SECURITY THREATS AND DEFENSE SYSTEMS ARCHITECTURES FOR MULTIMODAL LARGE LANGUAGE MODEL

Against the backdrop of the technological development of multimodal models, this book first introduces its core architecture and typical application scenarios, building upon this foundation to construct a security threat analysis framework covering the entire life cycle. It then delves into typical attack methods faced during the training and inference phases, such as data poisoning, backdoor attacks, cross-modal adversarial examples, and prompt injection, further proposing systematic defense strategies and engineering implementation methods covering all stages of model training, inference, and deployment. Finally, in conjunction with cutting-edge technological developments, it provides an outlook on the future trends and challenges of multimodal model security.

This book has a broad readership, suitable for researchers engaged in large-scale model and artificial intelligence security research, as well as engineering and technical personnel involved in the development, deployment, and operation of multimodal intelligent systems. It is also suitable for readers interested in large-scale model security, trustworthy artificial intelligence, and governance issues, providing them with a systematic knowledge framework and practical reference.

Part I MLLMs: Technology and Security Overview

Chapter 1 Introduction to MLLMs: Technology and Security

1.1 Overview of MLLM Technology

1.1.1 From Unimodal LLMs to MLLMs: Capability and Architecture Shifts

1.1.2 Comparative Analysis of Mainstream Multimodal Model Structures

1.1.3 Typical Application Scenarios of Multimodal Models

1.2 Overview of the Life Cycle and Attack Surface of MLLMs

1.2.1 Overview of the Life Cycle of MLLMs

1.2.2 Distribution of Potential Security Risks at Each Stage of the Model

1.2.3 Analysis of New Risk Types Specific to Multimodality

1.3 Chapter Summary

References

Part II Overview of Threat Models and Attack Techniques for Multimodal Models

Chapter 2 Overview of Threat Models and Attack Techniques for Multimodal Models

2.1 Data and Training Phase Attack Techniques

2.1.1 Modality-Aware Data Poisoning Attacks

2.1.2 Cross-Modal Backdoor Attack Mechanism

2.1.3 Model Inversion and Membership Inference Attacks

2.1.4 Modality Imbalance Attacks

2.2 Attack Techniques in the Reasoning and Fusion Phase

2.2.1 Image-Text Joint Adversarial Sample Attacks

2.2.2 Cross-Modal Hallucination Attacks

2.2.3 Modal Injection Attacks

2.3 Risks of Abuse and External Attacks

2.3.1 Risks of Abuse

2.3.2 External Attacks

2.4 Chapter Summary

References

Part III Overview of Defense Strategies for MLLMs

Chapter 3 Introduction to MLLMs Defense Strategies

3.1 Multimodal Adversarial Defense Strategies

3.1.1 Multimodal Adversarial Training Strategy

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