Omar Abusabha | Searcharxiv

arXiv2026

The Evolution of Binary Decompilation in the Modern Era: A Taxonomy, Literature Review, and Future Perspectives

Decompilation has become a foundational technique in software engineering and security analysis, and it is now advancing through the integration of modern machine learning (ML) approaches. This article presents a systematic review of decompilation studies published over the past decades and develops a comprehensive taxonomy of methodologies employed in contemporary research. We further examine trends in evaluation metrics, tools, and benchmarks used to assess state-of-the-art approaches. Our review reveals key challenges, such as the lack of reliable ground truth and the absence of standardized benchmarks, which hinder rigorous comparison. Finally, we outline future research directions.

cs.SE↗

arXiv2025

A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis

A function inlining optimization is a widely used transformation in modern compilers, which replaces a call site with the callee's body in need. While this transformation improves performance, it significantly impacts static features such as machine instructions and control flow graphs, which are crucial to binary analysis. Yet, despite its broad impact, the security impact of function inlining remains underexplored to date. In this paper, we present the first comprehensive study of function inlining through the lens of machine learning-based binary analysis. To this end, we dissect the inlining decision pipeline within the LLVM's cost model and explore the combinations of the compiler options that aggressively promote the function inlining ratio beyond standard optimization levels, which we term extreme inlining. We focus on five ML-assisted binary analysis tasks for security, using 20 unique models to systematically evaluate their robustness under extreme inlining scenarios. Our extensive experiments reveal several significant findings: i) function inlining, though a benign transformation in intent, can (in)directly affect ML model behaviors, being potentially exploited by evading discriminative or generative ML models; ii) ML models relying on static features can be highly sensitive to inlining; iii) subtle compiler settings can be leveraged to deliberately craft evasive binary variants; and iv) inlining ratios vary substantially across applications and build configurations, undermining assumptions of consistency in training and evaluation of ML models.

cs.CR↗