H. Dursunoglu

This article presents SEMA-GUARD, a framework that uses semantic analysis and graph neural networks to identify flaws in assembly code, and results imply that including semantic information in graph-based models may be a successful method for identifying vulnerabilities in compiled code.

External evaluation shows that while citation validity remains strong, evidence utilization, span alignment, and refusal calibration become harder under domain shift, indicating that trustworthy RAG systems require explicit validation between retrieval and final answer delivery.