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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Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models
May 25, 2026Arian Komaei Koma, Seyed Amir Kasaei, AmirMahdi Sadeghzadeh +1Adversarial Prompt Generation Adversarial Attacks
Department of Computer Engineering Sharif University of Technology
Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
May 25, 2026David Košťál, Martin JurečekAdversarial Attacks Malware Classification
Department of Information Security, Faculty of Information Technology, Czech Technical University in Prague
Opportunistic Target Selection: Early Directional Commitment for Query-Efficient Black-Box Adversarial Attacks
May 25, 2026Florent Tariolle, Florian YgerAdversarial Attacks Black-Box Adversarial Attacks
INSA Rouen Normandy · LITIS
When Interpretability Becomes a Liability: Adversarial Attacks on CBM Concept Layers
May 25, 2026Aditya SridharConcept Bottleneck Models Adversarial Attacks
Independent Researcher · Atlanta, GA, USA
AI-Driven Adaptive Adversaries and the Erosion of Cryptographic Trust in Public Key Systems
May 23, 2026Petar RadanlievAdversarial Attacks Cybersecurity
Department of Computer Sciences, University of Oxford, Wolfson Building, Parks Rd, Oxford OX1 3QG · The Alan Turing Institute, British Library, 96 Euston Rd., London NW1 2DB
Making Brain-Computer Interfaces More Secure
May 22, 2026Md Fahimul Kabir Chowdhury, Gahangir HossainBrain-Computer Interfaces Adversarial Attacks
Department of Computer Science and Engineering, University of North Texas, USA · Department of Data Science, University of North Texas, USA
Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
May 22, 2026Ahmed Sabbah, Mohammed Kharma, Radi Jarrar +2Distribution Shift Robustness Concept Drift
aDepartment of Computer Science, Birzeit University, P.O. Box 14, Birzeit, P627, West2026 Bank, Palestine · bDepartment of Computer Science, University of Central Florida, 4328 Scorpius St., Building 116, Room 246, Orlando, 32816, FL, USAMay 22
Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
May 22, 2026Phuc Duc Nguyen, Quang Duc NguyenTest-Time Adaptation Adversarial Attacks
College of Computing and Data Science Nanyang Technological University Singapore
WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents
May 22, 2026Zhixiang Guo, Siyuan Liang, Shi Fu +4Adversarial Attacks Adversarial Robustness
Nanyang Technological University · University of Szeged
What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference
May 22, 2026Mingyuan Fan, Yu Liu, Fuyi Wang +1Privacy-Preserving Language Models Adversarial Attacks
East China Normal University Shanghai, China · RMIT University Melbourne, Australia
PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
May 21, 2026Zexin Li, Ziliang Zhang, Hyoseung Kim +1Adversarial Attacks Cooperative MARL
University of California, Riverside
Hidden-State Privacy Has an Empty Middle
May 21, 2026Alexander Okezue BellPrivacy-Preserving Language Models Adversarial Attacks
Stanford University 450 Jane Stanford Way Stanford, CA 94305
The Distillation Game: Adaptive Evaluations & Efficient Defenses
May 21, 2026Youssef Allouah, Mahdi Haghifam, Sanmi Koyejo +1Model Extraction Attacks Adversarial Attacks
Toyota Technological Institute at Chicago · National University of Singapore
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
May 21, 2026Tianyun Zhang, Zhen Yang, Haozhao Wang +2Federated Learning Aggregation Byzantine-Robust Federated Learning
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China · Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
RADAR: Defending RAG Dynamically against Retrieval Corruption
May 21, 2026Ziyuan Chen, Yueming Lyu, Yi Liu +4Retrieval-Augmented Generation Adversarial Attacks
School of Intelligence Science and Technology, Nanjing University, Suzhou, China · City University of Hong Kong, Hong Kong, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China
SDM: A Powerful Tool for Evaluating Model Robustness
May 19, 2026Xinlei Liu, Tao Hu, Jichao Xie +3Adversarial Attacks Neural Network Robustness
Information Engineering University, Zhengzhou, China · Key Laboratory of Cyberspace Endogenous Safety & Security of Henan Province, Zhengzhou, China · Key Laboratory of Cyberspace Security Ministry of Education of China, Zhengzhou, China +1
Auditing Privacy in Multi-Tenant RAG under Account Collusion
May 19, 2026Florian A. D. BurnatData Leakage Privacy Auditing
University of Bath, Bath, UK.
Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies
May 19, 2026Yasmine HayderGraph Representation Learning Adversarial Attacks
PETSCRAFT · LIFO, INSA CVL, Univ. Orléans, Inria, France
Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures
May 19, 2026Zeyao Liu, Zhendong Zhao, Xiaojun Chen +3Adversarial Attacks Vision Transformer
Institute of Information Engineering, Chinese Academy of Sciences · State Key Laboratory of Cyberspace Security Defense · School of Cyber Security, University of Chinese Academy of Sciences +1
Targeted Downstream-Agnostic Attack
May 19, 2026Zhuxin Lei, Ziyuan Yang, Yi ZhangAdversarial Attacks Adversarial Robustness
College of Computer Science, Sichuan University, Chengdu, China
Personalized Face Privacy Protection From a Single Image
May 18, 2026Zachary Yahn, Fatih Ilhan, Tiansheng Huang +5Adversarial Attacks Adversarial Perturbations
Georgia Institute of Technology, Atlanta, Georgia, USA
A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
May 18, 2026Mohamed elShehaby, Ashraf MatrawyAdversarial Attacks Network Intrusion Detection
Carleton University, Ottawa, Canada
Learning to Look Benign: Targeted Evasion of Malware Detectors via API Import Injection
May 18, 2026Juozas Dautartas, Olga Kurasova, Juozapas Rokas Čypas +1Adversarial Attacks Malware Classification
Institute of Data Science and Digital Technologies, Faculty of Mathematics and Informatics, Vilnius University, LT-08412 Vilnius, Lithuania
Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers
May 18, 2026Gabriel Sauger, Jean-Yves Marion, Sazzadur Rahaman +3Adversarial Attacks Malware Classification
UL, CNRS, LORIA, Inria · ∗Universit´e de Lorraine, CNRS, LORIA, F-54000 Nancy, France · CNRS, UL, LORIA, Inria +2
Whispers in the Noise: Surrogate-Guided Concept Awakening via a Multi-Agent Framework
May 18, 2026Mengyu Sun, Ziyuan Yang, Zunlong Zhou +3Adversarial Attacks Black-Box Adversarial Attacks
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University · Lee Kong Chian School of Medicine, Nanyang Technological University · School of Cyber Science and Engineering, Sichuan University
MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks
May 18, 2026Hyo Seo Kim, Gang Luo, Can Chen +3Evolutionary Optimization Adversarial Attacks
Illinois Institute of Technology · University of North Carolina at Chapel Hill · Singapore Management University
Active Defense Against False Data Injection Attacks in Robotic Manipulators
May 18, 2026Gabriele Gualandi, Carl Mikael Larsson, Alessandro V. PapadopoulosRobotic Control False Data Injection Attacks
Mälardalen University, Väster˚as, Sweden
Universal Adversarial Triggers
May 18, 2026Benedict Florance Arockiaraj, Alexander Feng, Jianxiong Cai +1Adversarial Attacks Adversarial Text Attacks
Unleashing the Representational Power of Fourier Shapes for Attacking Infrared Object Detection
May 18, 2026Yixing Yong, Jian Wang, Ming Lei +2Adversarial Attacks Infrared Object Detection
School of Information and Communications Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiao-tong University, Xi’an, China · School of Physics, Xi’an Jiao-tong University, Xi’an, China.
Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models
May 17, 2026Zhiqiang Wang, Dongrui Liu, Yan Li +4Adversarial Attacks on VLMs Adversarial Attacks
Hong Kong University of Science and Technology · Shanghai Jiao Tong University · Beihang University

