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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Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings
May 16, 2026Anthonio Oladimeji Gabriel, Ahmad Rufai YusufMultilingual Language Model Evaluation Healthcare
Centre for Clinical Intelligence & Safety, Ìyàwó, Nigeria · Tomorrow University of Applied Sciences, Germany
Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations
May 15, 2026Narges Babadi, Hadis KarimipourVLM Robustness Adversarial Attacks on VLMs
Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada.
SLEIGHT-Bench: A Benchmark of Evasion Attacks Against Agent Monitors
May 15, 2026Elle Najt, Colin Toft, Tyler Tracy +2Software Engineering Agents Adversarial Attacks
1Anthropic Fellows Program · University of Waterloo · 3Redwood Research +1
Semantic Smoothing via Novel View Synthesis for Robust SAR Image Classification
May 15, 2026Daniel Brignac, Fengwei Tian, Banafsheh Latibari +2Randomized Smoothing Adversarial Attacks
The University of Arizona Tucson, Arizona, United States
Fast Adversarial Attacks with Gradient Prediction
May 14, 2026Kamil Ciosek, Aleksandr V. Petrov, Nicolò Felicioni +1Adversarial Attacks Neural Network Robustness
Spotify
Enabling Adversarial Robustness in AI Models through Kubeflow MLOps
May 14, 2026Stavros Bouras, Ioannis Korontanis, Antonios Makris +1Adversarial Attacks Adversarial Robustness
School of Electrical and Computer Engineering, National Technical University of Athens, Greece · Department of Informatics and Telematics, Harokopio University of Athens, Greece
Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusion
May 14, 2026Chenyi Wang, Ruoyu Song, Raymond Muller +5HD Map Construction Adversarial Attacks
University of Arizona · Purdue University · Lawrence Livermore National Laboratory +2
Backbone is All You Need: Assessing Vulnerabilities of Frozen Foundation Models in Synthetic Image Forensics
May 13, 2026Chiara Musso, Joy Battocchio, Andrea Montibeller +1Adversarial Attacks Vision Transformer
University of Trento Trento, Italy
Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning
May 13, 2026Hao Zhou, Tiru Wu, Yan Jiang +3Multimodal Robustness Adversarial Attacks
Finding the Weakest Link: Adversarial Attack against Multi-Agent Communications
May 13, 2026Maxwell Standen, Junae Kim, Claudia SzaboAdversarial Attacks Adversarial Perturbations
The University of Adelaide · DST Group
REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
May 12, 2026Buyun Liang, Jinqi Luo, Liangzu Peng +6Adversarial Prompt Generation Adversarial Attacks
University of Pennsylvania
SoK: A Comprehensive Analysis of the Current Status of Neural Tangent Generalization Attacks with Research Directions
May 12, 2026Thushari Hapuarachchi, Kaiqi XiongAdversarial Attacks Adversarial Robustness
University of South Florida Tampa, USA
Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving
May 12, 2026Shuo Ju, Qingzhao Zhang, Huashan Chen +6Adversarial Attacks Autonomous Driving Perception
Institute of Information Engineering, Chinese Academy of Sciences · The University of Arizona · Beijing Jiaotong University +1
Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
May 12, 2026Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia +6Adversarial Attacks Adversarial Robustness
Faculty of Computer Science, University of New Brunswick, Fredericton, Canada. · Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran. · Faculty of Mathematical and Computer Science, Kharazmi University, Tehran, Iran. +4
Adversarial SQL Injection Generation with LLM-Based Architectures
May 11, 2026Ali Karakoc, H. Birkan YilmazRetrieval-Augmented Generation Adversarial Attacks
Department of Computer Engineering, NETLAB, Bogazici University, Istanbul, Turkey.
Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization
May 11, 2026Mengqi He, Xinyu Tian, Xin Shen +6Adversarial Attacks on VLMs Adversarial Attacks
Australian National University, ACT, Australia · The University Of Queensland, Brisbane, Queensland, Australia · GE research, Niskayuna, New York, USA +2
Skill Description Deception Attack against Task Routing in Internet of Agents
May 11, 2026Jiayi He, Xiaofeng Luo, Jiawen Kang +3Adversarial Attacks AI Agent Security
School of Automation Guangdong University of Technology Guangzhou, China · College of Computing and Data Science Nanyang Technological University Singapore, Singapore · The State Key Laboratory of Public Big Data Guizhou University Guiyang, China +1
AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
May 9, 2026Tingxi Li, Mingfang Ji, Ravishka Shemal Rathnasuriya +3Efficient Neural Network Inference Adversarial Attacks
The University of Texas at Dallas · Tianjin University
Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression
May 9, 2026Jiaming Liang, Chi-Man Pun, Weisi Lin +1Learned Image Compression Adversarial Attacks
University of Macau Macau, China · Nanyang Technological University Singapore
ATAAT: Adaptive Threat-Aware Adversarial Tuning Framework against Backdoor Attacks on Vision-Language-Action Models
May 9, 2026Kewei Chen, Yayu Long, Shuai Li +1Adversarial Attacks Adversarial Attacks on VLA Models
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences · Chongqing School, University of Chinese Academy of Sciences · Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows
May 7, 2026Prateek Paudel, Nitin Jha, Abhishek Parakh +1Adversarial Attacks Network Intrusion Detection
Kennesaw State University, Marietta, GA, USA · University of Nebraska Omaha, Omaha, NE, USA
Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
May 7, 2026Tran Gia Bao Ngo, Zulfikar Alom, Federico Errica +2Graph Neural Networks Adversarial Attacks
Department of Computer Science, University of Manitoba · University of Toledo · NEC Laboratories Europe +2
MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents
May 5, 2026Jonathan Steinberg, Oren GalLanguage Model Safety Evaluation AI Coding Agents
Swarms & AI Lab (SAIL), University of Haifa
TsallisPGD: Adaptive Gradient Weighting for Adversarial Attacks on Semantic Segmentation
May 5, 2026Alexander Matyasko, Xin Lou, Indriyati Atmosukarto +1Adversarial Attacks Adaptive Loss Weighting
Singapore Institute of Technology, Singapore
Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning
May 3, 2026Zhiyang Dai, Yansong Gao, Boyu Kuang +5Contrastive Learning Dataset Watermarking
School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · University of Western Australia, Perth, Australia · College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China +3
Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models
May 2, 2026Amin Bigdeli, Amir Khosrojerdi, Radin Hamidi Rad +3Learning to Rank Adversarial Attacks
University of Waterloo, Canada · University of Toronto, Canada · Mila – Quebec AI Institute, Canada +1
Asymmetric Invertible Threat: Learning Reversible Privacy Defense for Face Recognition
May 2, 2026Jiabei Zhang, Ziyuan Yang, Andrew Beng Jin Teoh +1Adversarial Training Adversarial Attacks
School of Cyber Science and Engineering, Sichuan University · Lee Kong Chian School of Medicine, Nanyang Technological University · School of Electrical and Electronic Engineering, Yonsei University
When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems
May 1, 2026Lingxi Zhang, Guangtao Zheng, Hanjie ChenAdversarial Attacks Language Model Robustness
1Rice University · University of Virginia
Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters
May 1, 2026Alexander Warnecke, Konrad RieckAdversarial Attacks Adversarial Examples
1BIFOLD & TU Berlin
Adaptive Equilibrium: Dynamic Weighting Framework for Generalized Interruption of DeepFake Models
May 1, 2026Hongrui Zheng, Liejun Wang, Zhiqing GuoAdversarial Attacks Adaptive Loss Weighting
School of Computer Science and Technology, Xinjiang University, Urumqi, China · Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Urumqi, China · Silk Road Multilingual Cognitive Computing International Cooperation Joint Laboratory, Xinjiang University, Urumqi, China

