Adversarial Attacks on Unified Multimodal Models

Latest papers

23 papers3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models
Oct 15, 2026Jiaming Zhang, Zherui Li, Siqi Guo +3VLM Robustness Adversarial Attacks on VLMs
College of Computing and Data Science Nanyang Technological University, Singapore
The Poisoned Conversation: Privacy-Leaking Watermarks in Unified Multimodal Models
Oct 4, 2026Tobias Braun, Jonas Henry Grebe, Emil Sivic +4Unified Multimodal Models Multimodal Generation
TU Darmstadt, Germany · hessian.AI, Germany · Zuse School
UnifiedAttack: Evaluating the Safety of Large Multimodal Models in Synergistic Harmful Image-Text Generation
Sep 29, 2026Bingjun Luo, Jialin Guo, Tony Wang +1Multimodal Jailbreak Attacks Adversarial Attacks on Unified Multimodal Models
Tsinghua University · Harbin Engineering University
SPARK: Representation-Level KV Memory Alignment for Safer Vision-Language Models
Sep 13, 2026Mohd Azfar, Izhar Dad KhanVLM Robustness Adversarial Attacks on VLMs
Multimodal Resource-Exhaustion Attacks on Vision-Language Models via Joint Pixel-Prompt Optimization
Sep 5, 2026Zhaoxiong Ni, Yatie Xiao, Chi-Man Pun +3Adversarial Attacks on VLMs Adversarial Attacks on Unified Multimodal Models
Guangzhou University School of Computer Science and Cyber Engineering Guangzhou, China · University of Macau Department of Computer and Information Science Macau, China · Guangzhou University School of Artificial Intelligence Guangzhou, China +1
Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain
Sep 1, 2026Daizong Liu, Junhao Dong, Zhiyuan Ma +6Adversarial Attacks on VLMs Adversarial Attacks
Institute for Math & AI, Wuhan University, Wuhan, 430072, China · College of Computing and Data Science, Nanyang Technological University, Singapore · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China +3
Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation
Aug 27, 2026Kaichao Jiang, Changtao Miao, Baiqi Wu +8Adversarial Attacks on Unified Multimodal Models Audio-Video Generation
University of Science and Technology of China · Independent Researcher · Zhejiang University +1
Text-Anchored Semantic Perturbations for Transferable Jailbreak Attacks on Multimodal Large Language Models
Aug 23, 2026Wenyun Li, Guiping Cao, Xiangyuan Lan +1Adversarial Attacks on Unified Multimodal Models Multimodal Jailbreak Attacks
Harbin Institute of Technology, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · Pazhou Laboratory (Huangpu), Guangzhou, China
On the Adversarial Robustness of Multimodal LLM Judges
Jun 14, 2026Zihan Wang, Guansong Pang, Zelin Liu +3LLM-as-a-Judge Adversarial Robustness
School of Computer Science and Engineering, Beihang University, Beijing, China · School of Computing and Information Systems, Singapore Management University, Singapore · State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing, China +1
Investigating Adversarial Robustness of Multi-modal Large Language Models
Jun 2, 2026Hashmat Shadab Malik, Muzammal Naseer, Salman KhanAdversarial Training VLM Robustness
Mohamed Bin Zayed University of AI, UAE · Khalifa University, UAE · Australian National University, Australia
Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models
May 19, 2026Tobias Braun, Jonas Henry Grebe, Hossein Shakibania +2Multimodal Foundation Models Backdoor Attacks
TU Darmstadt & hessian.AI, Germany
DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs
May 18, 2026Wenzhuo Xu, Zhipeng Wei, Zonghao Ying +4Multimodal Large Language Models LLM Safety
AI Security Lab, Beijing, China · International Computer Science Institute, CA, USA · UC Berkeley, CA, USA +1
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
VisInject: Disruption != Injection -- A Dual-Dimension Evaluation of Universal Adversarial Attacks on Vision-Language Models
May 2, 2026Pang Liu, Yingjie LaoVLM Evaluation VLM Robustness
Department of Electrical and Computer Engineering, Tufts University
Penny Wise, Pixel Foolish: Bypassing Price Constraints in Multimodal Agents via Visual Adversarial Perturbations
Apr 15, 2026Jiachen Qian, Zhaolu KangVLM Robustness Adversarial Attacks on VLMs
City University of Hong Kong · Peking University
A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning
Apr 5, 2026Tianle Chen, Deepti GhadiyaramAdversarial Attacks on Unified Multimodal Models Adversarial Attacks
Department of Computer Science, Boston University
CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment
Mar 31, 2026Anurag Kumar, Raghuveer Peri, Jon Burnsky +4MLLM Safety LLM Safety Alignment
The Ohio State University · AWS
Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey
Mar 30, 2026Bhavuk Jain, Sercan Ö. Arık, Hardeo K. ThakurAdversarial Attacks on Unified Multimodal Models LLM Security
Google · Bennett University, India
Unsafe by Reciprocity: How Generation-Understanding Coupling Undermines Safety in Unified Multimodal Models
Mar 28, 2026Kaishen Wang, Heng HuangMultimodal Robustness Unified Multimodal Models
University of Maryland, College Park, MD 20742, USA
Multi-Paradigm Collaborative Adversarial Attack Against Multi-Modal Large Language Models
Mar 5, 2026Yuanbo Li, Tianyang Xu, Cong Hu +3Adversarial Attacks on Unified Multimodal Models Transfer-Based Adversarial Attacks
School of Artificial Intelligence and Computer Science, Jiangnan University · Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey
MIDAS: Multi-Image Dispersion and Semantic Reconstruction for Jailbreaking MLLMs
Feb 28, 2026Yilian Liu, Xiaojun Jia, Guoshun Nan +6Multimodal Jailbreak Attacks Adversarial Attacks on Unified Multimodal Models
Beijing University of Posts and Telecommunications, China · Nanyang Technological University, Singapore · Guilin University of Electronic Technology, China
VALD: Multi-Stage Vision Attack Detection for Efficient LVLM Defense
Feb 23, 2026Nadav Kadvil, Malak Fares, Ayellet TalVLM Robustness Large Vision-Language Models
Technion – Israel Institute of Technology
PA-Attack: Guiding Gray-Box Attacks on LVLM Vision Encoders with Prototypes and Attention
Feb 23, 2026Hefei Mei, Zirui Wang, Chang Xu +2Attention-Based Adversarial Attacks Adversarial Attacks on VLMs
City University of Hong Kong · The University of Sydney

