# A Survey of Binary Code Similarity

arXiv: https://arxiv.org/abs/1909.11424 · HTML: https://mlsanity.com/paper/1909.11424/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection](https://mlsanity.com/paper/2106.08890/) (2021)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations](https://mlsanity.com/paper/2402.02167/) (2024)
- [Semantic-aware Binary Code Representation with BERT](https://mlsanity.com/paper/2106.05478/) (2021)
- [Pre-Training Representations of Binary Code Using Contrastive Learning](https://mlsanity.com/paper/2210.05102/) (2022)
- [Deep-Learning-based Vulnerability Detection in Binary Executables](https://mlsanity.com/paper/2212.01254/) (2022)
- [EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis](https://mlsanity.com/paper/2310.01835/) (2023)
- [Predicting Patch Correctness Based on the Similarity of Failing Test Cases](https://mlsanity.com/paper/2107.13296/) (2021)
- [FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods](https://mlsanity.com/paper/2208.14191/) (2022)
- [VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity](https://mlsanity.com/paper/2312.00507/) (2023)
- [MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code](https://mlsanity.com/paper/2408.01354/) (2024)
- [Fool Me If You Can: On the Robustness of Binary Code Similarity Detection Models against Semantics-preserving Transformations](https://mlsanity.com/paper/2602.12681/) (2026)
- [Binary Diffing as a Network Alignment Problem via Belief Propagation](https://mlsanity.com/paper/2112.15337/) (2021)
- [SimCLF: A Simple Contrastive Learning Framework for Function-level Binary Embeddings](https://mlsanity.com/paper/2209.02442/) (2022)
- [Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck](https://mlsanity.com/paper/2307.10631/) (2023)
- [A Unified Evaluation of Learning-Based Similarity Techniques for Malware Detection](https://mlsanity.com/paper/2602.15376/) (2026)
- [EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild](https://mlsanity.com/paper/2604.01554/) (2026)
- [SoK: AI-Augmented Binary Reversing](https://mlsanity.com/paper/2606.17398/) (2026)
- [Instruction Alignment for Binary Code Representation Learning](https://mlsanity.com/paper/2608.11766/) (2026)

## Abstract (arXiv)

Binary code similarity approaches compare two or more pieces of binary code to identify their similarities and differences. The ability to compare binary code enables many real-world applications on scenarios where source code may not be available such as patch analysis, bug search, and malware detection and analysis. Over the past 20 years numerous binary code similarity approaches have been proposed, but the research area has not yet been systematically analyzed. This paper presents a first survey of binary code similarity. It analyzes 61 binary code similarity approaches, which are systematized on four aspects: (1) the applications they enable, (2) their approach characteristics, (3) how the approaches are implemented, and (4) the benchmarks and methodologies used to evaluate them. In addition, the survey discusses the scope and origins of the area, its evolution over the past two decades, and the challenges that lie ahead.