Adversarial Attacks

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28 papers in the last four weeks, up 155% on the four weeks before. 0.3% of all new papers.

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Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding
May 1, 2026An Huang, Junggab Son, Zuobin XiongAdversarial Attacks Diffusion Models
Department of Computer Science, University of Nevada Las Vegas, Las Vegas, USA.
Attention Is Where You Attack
Apr 30, 2026Aviral Srivastava, Sourav PandaSelf-Attention Adversarial Attacks
Amazon · The Pennsylvania State University
Low Rank Adaptation for Adversarial Perturbation
Apr 30, 2026Han Liu, Shanghao Shi, Yevgeniy Vorobeychik +2Adversarial Attacks Low-Rank Approximation
Washington University in St. Louis
One Perturbation, Two Failure Modes: Probing VLM Safety via Embedding-Guided Typographic Perturbations
Apr 28, 2026Ravikumar Balakrishnan, Sanket MendaparaVLM Evaluation VLM Robustness
Cisco Systems
Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving
Apr 26, 2026Xinyu Zeng, Xiangkun He, Lei Tao +2Flow Matching End-to-End Autonomous Driving
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 · School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
Apr 25, 2026Jingyu Zhang, Jacky Wai Keung, Yan Xiao +3End-to-End Autonomous Driving Adversarial Attacks
Department of Computer Science, City University of Hong Kong, Hong Kong SAR · School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
Apr 24, 2026Lukáš Hrdonka, Martin JurečekAdversarial Attacks Malware Classification
Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, Prague, Czech Republic
Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
Apr 24, 2026Olha Jurečková, Martin Jureček, Matouš Kozák +1Adversarial Attacks Adversarial Robustness
Faculty of Information Technology, Czech Technical University in Prague, Th´akurova 9, Prague, 16000, Czech Republic
Sharpness-Aware Poisoning: Enhancing Transferability of Injective Attacks on Recommender Systems
Apr 24, 2026Junsong Xie, Yonghui Yang, Pengyang Shao +1Sharpness-Aware Minimization Adversarial Attacks
Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei 230009, China · Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei 230009, China · Laboratory of NExT++, National University of Singapore, Singapore
Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
Apr 23, 2026Pawan Acharya, Lan ZhangConcept Drift Adversarial Attacks
Northern Arizona University, Flagstaff, AZ, USA
Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
Apr 22, 2026Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo +2Randomized Smoothing Adversarial Attacks
Department of Computer Applications, Cochin University of Science and Technology, India · Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Adolfo Ferrata 5, 27100, Pavia, Italy
PASTA: A Patch-Agnostic Twofold-Stealthy Backdoor Attack on Vision Transformers
Apr 21, 2026Dazhuang Liu, Yanqi Qiao, Rui Wang +2Adversarial Attacks Vision Transformer
Delft University of Technology, Delft, the Netherlands. · University of Turku, Turku, Finland
Temporal UI State Inconsistency in Desktop GUI Agents: Formalizing and Defending Against TOCTOU Attacks on Computer-Use Agents
Apr 20, 2026Wenpeng XuAdversarial Attacks Computer-Use Agents
University of California, San Diego
Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems
Apr 20, 2026Junyi Yao, Zihao Zheng, Jiayu LongAlgorithmic Auditing Learning to Rank
Washington University in St. Louis USA
No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
Apr 16, 2026Francesco Diana, Chuan Xu, André Nusser +1Gradient Inversion Attacks Adversarial Attacks
Université Côte d’Azur, France · Inria, France · CNRS, France +1
When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPs
Apr 11, 2026Jose Efraim Aguilar Escamilla, Haoyang Hong, Jiawei Li +4Markov Decision Processes Adversarial Attacks
Oregon State University, OR, USA · University of Illinois Urbana–Champaign, IL, USA · Princeton University, NJ, USA +1
On Optimizing Multimodal Jailbreaks for Spoken Language Models
Mar 19, 2026Aravind Krishnan, Karolina Stańczak, Dietrich KlakowAdversarial Attacks LLM Jailbreak Attacks
Saarland University, Germany · DFKI GmbH, Saarbrücken, Germany · ETH AI Center, Switzerland
Dual-Modality Multi-Stage Adversarial Safety Training: Robustifying Multimodal Web Agents Against Cross-Modal Attacks
Mar 4, 2026Haoyu Liu, Dingcheng Li, Lukas Rutishauser +1Adversarial Training Multimodal Robustness
UC Berkeley, IEOR & BAIR · Google · Google Deepmind
Generating adversarial inputs for a graph neural network model of AC power flow
Feb 20, 2026Robert ParkerNeural Surrogate Modeling Graph Neural Networks
Los Alamos National Laboratory, Los Alamos, NM, USA
Temperature Scaling Attack Disrupting Model Confidence in Federated Learning
Feb 6, 2026Kichang Lee, Jaeho Jin, JaeYeon Park +2Adversarial Attacks Federated Learning
Yonsei University · Dankook University
Exploring Sparsity and Smoothness of Arbitrary Lp Norms in Adversarial Attacks
Feb 6, 2026Christof Duhme, Florian Eilers, Xiaoyi JiangAdversarial Attacks Adversarial Perturbations
Department of Computer Science University of Münster Münster, Germany
Perturbing the Phase: Analyzing Adversarial Robustness of Complex-Valued Neural Networks
Feb 6, 2026Florian Eilers, Christof Duhme, Xiaoyi JiangAdversarial Attacks Neural Network Robustness
Department of Computer Science University of Münster Münster, Germany
Stage-wise Attention-Guided Region Sequencing for Adversarial Attacks on Large Vision-Language Models
Feb 4, 2026Jaehyun Kwak, Nam Cao, Boryeong Cho +3Adversarial Attacks on VLMs Adversarial Attacks
KAIST · KENTECH
From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching
Jan 30, 2026Zhixiang Zhang, Zesen Liu, Yuchong Xie +2Adversarial Attacks LLM Agent Security
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology · Fudan University
Mind the Gap: Action Rebinding Attacks against Android GUI Agents
Jan 18, 2026Yi Qian, Kunwei Qian, Xingbang He +7Mobile GUI Automation Adversarial Attacks
State Key Laboratory for Novel Software Technology, Nanjing University · Hornor Device Co., Ltd · Institute of Dataspace, Hefei Comprehensive National Science Center China
DDSA: Dual-Domain Strategic Attack for Spatial-Temporal Efficiency in Adversarial Robustness Testing
Jan 18, 2026Jinwei Hu, Shiyuan Meng, Yi Dong +1Adversarial Attacks Adversarial Robustness
University of Liverpool, Liverpool, UK · Shanghai Artificial Intelligence Laboratory, Shanghai, China
Self-Creating Random Walks for Decentralized Learning under Pac-Man Attacks
Jan 12, 2026Xingran Chen, Parimal Parag, Rohit Bhagat +1Adversarial Attacks Decentralized Learning
Engineering Systems and Design Pillar, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372 · Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India · Department of Electrical and Computer Engineering, Rutgers University, Piscataway Township, NJ 08854, USA
High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models
Dec 26, 2025Mengqi He, Xinyu Tian, Xin Shen +4VLM Robustness Adversarial Attacks on VLMs
The Australia National University Canberra, Australia · The University of Queensland Brisbane, Queensland, Australia · GE research Niskayuna, New York, USA
Enhancing Adversarial Transferability through Block Stretch and Shrink
Nov 21, 2025Quan Liu, Feng Ye, Chenhao Lu +4Adversarial Attacks Adversarial Perturbations
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
Nov 12, 2025Tairan Huang, Yulin Jin, Junxu Liu +2Adversarial Attacks Adversarial Perturbations
The Hong Kong Polytechnic University Hong Kong, China

