Black-Box Adversarial Attacks

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8 papers in the last four weeks, up 60% on the four weeks before. 0.1% of all new papers.

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Latest papers 115

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Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment
May 11, 2026Haobo Wang, Xiaorong Ma, Weiqi Luo +2Adversarial Attacks on VLMs Multimodal Large Language Models
Sun Yat-sen University, China · Nanyang Technological University, Singapore · Shenzhen MSU-BIT University, China
LLM-Agnostic Semantic Representation Attack
May 9, 2026Jiawei Lian, Jianhong Pan, Lefan Wang +4Adversarial Prompt Generation Adversarial Attacks on LLMs
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR · Hong Kong Institute of AI for Science, City University of Hong Kong, Hong Kong SAR
On the Hardness of Junking LLMs
May 6, 2026Marco Rando, Samuel VaiterLLM Backdoor Attacks LLM Jailbreak Attacks
Université Côte d’Azur, Inria, CNRS, LJAD, Nice, France · CNRS & Université Côte d’Azur, LJAD, Nice, France
Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training
May 4, 2026Wenjing Duan, Qi Zhou, Yuanfan LiAdversarial Training AI-Generated Text Detection
Faculty of Electronic and Information Engineering, Xi’an Jiaotong University
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
Low Rank Adaptation for Adversarial Perturbation
Apr 30, 2026Han Liu, Shanghao Shi, Yevgeniy Vorobeychik +2Adversarial Attacks Low-Rank Approximation
Washington University in St. Louis
Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines
Apr 26, 2026Mazal Bethany, Kim-Kwang Raymond Choo, Nishant Vishwamitra +1Fake News Detection Adversarial Prompt Generation
Department of Information Systems and Cybersecurity, University of Texas at San Antonio, San Antonio, TX 78249 USA
Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models
Apr 23, 2026Naheed Rayhan, Sohely JahanLanguage Model Safety Evaluation Adversarial Attacks on LLMs
Jagannath University · University of Barishal
EvoJail: Evolutionary Diverse Jailbreak Prompt Generation for Large Language Models
Apr 22, 2026Rui Tang, Kaiyu Xu, Pengsen Cheng +3Adversarial Prompt Generation LLM Jailbreak Attacks
aSchool of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China · dCollege of Computer Science, Sichuan University, Chengdu 610065, China · aSchool of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China bKey Laboratory of Data Protection and Intelligent Management (Sichuan University), Ministry of Education, Chengdu 610065, China
Beyond Explicit Refusals: Soft-Failure Attacks on Retrieval-Augmented Generation
Apr 20, 2026Wentao Zhang, Yan Zhuang, ZhuHang Zheng +3Retrieval-Augmented Generation RAG Security
University of Electronic Science and Technology of China, Chengdu, China · Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China
When Background Matters: Breaking Medical Vision Language Models by Transferable Attack
Apr 19, 2026Akash Ghosh, Subhadip Baidya, Sriparna Saha +1VLM Robustness Adversarial Attacks on VLMs
Indian Institute of Technology Patna1 · Work done as Visiting Researcher at MBZUAI · MBZUAI3 +1
Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization
Apr 16, 2026Haochun Tang, Yuliang Yan, Jiahua Lu +2Adversarial Prompt Generation Cost-Aware Inference
1Key Laboratory of Symbolic Computation and Knowledge Engineering, MoE, Jilin University · 2The Hong Kong University of Science and Technology (Guangzhou)
Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification
Apr 16, 2026Weiwei Zhuang, Wangze Xie, Qi Zhang +9Remote Sensing Image Understanding Transfer-Based Adversarial Attacks
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361000, China · Faculty of Data Science, City University of Macau, Macau SAR, China · Dundee International Institute, Central South University, Changsha, China +7
Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection
Apr 9, 2026Wenkui Yang, Chao Jin, Haisu Zhu +7GUI Agents AI Agent Security
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · 2MAIS&NLPR, Institute of Automation, Chinese Academy of Sciences · 4ShanghaiTech University +2
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
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
Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
Jun 30, 2025Tim Roith, Leon Bungert, Philipp WackerEvolutionary Optimization Black-Box Adversarial Attacks
TUM School of Computation, Information and Technology and Munich Center for Machine Learning (MCML), Boltzmannstr. 3, 85748 Garching bei München, Germany · Institute of Mathematics, Center for Artificial Intelligence and Data Science (CAIDAS), University of Würzburg, Emil-Fischer-Str. 40, 97074 Würzburg, Germany · School of Mathematics and Statistics, University of Canterbury
HauntAttack: When Attack Follows Reasoning as a Shadow
Jun 8, 2025Jingyuan Ma, Rui Li, Zheng Li +4Adversarial Attacks on LLMs Jailbreak Attacks
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University · StepFun · Department of Computing, The Hong Kong Polytechnic University +1
How Vulnerable Is My Learned Policy? Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies
Feb 6, 2025Akansha Kalra, Basavasagar Patil, Guanhong Tao +1Adversarial Robustness Adversarial Imitation Learning
University of Utah
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
Perturbation Effects on Robustness and Individual Fairness
Apr 1, 2024Xuran Li, Hao Xue, Peng Wu +4Neural Network Robustness Algorithmic Fairness
University of New South Wales Sydney, Australia · The Hong Kong University of Science and Technology Guangzhou, China · Key Laboratory of System Software, Institute of Software, Chinese Academy of Sciences Beijing, China +3
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
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
Dec 4, 2023Xunguang Wang, Pingchuan Ma, Zhenlan Ji +3Adversarial Attacks on VLMs Large Vision-Language Models
The Hong Kong University of Science and Technology, Hong Kong, China
Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Date pendingKamel Kamel, Hridoy Sankar Dutta, Keshav Sood +1Audio Deepfake Detection Speaker Verification
School of Information Technology, Deakin University, Australia

