Abstract: LVLMs(Large Vision-Language Models) have demonstrated strong capabilities in multimodal understanding,reasoning,and generation.However,the incorporation of visual inputs has also introduced new security risks characterized by cross-modal vulnerabilities,increased attack stealthiness,and more complex pathways of risk propagation.These issues have made LVLMs security an increasingly important research topic in multimodal artificial intelligence.Although existing studies have extensively investigated attack construction,security evaluation,and defense mechanisms in the context of LVLMs,they differ considerably in threat models,task settings,evaluation metrics,and methodological choices.A systematic understanding of attack types,defense mechanisms,and their scope of applicability remains insufficient.This paper reviews LVLMs security research from both attack and defense perspectives.Firstly,it outlines a basic analytical framework for LVLMs security that includes model architectures,data resources,and evaluation metrics.Secondly,from the attack perspective,existing methods are categorized into adversarial attacks,jailbreak attacks,prompt injection attacks,and backdoor attacks.These methods are further compared in terms of attack objectives,threat models,implementation mechanisms,and application contexts.On the defense side,existing defense strategies are examined across the training and inference stages,with attention to their mechanisms,applicable conditions,and limitations.Finally,the paper discusses key challenges and future directions in LVLMs security,providing guidance for the design of more robust attack and defense methods,more reliable security evaluation systems,and trustworthy multimodal systems.

Key words: Large vision-language models, Large model security, Adversarial attack, Jailbreak attack, Prompt injection attack, Backdoor attack, Defense