Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

Computer Science > Cryptography and Security [Submitted on 30 Mar 2026] Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey Bhavuk Jain, Sercan Ö. Arık, Hardeo K. Thakur Multimodal large language models (MLLMs) integrate information from multiple modalities such as text, images, audio, and video, enabling complex capabilities such as visual question answering and audio translation. While powerful, this increased expressiveness introduces new and amplified vulnerabilities to adversarial manipulation. This survey provides a comprehensive and systematic analysis of adversarial threats to MLLMs, moving beyond enumerating attack techniques to explain the underlying causes of model susceptibility. We introduce a taxonomy that organizes adversarial attacks according to attacker objectives, unifying diverse attack surfaces across modalities and deployment settings. Additionally, we also present a vulnerability-centric analysis that links integrity attacks, safety and jailbreak failures, control and instruction hijacking, and training-time poisoning to shared architectural and representational weaknesses in multimodal systems. Together, this framework provides an explanatory foundation for understanding adversarial behavior in MLLMs and informs the development of more robust and secure multimodal language systems. Comments: Survey paper, 37 pages, 10 figures, accepted at TMLR Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.27918 [cs.CR]   (or arXiv:2603.27918v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.27918 Focus to learn more Journal reference: Transactions on Machine Learning Research, 2026 Submission history From: Bhavuk Jain [view email] [v1] Mon, 30 Mar 2026 00:16:31 UTC (4,951 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)