SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach

2024-11-17

arXiv.org

AI Summary

Multimodal foundation models (MFMs) face unique security and reliability challenges arising from heterogeneous modality fusion, particularly cross-modal risks such as modality misalignment. Method: We propose the first information-theoretic threat taxonomy—grounded in channel capacity, noise, and bandwidth—to systematically characterize such risks. Through a systematic mapping of threats (SoK), multimodal alignment behavior analysis, and defense mechanism alignment, we identify structural gaps in existing alignment protection strategies. We further develop an extensible, quantifiable security evaluation paradigm. Contribution/Results: Our work bridges dual gaps in MFM robustness research: (i) theoretical modeling grounded in information theory and (ii) empirical, metrics-driven assessment. The framework provides both a rigorous theoretical foundation and actionable guidelines for secure, trustworthy MFM deployment.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence, combining diverse data modalities to enhance learning and understanding across a wide range of applications. However, this integration also brings unique safety and security challenges. In this paper, we conceptualize cybersafety and cybersecurity in the context of multimodal learning and present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats to these models. We propose a taxonomy framework grounded in information theory, evaluating and categorizing threats through the concepts of channel capacity, signal, noise, and bandwidth. This approach provides a novel framework that unifies model safety and system security in MFMs, offering a more comprehensive and actionable understanding of the risks involved. We used this to explore existing defense mechanisms, and identified gaps in current research - particularly, a lack of protection for alignment between modalities and a need for more systematic defense methods. Our work contributes to a deeper understanding of the security and safety landscape in MFMs, providing researchers and practitioners with valuable insights for improving the robustness and reliability of these models.

Problem

• Identify safety and security threats in multimodal foundation models
• Analyze information flow vulnerabilities across diverse data modalities
• Develop game-theoretic defenses for cross-modal alignment gaps

Innovation

• Information theory-based risk evaluation
• Game-theoretic defense mechanisms
• Cross-modal alignment protection