May 16, 2026, cs.CYJ/K move · Enter open · S save

Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings

Anthonio Oladimeji Gabriel, Ahmad Rufai Yusuf

Centre for Clinical Intelligence & Safety, Ìyàwó, Nigeria · Tomorrow University of Applied Sciences, Germany

Multilingual Language Model Evaluation Healthcare Adversarial Attacks Adversarial Robustness Clinical Language Model Evaluation

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Current clinical artificial intelligence (AI) systems are evaluated almost exclusively on clean, standardised, English-language inputs, conditions that do not reflect the realities of healthcare delivery in low-resource settings. This study presents the first systematic dual audit of two orthogonal safety vulnerabilities in clinical AI: adversarial image fragility and cross-lingual diagnostic drift. Using DenseNet121, the architecture underlying CheXNet, fine-tuned on the COVID-QU-Ex chest X-ray dataset (85,318 images; COVID-19, Non-COVID Pneumonia, Normal), we demonstrate that diagnostic accuracy collapses from 89.3% to 62.0% under a Fast Gradient Method (FGM) perturbation of epsilon=0.021, a magnitude imperceptible to the human eye. Standard defensive strategies including Gaussian smoothing and ensemble voting failed to restore clinical safety. In a parallel language fragility experiment, we tested Llama3.1:8b and NatLAS (N-ATLAS) on 20 COVID-19 clinical cases presented in Standard English, Nigerian Pidgin (Naija), and Yoruba-inflected English. Both models exhibited significant accuracy degradation: Llama3.1:8b dropped from 80.0% to 65.0% on Pidgin; NatLAS, an African-context model, collapsed from 85.0% to 55.0%, with diagnosis consistency falling to 50%. These findings establish a quantitative failure envelope for clinical AI under conditions representative of Primary Health Centre (PHC) deployment in Nigeria, and motivate urgent calls for adversarially hardened, linguistically inclusive clinical AI architectures.

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