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

arXiv: https://arxiv.org/abs/2512.14045 · HTML: https://mlsanity.com/paper/2512.14045/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 104 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection](https://mlsanity.com/paper/1708.06525/) (2017)
- [jTrans: Jump-Aware Transformer for Binary Code Similarity](https://mlsanity.com/paper/2205.12713/) (2022)
- [Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity](https://mlsanity.com/paper/2012.08680/) (2020)
- [SAFE: Self-Attentive Function Embeddings for Binary Similarity](https://mlsanity.com/paper/1811.05296/) (2018)
- [Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs](https://mlsanity.com/paper/1808.04706/) (2018)
- [Augmenting Decompiler Output with Learned Variable Names and Types](https://mlsanity.com/paper/2108.06363/) (2021)
- [XDA: Accurate, Robust Disassembly with Transfer Learning](https://mlsanity.com/paper/2010.00770/) (2020)
- [Towards Neural Decompilation](https://mlsanity.com/paper/1905.08325/) (2019)
- [Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned](https://mlsanity.com/paper/2011.10749/) (2020)
- [FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis](https://mlsanity.com/paper/2308.05362/) (2023)
- [Learning to Find Usages of Library Functions in Optimized Binaries](https://mlsanity.com/paper/2103.05221/) (2021)
- [Unleashing the Hidden Power of Compiler Optimization on Binary Code Difference: An Empirical Study](https://mlsanity.com/paper/2103.12357/) (2021)
- [Cross-Inlining Binary Function Similarity Detection](https://mlsanity.com/paper/2401.05739/) (2024)
- [1-to-1 or 1-to-n? Investigating the effect of function inlining on binary similarity analysis](https://mlsanity.com/paper/2112.12928/) (2021)
- [The Inconvenient Truths of Ground Truth for Binary Analysis](https://mlsanity.com/paper/2210.15079/) (2022)

## Built on by (most cited first, 1 of 1)

- [SoK: AI-Augmented Binary Reversing](https://mlsanity.com/paper/2606.17398/) (2026)

## Abstract (arXiv)

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.