Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

📄 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.

🔍 Key Points

Introduces a comprehensive taxonomy for categorizing adversarial attacks on multimodal large language models (MLLMs) based on attacker objectives and vulnerabilities, providing a unified framework that transcends existing modality-specific classifications.
Presents a vulnerability-centric analysis that links architecture and representation weaknesses of MLLMs to various attack types, enhancing the understanding of why MLLMs are susceptible to adversarial manipulations.
Explores multiple families of attacks including integrity, safety and jailbreak, control and injection, and poisoning and backdoor attacks, detailing the principles and methodologies behind each type.
Discusses various defense mechanisms for enhancing robustness in MLLMs against identified adversarial threats, thus informing the design of safer multimodal AI systems.
Emphasizes the impact of cross-modal interactions and architectural choices that amplify the vulnerabilities within MLLMs, guiding future research directions in AI security.

💡 Why This Paper Matters

This survey is crucial as it systematically tackles the emerging threats posed to multimodal large language models by outlining not only the attacks but also the fundamental vulnerabilities they exploit. This understanding is vital as MLLMs become integral to AI applications, including sensitive areas such as healthcare and security. The findings underscore the need for improved defense mechanisms and careful model design for robust AI deployments.

🎯 Why It's Interesting for AI Security Researchers

The paper offers substantial insights into the security landscape of multimodal AI systems, making it significant for AI security researchers. By detailing specific vulnerabilities and categorizing attack types, it provides foundational knowledge for developing countermeasures and informing safe practices in deploying MLLMs, which are expected to play a pivotal role in future AI applications.

Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

📄 Abstract

🔍 Key Points

💡 Why This Paper Matters

🎯 Why It's Interesting for AI Security Researchers

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