The growing complexity and interconnectivity of software supply chains have elevated the risks of security threats, demanding innovative solutions. This thesis investigates the fine-tuning of Large Language Models (LLMs), particularly Microsoft Phi-2, to enhance their ability to identify and mitigate software supply chain vulnerabilities. Using advanced techniques such as Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA), the Phi-2 model was trained on a domain-specific dataset comprising incident reports, threat intelligence data, and best practices. 

The methodology encompasses a rigorous evaluation process using quantitative metrics, including ROUGE, BERTScore, and BLEURT, supplemented by qualitative insights derived from semi-structured interviews with cybersecurity experts. The in[1]terviews revealed valuable perspectives on the practical applicability of the fine-tuned model in addressing real-world threats such as compromised third-party components, open-source dependency vulnerabilities, and emerging attack patterns. 

The fine-tuned model exhibited significant improvements in generating contex[1]tually relevant, precise, and actionable threat mitigation strategies compared to its baseline. The findings demonstrate that domain-specific fine-tuning of LLMs is a vi[1]able approach for advancing automated threat detection and response capabilities in software supply chains. This research provides a robust framework for integrating AI[1]driven solutions into the software development lifecycle, contributing to the fields of software engineering and cybersecurity by improving resilience against supply chain attacks.