Oct 15, 2026J/K move · Enter open · S save

FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models

Jiaming Zhang, Zherui Li, Siqi Guo, Junhao Dong+2

College of Computing and Data Science Nanyang Technological University, Singapore

VLM Robustness Adversarial Attacks on VLMs VLM Evaluation Adversarial Attacks on Unified Multimodal Models Multimodal Learning

Open paper AskPDF arXiv

As vision-language models (VLMs) transition from frozen-encoder pipelines to native multimodal architectures, the visual channel increasingly serves as both a perceptual input and a carrier of rendered text that competes with user prompts. Current single-attack, pixel-centric evaluations fail to capture this complexity: they collapse distinct visual attack surfaces into a single scalar and ignore how architectural shifts alter vulnerability. We introduce PaRMA, an evidence-based auditing framework that defines pathway-conditioned risk over two primary image-level interventions: pixel perturbation and rendered-text insertion. Instead of arbitrary attack enumeration, PaRMA uses pathway-specific diagnostics to expose architecture-specific blind spots. Notably, we uncover a see-but-not-deceived signature where native VLMs attend heavily to typographic overlays but visually reject them, and we identify an objective mismatch between encoder-level and answer-level attacks in deep-fusion models. To exploit these findings, we instantiate FusionAudit, introducing two diagnostic-motivated attacks: MANTIS (margin-aware pixel perturbation) and Nat-Evid (host-congruent semantic insertion). Evaluating 18 open-weight VLMs across a novel nativeness taxonomy, we demonstrate that legacy single-attack rankings (e.g., PGD) are unstable for native models. Our diagnostic-driven attacks substantially raise the estimated residual risk, showing that PaRMA provides a falsifiable, evidence-driven auditing methodology for uncovering vulnerabilities missed by standard benchmarks.