A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis
Published:Dec 16, 2025 03:21
•1 min read
•ArXiv
Analysis
This article likely explores the impact of function inlining, a compiler optimization technique, on the effectiveness and security of machine learning models used for binary analysis. It probably discusses how inlining can alter the structure of code, potentially making it harder for ML models to accurately identify vulnerabilities or malicious behavior. The research likely aims to understand and mitigate these challenges.
Key Takeaways
Reference
“The article likely contains technical details about function inlining and its effects on binary code, along with explanations of how ML models are used in binary analysis and how they might be affected by inlining.”