensive retraining of the entire model? We address this question both theoretically and through experiments. Our theoretical analysis reveals a crucial factor impacting model robustness - a systematic expected shift or perturbation-induced bias in neural network module outputs. Motivated by this analysis, we show that robustness can be achieved via a simple fine-tuning process: debiasing for robustness. We identify conditions when debiasing helps and when it does not, and demonstrate, through both theory and extensive experiments, that debiasing for robustness may indeed be a quick and efficient tool to enhance robustness and provide certification against random prompt perturbations.

Perturbations Adversarial Transferability

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