May 25, 2026, cs.CVJ/K move · Enter open · S save

Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models

Arian Komaei Koma, Seyed Amir Kasaei, AmirMahdi Sadeghzadeh, Mohammad Hossein Rohban

Department of Computer Engineering Sharif University of Technology

Adversarial Prompt Generation Adversarial Attacks Diffusion Model Unlearning Black-Box Adversarial Attacks T2I Diffusion Models

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Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been introduced to make the model generate such unlearned concepts. These attacks, nevertheless, do not assume a realistic threat model, i.e. they either assume access to the model weights, or result in gibberish adversarial prompts that could be easily detected even through naive rule-based safeguarding. We aim to address this gap in this paper. We introduce BEAP, a black-box, embedding-aware adversarial prompting attack that leverages a large language model (LLM) to iteratively generate effective adversarial prompts and exploit such hidden vulnerabilities. BEAP performs an embedding-aware search in text space, combining multiple reward signals: unlearned concept presence, text-image alignment, and image quality, to refine generated prompts. Unlike previous attack methods, BEAP keeps its prompts undetectable to safety filters while producing high-quality images. Across five unlearning methods, BEAP achieves a macro-averaged ASR of 97.8% under the held-out OpenNSFW2 evaluation, exceeding the white-box UDA baseline by 41.6 percentage points (56.2% to 97.8%). Counting unsuccessful searches at the full 100-query budget, BEAP uses 32.1 image-generation queries per evaluated prompt on average under this criterion.

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Latest papers 292