Exploring Privacy and Security Risks in LLMs: Data Leakage, Prompt Injection, and Membership Inference

In this paper, we present an analysis of the dual role of Large Language Models (LLMs) in the context of multimedia security, examining how these models can both strengthen defensive capabilities and introduce new vulnerabilities within modern content ecosystems. As LLMs increasingly interface with multimedia workflows-ranging from text-image generation pipelines to cross-modal retrieval and content moderation-their susceptibility to attacks such as data leakage, prompt injection, and jailbreaks raises critical concerns for the integrity and trustworthiness of multimedia platforms. We focus our analysis on privacy-oriented threats, including model inversion and membership inference, and discuss their implications for systems that process or generate multimedia content. Building on this risk landscape, we outline principles for improving the robustness and resilience of LLM-driven multimedia applications, highlighting strategies suited to in-the-wild threat scenarios. Indeed, we explore the constructive application of LLMs within cybersecurity frameworks, such as the Cyber Kill Chain, demonstrating how these models can be leveraged for threat detection, risk assessment, and automated defensive operations.