Adversarial Attacks

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Adversarial attacks study how deliberately chosen inputs, training data, model parameters, or system interactions can make AI systems fail or behave as intended by an attacker. Common approaches include gradient-based perturbations, black-box query and evolutionary search, generative or semantic changes, physical patches and sensor interference, and poisoning or backdoors. Work spans image, language, audio, graph, and time-series models as well as federated learning, recommenders, robotics, and AI agents, with evaluations on benchmarks and increasingly realistic deployment settings. A central tension is that attacks must be effective and stealthy under practical constraints, while defenses and benchmarks must keep pace with adaptive threats and distinguish digital robustness from real-world resilience.

Latest papers

451 papers54 papers in the last four weeks, up 69% on the four weeks before. 0.3% of all new papers.

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
Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning
Nov 12, 2025Jian Wang, Hong Shen, Chan-Tong LamAdversarial Attacks Backdoor Attacks in Federated Learning
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China · School of Cyberspace Security, Guangzhou University of Software, Guangzhou, China · School of Engineering and Technology, Central Queensland University, Australia
Solver-Integrated Adversarial Attacking and Training of Neural Operators
Oct 21, 2025Yifei SunAdversarial Training Adversarial Attacks
UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks
Oct 19, 2025Mansi Phute, Matthew Hull, Haoran Wang +6Adversarial Attacks Adversarial Perturbations
Georgia Institute of Technology, USA · Technology Innovation Institute, UAE
Wasserstein Robust Classification Error: Surrogate Loss Misalignment and Distributional Attacks
Oct 11, 2025Bach C. Le, Tung V. Dao, Binh T. Nguyen +1Robust Classification Adversarial Robustness
College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam
Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning
Sep 26, 2025Li Xia, Jing Yu, Zheng Liu +3Gradient Inversion Attacks Adversarial Attacks
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China
Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Sep 9, 2025Kamel Kamel, Hridoy Sankar Dutta, Keshav Sood +1Audio Deepfake Detection Speaker Verification
School of Information Technology, Deakin University, Australia
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Aug 27, 2025Viktor Valadi, Lucas Beerens, Mattias Åkesson +4Gradient Inversion Attacks Adversarial Attacks
Scaleout Systems Sweden · Recorded Future Sweden · AI Sweden Sweden +1
On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification
Jun 14, 2025Matteo Vilucchio, Lenka Zdeborová, Bruno LoureiroAdversarial Attacks Adversarial Robustness
Information Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Département d’Informatique, École Normale Supérieure - PSL & CNRS, France
Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning
May 26, 2025Shijie Liu, Andrew C. Cullen, Paul Montague +2Adversarial Attacks Backdoor Attacks
School of Computing and Information Systems, University of Melbourne, Parkville, Australia · DST Group, Adelaide
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
May 19, 2025Sanggeon Yun, Ryozo Masukawa, Hyunwoo Oh +2Adversarial Attacks Adversarial Examples
Department of Computer Science University of California, Irvine · Whiting School of Engineering Johns Hopkins University
How to Backdoor Image Knowledge Distillation
Apr 30, 2025Qian Ma, Chen Wu, Prasenjit Mitra +1Adversarial Attacks Data Poisoning Attacks
The Pennsylvania State University, State College, USA · Carnegie Mellon University Africa, Kigali, Rwanda
Boosting the Local Invariance for Better Adversarial Transferability
Mar 8, 2025Bohan Liu, Xiaosen WangAdversarial Attacks Transfer-Based Adversarial Attacks
School of Computer Science and Technology, Xidian University, Xi’an 710126, China · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Approaching the Harm of Gradient Attacks While Only Flipping Labels
Feb 28, 2025Abdessamad El-Kabid, El-Mahdi El-MhamdiFederated Learning Aggregation Adversarial Attacks
École Polytechnique
On Adversarial Attacks In Acoustic Drone Localization
Feb 27, 2025Tamir Shor, Chaim Baskin, Alex BronsteinAdversarial Attacks UAV Navigation
Department of Computer Science Technion – Israel Institute of Technology Haifa, Israel · School of Electrical and Computer Engineering Ben-Gurion University of the Negev Be’er Sheva, Israel · Technion – Israel Institute of Technology +1
Towards Reliable Recommender Systems for Rating Data
Dec 30, 2024Aurore Archimbaud, Andreas Alfons, Ines WilmsRobust Matrix Completion Adversarial Attacks
TBS Business School · Erasmus University Rotterdam · Maastricht University
GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning
Dec 17, 2024Rui Zhang, Ka-Ho ChowGradient Inversion Attacks Adversarial Attacks
HKU Musketeers Foundation Institute of Data Science · The University of Hong Kong · Hong Kong SAR, China +1
Stealthy Multi-Task Adversarial Attacks
Nov 26, 2024Jiacheng Guo, Tianyun Zhang, Lei Li +3Adversarial Attacks Adversarial Perturbations
Cleveland State University, Cleveland, OH 44115, USA · University of Wisconsin-Madison, Madison, WI 53706, USA · Western Digital Research, San Jose, CA 95119, USA
Adversarial Robustness of AI-Generated Image Detectors in the Real World
Oct 2, 2024Sina Mavali, Jonas Ricker, David Pape +2Adversarial Attacks Adversarial Robustness
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Ruhr University Bochum, Bochum, Germany
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Jun 14, 2024Srishti Gupta, Zhang Chen, Luca Demetrio +9Overparameterized Neural Network Adversarial Attacks
Sapienza University of Rome, Rome, Italy. · University of Cagliari, Cagliari, Italy. · Northwestern Polytechnical University, Xi’An, China. +1
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems
Feb 6, 2024Oubo Ma, Yuwen Pu, Linkang Du +5Adversarial Attacks Partially Observable RL
Zhejiang University · Xi’an Jiaotong University · Laboratory for Big Data and Decision +2
SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models
Dec 13, 2023Feifei Wang, Zhentao Tan, Tianyi Wei +2Adversarial Attacks Adversarial Attacks on Diffusion Models
University of Science and Technology of China · Alibaba Cloud
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
Nov 20, 2023Guangjing Wang, Ce Zhou, Yuanda Wang +3Adversarial Attacks Transfer-Based Adversarial Attacks
University of South Florida, USA · Missouri University of Science and Technology, USA · Michigan State University, USA +2
Entropic perspective on AI resilience, data-minimal learning and optimal control
Oct 19, 2023Illia HorenkoData-Efficient Learning Adversarial Robustness
Chair for Mathematics of AI, Faculty of Mathematics, RPTU Kaiserslautern-Landau, Gottlieb-Daimler-Str. 48, Kaiserslautern, 67663, Germany.
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Oct 6, 2023Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4Byzantine-Robust Federated Learning Adversarial Attacks
University of California, Irvine · Texas A&M University · University of Birmingham +3

