Large language model (LLM)-enabled edge networks (LLMENs) offer mobile users high-quality and low-latency AI-generated content services in the 6G era. However, unlike typical edge networks, LLMENs present unique security challenges due to the inherent complexity of LLMs, their high computational overhead, and continuous interactions with users. Specifically, both frequent user interactions (i.e., queries and responses) over wireless channels and potential electromagnetic information leakage from intensive LLM computations make LLMENs susceptible to various security threats, such as eavesdropping, jamming, prompt poisoning, and prompt injection attacks. Since existing countermeasures against these attacks often incur prohibitive overhead, developing holistic, efficient, and secure privacy protections for LLMENs is crucial. This article first reviews the vulnerabilities of LLMENs, outlines various attacks, and analyzes the drawbacks of existing countermeasures. To overcome these limitations, we propose a covert communications (CC) and computations approach to enhance both the overall security and efficiency of LLMENs. Furthermore, various supplementary solutions are developed to improve the covertness of this framework. Finally, our approach is further evaluated through a case study where the total latency is minimized under stringent communication and computational security requirements. Numerical results demonstrate the proposed approach's effectiveness in enhancing both privacy protection and the execution efficiency of LLM tasks.

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