# SAFE: Self-Attentive Function Embeddings for Binary Similarity

arXiv: https://arxiv.org/abs/1811.05296 · HTML: https://mlsanity.com/paper/1811.05296/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 30 of 33)

- [PalmTree: Learning an Assembly Language Model for Instruction Embedding](https://mlsanity.com/paper/2103.03809/) (2021)
- [CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision](https://mlsanity.com/paper/2402.16928/) (2024)
- [Backdoor Vulnerabilities in Normally Trained Deep Learning Models](https://mlsanity.com/paper/2211.15929/) (2022)
- [kTrans: Knowledge-Aware Transformer for Binary Code Embedding](https://mlsanity.com/paper/2308.12659/) (2023)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2502.10455/) (2025)
- [Semantic-aware Binary Code Representation with BERT](https://mlsanity.com/paper/2106.05478/) (2021)
- [Efficient Malware Analysis Using Metric Embeddings](https://mlsanity.com/paper/2212.02663/) (2022)
- [Is Function Similarity Over-Engineered? Building a Benchmark](https://mlsanity.com/paper/2410.22677/) (2024)
- [XFL: Naming Functions in Binaries with Extreme Multi-label Learning](https://mlsanity.com/paper/2107.13404/) (2021)
- [FASER: Binary Code Similarity Search through the use of Intermediate Representations](https://mlsanity.com/paper/2310.03605/) (2023)
- [FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods](https://mlsanity.com/paper/2208.14191/) (2022)
- [UniASM: Binary Code Similarity Detection without Fine-tuning](https://mlsanity.com/paper/2211.01144/) (2022)
- [BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding](https://mlsanity.com/paper/2409.07889/) (2024)
- [Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection](https://mlsanity.com/paper/2508.16625/) (2025)
- [Binary BPE: A Family of Cross-Platform Tokenizers for Binary Analysis](https://mlsanity.com/paper/2511.17573/) (2025)
- [VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity](https://mlsanity.com/paper/2312.00507/) (2023)
- [A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer](https://mlsanity.com/paper/2412.11177/) (2024)
- [A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis](https://mlsanity.com/paper/2512.14045/) (2025)
- [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)
- [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)
- [On the Role of Pre-trained Embeddings in Binary Code Analysis](https://mlsanity.com/paper/2502.08682/) (2025)
- [Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity](https://mlsanity.com/paper/2509.23449/) (2025)
- [Walma: Learning to See Memory Corruption in WebAssembly](https://mlsanity.com/paper/2603.24167/) (2026)
- [EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild](https://mlsanity.com/paper/2604.01554/) (2026)
- [Patch2Vuln: Agentic Reconstruction of Vulnerabilities from Linux Distribution Binary Patches](https://mlsanity.com/paper/2605.06601/) (2026)
- [Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers](https://mlsanity.com/paper/2605.18231/) (2026)
- [ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage](https://mlsanity.com/paper/2605.21615/) (2026)
- [SoK: AI-Augmented Binary Reversing](https://mlsanity.com/paper/2606.17398/) (2026)

## Abstract (arXiv)

The binary similarity problem consists in determining if two functions are similar by only considering their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc., and thus have an immediate practical impact. Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations. However, embeddings are usually derived from binary code using manual feature extraction, that may fail in considering important function characteristics, or may consider features that are not important for the binary similarity problem. In this paper we propose SAFE, a novel architecture for the embedding of functions based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions (i.e., it does not incur in the computational overhead of building or manipulating control flow graphs), and is more general as it works on stripped binaries and on multiple architectures. We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions. Furthermore, we show how clusters of our embedding vectors are closely related to the semantic of the implemented algorithms, paving the way for further interesting applications (e.g. semantic-based binary function search).