Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

TL;DR

This survey systematically analyzes adversarial threats to multimodal large language models, offering a taxonomy and vulnerability analysis to guide the development of more robust systems.

Contribution

It introduces a comprehensive taxonomy and vulnerability-centric framework to understand and categorize adversarial attacks on MLLMs.

Findings

Unified attack surface taxonomy across modalities

Linking vulnerabilities to architectural weaknesses

Guidance for developing robust multimodal models

Abstract

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.