Software vulnerabilities remain pervasive, even with the rise of AI-powered code assistants, advanced static analysis tools, and comprehensive testing frameworks. It’s clear that we must move beyond merely preventing these bugs; we need to eliminate them swiftly and efficiently. However, manual code intervention is slow, expensive, and can often introduce new security flaws, especially in legacy codebases. The advent of highly advanced Large Language Models (LLMs) presents a significant opportunity for automated software defect patching. We introduce LLM4CVE, an LLM-based iterative pipeline designed for robust and accurate repair of vulnerable functions in real-world code. We evaluate our pipeline using State-of-the-Art LLMs, including GPT-3.5, GPT-4o, Llama 3 8B, and Llama 3 70B. Our results demonstrate a human-verified quality score of 8.51/10 and a 20% increase in ground-truth code similarity with Llama 3 70B. To foster further research in LLM-based vulnerability repair, we release our evaluation framework, fine-tuned model weights, and experimental results on our website: https://sites.google.com/view/llm4cve