In recent years, the proliferation of software vul- nerabilities has significantly increased the complexities and costs associated with manual remediation efforts. Although AI-based methods for automated vulnerability repair are gaining traction, many existing approaches have two limitations: 1) treat code as a sequence of tokens, neglecting critical structural information like control flow and data flow, and 2) do not fully utilize the repair patterns of vulnerabilities. To address these limitations, we introduce FAVOR, an innovative tool that utilizes both the vulnerable function’s code and its control flow graph (CFG) as inputs. FAVOR incorporates a dependency embedding module to capture structural and dependency information and leverages CodeT5, a state-of-the-art model pre-trained for code generation tasks. To further enhance the repair process, we introduce a pattern store that uses KNN search to retrieve similar past repair patterns, which helps guide the model toward generating more contextually accurate patches. In our experiments, FAVOR, trained on a dataset of 6548 faulty C/C++ functions, repaired 45 more vulnerabilities compared to VULREPAIR, demonstrating improved accuracy and efficiency in automated vulnerability repair.