May 11, 2026, cs.CVJ/K move · Enter open · S save

Adversarial Attacks Against MLLMs via Progressive Resolution Processing and Adaptive Feature Alignment

Haobo Wang, Xiaorong Ma, Weiqi Luo, Xiaojun Jia+1

Sun Yat-sen University, China · Nanyang Technological University, Singapore · Shenzhen MSU-BIT University, China

Adversarial Attacks on VLMs Multimodal Large Language Models Transfer-Based Adversarial Attacks Black-Box Adversarial Attacks Adversarial Transferability

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Adversarial perturbations can mislead Multimodal Large Language Models (MLLMs) recognize a benign image as a specific target object, posing serious risks in safety-critical scenarios such as autonomous driving and medical diagnosis. This makes transfer-based targeted attacks crucial for understanding and improving black-box MLLM robustness. Existing transfer-based targeted attack methods typically rely on the final global features of the surrogate encoder and anchor optimization to original-resolution target crops, leading to their limited transferability and robustness. To address these challenges, we propose Progressive Resolution Processing and Adaptive Feature Alignment (PRAF-Attack), a targeted transfer-based attack framework that integrates multi-scale global semantic guidance with robust intermediate-layer local alignment. Unlike prior methods that align only the surrogate encoder's final layer, we design an adaptive feature alignment strategy that leverages intermediate representations to enhance transferability. Specifically, we introduce an adaptive intermediate layer selection mechanism to identify transferable hierarchical features across surrogate ensembles via gradient consistency, along with an adaptive patch-level optimization strategy that preserves highly correlated local regions through efficient patch filtering. To overcome the reliance on fixed original-resolution target crops, we propose a progressive resolution processing strategy that gradually refines optimization from coarse to fine, enabling the attack to better exploit target information at multiple scales and achieve stronger transferability. We evaluate PRAF-Attack on a diverse suite of black-box MLLMs, including six open-source models and six closed-source commercial APIs. Compared with seven state-of-the-art targeted attack baselines, the proposed PRAF-Attack consistently achieves superior transferability.

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