Shengli Liu

SA2Pat: enhancing binary security patch function localization via security advisory-guided LLMs

SemASTer: Cross-Architecture Binary Code Similarity Detection via AST-Driven Semantic Compensation

Binary Code Similarity Detection (BCSD) plays a crucial role in vulnerability discovery and malware analysis. However, it faces two major challenges in cross-architecture scenarios: syntactic variations arising from architectural differences and semantic distortions induced by compiler optimizations. To address these issues, we propose SemASTer, a robust framework for cross-architecture binary code similarity detection that leverages Abstract Syntax Tree (AST) as its semantic backbone. A degradation-aware channel attention mechanism dynamically incorporates compensatory information only when the AST backbone is deemed unreliable due to aggressive optimizations. Furthermore, we introduce two complementary compensation pathways: behavioral semantic compensation to recover lost runtime dynamics, and control-flow compensation to reconstruct disrupted logical dependencies. Experimental results demonstrate that SemASTer achieves an 8.3% improvement in Recall@1 over the current state-of-the-art method in cross-architecture tasks, and an 11.4% gain under fully heterogeneous compilation settings (varying architectures, compilers, and optimization levels). Moreover, its effectiveness and robustness are validated through real-world vulnerability detection scenarios.