May 1, 2026, cs.LGJ/K move · Enter open · S save

Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding

An Huang, Junggab Son, Zuobin Xiong

Department of Computer Science, University of Nevada Las Vegas, Las Vegas, USA.

Adversarial Attacks Diffusion Models

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Diffusion models have become the foundation of modern generative systems, with most research focusing primarily on improving generation efficiency and output quality. The timestep embedding component is a crucial part of the diffusion pipeline, which provides a temporal conditioning signal to the denoising network, enabling it to adapt its predictions across different noise levels throughout the process. Despite their potential to contain substantial information, timestep embeddings remain underexplored in current research, especially for security risks and reliable provenance. To fill this gap, we introduce Shadow Timestep Embedding (STE), a novel mechanism that investigates the underutilized temporal space for malicious information injection into diffusion models. In particular, when zooming in on the timestep embedding space, we find that different timesteps exhibit distinct representational capabilities that can encode side-channel information. Moreover, such encoded information can be utilized for attack and defense purposes through the scheduler interface. We present a theoretical analysis of timestep embeddings as position-encoding mappings and derive a mutual coherence evaluation that explains the separability of disjoint timestep intervals. Our findings reveal the diffusion model's timestep as a powerful side channel for carrying dedicated information, motivating new directions for adversarial generative modeling by understanding the temporal dimension.

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