Low-Rank Adaptation (LoRA) has become a common way to fine-tune Large Language Models (LLMs) since it is quick and easy to move about. However, the fact that LoRA modules are lightweight and may be shared makes them more vulnerable to security threats, such as backdoor injection and malicious sharing through open-source repositories. This paper proposes a multi-layer security mechanism to protect LoRA-based fine-tuning from backdoors and hostile threats. This paper uses (i) signature verification, safetensors adoption, and sandboxing to make the supply chain stronger; (ii) matrix norms, eigenvalue spectra, and target-layer inspection to analyze static weight; (iii) automated red-teaming and trigger sweeping to evaluate dynamic weight; and (iv) runtime adapters, policy filters, and continuous monitoring to make deployment safe. This paper also puts these protections into a Continuous Integration/Continuous Deployment (CI/CD) pipeline so that they may be used in the real world. Initial tests on LLaMA-2-7B show that the framework can find backdoored LoRA modules without affecting the performance of benign ones. This study underscores the critical necessity for a secure LoRA ecosystem and offers practical techniques for reliable LLM adaptation.