A comprehensive survey on large language models for multimedia data security: challenges and solutions

The rapid expansion of IoT applications utilizes multimedia data integrated with Large Language Models (LLMs) for interpreting digital information by leveraging the capabilities of artificial intelligence (AI) driven neural network systems. These models are extensively used as generative AI tools for data augmentation but data security and privacy remain a fundamental concern associated with LLM model in the digital domain. Traditional security approach shows potential challenges in addressing emerging threats such as adversarial attacks, data poisoning, or privacy breaches, especially in dynamic and resource-constrained IoT environments. Such malicious attacks target the LLM model during the learning and evaluation phase to exploit the vulnerabilities for unauthorized access. The proposed study conducts a comprehensive survey of the transformative potential of LLM models for securing multimedia data offering analysis of their capabilities, challenges, and solutions. The proposed study explores potential security threats and remedies for each type of multimedia data and investigates the various traditional and emerging data protection schemes. The study systematically classifies emerging attacks on LLM models during training and testing phases which include membership attacks, adversarial perturbations, prompt injection, etc. The study also investigates the various robust defense mechanism such as adversarial training, regularization, encryption, etc. The study evaluates the efficiency of potential LLM models such as generative LLM, transformer-based, and other multimodal systems in securing image, text, and video multimedia data highlighting their adaptability and scalability. The proposed survey compares state-of-the-art solutions and underscores the efficiency of LLM-driven mechanisms over traditional approaches in mitigating emerging attacks such as zero-day threats on multimedia data. It ensures real-time compliance with standard regulations like GDPR (General Data Protection Regulation). The proposed work identifies some open challenges including privacy-preserving LLM deployment, black-box interpretability, personalized LLM privacy risk, and cross-model security integration. It also highlights some robust future solutions such as lightweight LLM design and hybrid security frameworks. The proposed work bridges critical research gaps by providing insights into LLM-based emerging techniques to safeguard sensitive data in IoT-based real-world applications.