Jan 18, 2026J/K move · Enter open · S save

Mind the Gap: Action Rebinding Attacks against Android GUI Agents

Yi Qian, Kunwei Qian, Xingbang He, Ligeng Chen+6

State Key Laboratory for Novel Software Technology, Nanjing University · Hornor Device Co., Ltd · Institute of Dataspace, Hefei Comprehensive National Science Center China

Mobile GUI Automation Adversarial Attacks GUI Agents AI Agent Security Mobile GUI Agents

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Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted to perceive screen content and inject inputs across application boundaries. While these agents aim to automate complex tasks, we demonstrate that their design introduces a fundamental conflict with Android's strict application sandboxing. We present a novel cross-application Action Rebinding attack, which allows a malicious application with zero dangerous permissions to hijack the agent's execution and perform privileged operations on behalf of the attacker. Our attack exploits the inevitable observation-action gap inherent in the agent's reasoning pipeline. A malicious app can render a benign ``contextual carrier'' to elicit a planned action, and then swap the foreground to a sensitive target application during the reasoning latency. The agent, unaware of the transition, unwittingly executes the action in the privileged context. We further advance this attack by weaponizing the agent's own task-recovery logic to create programmable, multi-step exploit loops , and introducing an Intent Alignment Strategy (IAS) that manipulates the agent's reasoning to rationalize the hijacked state. We evaluate our attack on six widely-used Android GUI agents. Our results demonstrate a 100% success rate for atomic action hijacking and the ability to orchestrate high-impact exploits, including unauthorized file deletion, SMS transmission, and app uninstallation, without the attacker holding any corresponding permissions. Furthermore, since the malicious application separates intent from capability and contains no privileged API calls, it achieves a 0% detection rate across commercial malware scanners (e.g., VirusTotal), highlighting a critical blind spot in current mobile security analysis. To access experimental logs and demonstration videos, please contact [email protected].