Multimodal Jailbreak Attacks

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2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs
Sep 30, 2026Wenyu Chen, Li Wang, Chuanchao Zang +5VLM Robustness Jailbreak Attacks
Shandong University
The Price of Consistency: Exploiting Visual Anchors for Multimodal Jailbreaking in Video Generation
Sep 7, 2026Peng Li, Qianqian Xu, Yangbangyan Jiang +2Image-to-Video Generation Multimodal Jailbreak Attacks
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China · State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence and Robotics, Hunan University, Changsha, China +1
Jailbreaking Text-to-Image Models Through Cracks: Navigating Heterogeneous Safety Filters via Multi-Agent Debate
Sep 1, 2026Kaiyan Wen, Shijie Zhang, Lu Yu +1Adversarial Attacks on VLMs Multi-Agent Debate
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China · Shenzhen MSU-BIT University, Shenzhen, China · City University of Hong Kong, Hong Kong, China
Fully Unleashing the Multimodal Attacker: Meta-Adaptive Jailbreaking of Vision-Language Models
Aug 27, 2026Benlei Cui, Shen Pang, Yuke Wang +7VLM Robustness Adversarial Attacks on VLMs
1Yuvion Team, Alibaba Group · Laboratory for Statistical Monitoring and Intelligent Governance of Common Prosperity, School of Statistics and Data Science, Zhejiang Gongshang University
Mind the Gap: Zero-Query Jailbreaks via Filter-Generator Discrepancy in Text-to-Image Systems
Aug 2, 2026Wanguang Li, Zhaoxin Wang, Handing WangAdversarial Prompt Generation T2I Generation
School of Artificial Intelligence, Xidian University, Shaanxi, China
PixJail: Self-Evolving Paper-to-Pipeline Reproduction for Text-to-Image Jailbreak Evaluation
Jun 23, 2026Leyi Sheng, Han Sun, Zhen Sun +4AI Safety Evaluation Multimodal Jailbreak Attacks
1The Hong Kong University of Technology and Science (Guangzhou) · 2East China Normal University · 3Shanghai Qi Zhi Institute +4
Jailbreaking Multimodal Large Language Models using Multi-Clip Video
Jun 1, 2026Choongwon Kang, Seungjong Sun, Hyunmin Jun +1Multimodal Robustness Video-Language Models
Department of Applied Artificial Intelligence, Sungkyunkwan University · Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University
When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?
May 27, 2026Yuan Tian, Bing Hu, Fang Wu +3VLM Robustness Tool Use in VLMs
Independent Researcher · Stanford University · Harvard University +2
PAST2HARM: A Simple Adaptive Past Tense Attack for Jailbreaking Multimodal AI
May 26, 2026Snehasis MukhopadhyayAdversarial Prompt Generation Multimodal Jailbreak Attacks
Indian Institute of Information Technology, Kalyani
Same Model, Different Weakness: How Language and Modality Reshape the Jailbreak Attack Surface in Frontier MLLMs
May 22, 2026Casey Ford, Madison Van Doren, Sicheng Jin +1Multilingual Language Model Evaluation Adversarial Attacks on VLMs
Appen
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
OrchJail: Jailbreaking Tool-Calling Text-to-Image Agents by Orchestration-Guided Fuzzing
May 8, 2026Jianming Chen, Yawen Wang, Junjie Wang +3Adversarial Prompt Generation T2I Generation
Institute of Software, Chinese Academy of Sciences, Beijing, China · Science & Technology on Integrated Information System Laboratory, Beijing, China · State Key Laboratory of Complex System Modeling and Simulation Technology, Beijing, China +1
Conceal, Reconstruct, Jailbreak: Exploiting the Reconstruction-Concealment Tradeoff in MLLMs
May 7, 2026Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1Multimodal Large Language Models Jailbreak Attacks
1NC State University · 2Zhejiang University
On Optimizing Multimodal Jailbreaks for Spoken Language Models
Mar 19, 2026Aravind Krishnan, Karolina Stańczak, Dietrich KlakowAdversarial Attacks LLM Jailbreak Attacks
Saarland University, Germany · DFKI GmbH, Saarbrücken, Germany · ETH AI Center, Switzerland
When the Prompt Becomes Visual: Vision-Centric Jailbreak Attacks for Large Image Editing Models
Feb 10, 2026Jiacheng Hou, Yining Sun, Ruochong Jin +4Jailbreak Attacks Multimodal Jailbreak Attacks
Tsinghua University, China · Pengcheng Laboratory, Shenzhen, China · Central South University, Changsha, China.
Robustness of Vision Language Models Against Split-Image Harmful Input Attacks
Feb 8, 2026Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu +1VLM Robustness Adversarial Attacks on VLMs
Pennsylvania State University · Bangladesh University of Engineering and Technology
Divide and Conquer: A Hybrid Strategy Defeats Multimodal Large Language Models
Dec 21, 2024Yanxu Mao, Peipei Liu, Tiehan Cui +3Adversarial Attacks on LLMs Multimodal Large Language Models
School of Computer Science and Technology, Shanghai Jiao Tong University, Shanghai, China · Zhongguancun Laboratory, Beijing, China · School of Software, Henan University, Kaifeng, China +1

Sep 30, 2026, cs.CRJ/K move · Enter open · S save

SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs

Wenyu Chen, Li Wang, Chuanchao Zang, Xiangtao Meng+4

Shandong University

VLM Robustness Jailbreak Attacks Multimodal Jailbreak Attacks

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Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit policy-violating responses. Existing video jailbreaks primarily manipulate how harmful queries are visually presented, thereby treating video merely as a carrier. Consequently, the surrounding video scenario remains unexplored as a contextual attack surface. In this paper, we show that the same harmful query can elicit different safety responses when placed in different video scenarios. To systematically exploit this vulnerability, we propose SceneJail, an adaptive black-box jailbreak framework with two coordinated components. Adaptive Scenario Construction dynamically searches for a surrounding scenario that is contextually compatible with the harmful query. Scenario-aware Prompt Search uses black-box response feedback to search for textual guidance tailored to the selected scenario. Extensive evaluations on the HADES and SafeBench datasets across eight Video-MLLMs, including two proprietary models, GPT-4.1 and Gemini3.5-Flash, demonstrate the effectiveness of SceneJail. SceneJail-F, which presents the complete query persistently, achieves average attack success rates (ASR) up to 91.5%, outperforming the strongest baselines by 29.1 percentage points. Furthermore, SceneJail-S, which distributes the query across successive frames, remains highly robust against current defenses, retaining a 72.3% ASR even under strict image filtering.

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