{"ID":2863989,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25514","arxiv_id":"2509.25514","title":"AGNOMIN -- Architecture Agnostic Multi-Label Function Name Prediction","abstract":"Function name prediction is crucial for understanding stripped binaries in software reverse engineering, a key step for \\textbf{enabling subsequent vulnerability analysis and patching}. However, existing approaches often struggle with architecture-specific limitations, data scarcity, and diverse naming conventions. We present AGNOMIN, a novel architecture-agnostic approach for multi-label function name prediction in stripped binaries. AGNOMIN builds Feature-Enriched Hierarchical Graphs (FEHGs), combining Control Flow Graphs, Function Call Graphs, and dynamically learned \\texttt{PCode} features. A hierarchical graph neural network processes this enriched structure to generate consistent function representations across architectures, vital for \\textbf{scalable security assessments}. For function name prediction, AGNOMIN employs a Renée-inspired decoder, enhanced with an attention-based head layer and algorithmic improvements. We evaluate AGNOMIN on a comprehensive dataset of 9,000 ELF executable binaries across three architectures, demonstrating its superior performance compared to state-of-the-art approaches, with improvements of up to 27.17\\% in precision and 55.86\\% in recall across the testing dataset. Moreover, AGNOMIN generalizes well to unseen architectures, achieving 5.89\\% higher recall than the closest baseline. AGNOMIN's practical utility has been validated through security hackathons, where it successfully aided reverse engineers in analyzing and patching vulnerable binaries across different architectures.","short_abstract":"Function name prediction is crucial for understanding stripped binaries in software reverse engineering, a key step for \\textbf{enabling subsequent vulnerability analysis and patching}. However, existing approaches often struggle with architecture-specific limitations, data scarcity, and diverse naming conventions. We...","url_abs":"https://arxiv.org/abs/2509.25514","url_pdf":"https://arxiv.org/pdf/2509.25514v2","authors":"[\"Yonatan Gizachew Achamyeleh\",\"Tongtao Zhang\",\"Joshua Hyunki Kim\",\"Gabriel Garcia\",\"Shih-Yuan Yu\",\"Anton Kocheturov\",\"Mohammad Abdullah Al Faruque\"]","published":"2025-09-29T21:18:18Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.CR\",\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false}