# FASER: Binary Code Similarity Search through the use of Intermediate Representations

arXiv: https://arxiv.org/abs/2310.03605 · HTML: https://mlsanity.com/paper/2310.03605/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection](https://mlsanity.com/paper/1708.06525/) (2017)
- [PalmTree: Learning an Assembly Language Model for Instruction Embedding](https://mlsanity.com/paper/2103.03809/) (2021)
- [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)
- [Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned](https://mlsanity.com/paper/2011.10749/) (2020)
- [Cross-Language Binary-Source Code Matching with Intermediate Representations](https://mlsanity.com/paper/2201.07420/) (2022)
- [Asteria-Pro: Enhancing Deep-Learning Based Binary Code Similarity Detection by Incorporating Domain Knowledge](https://mlsanity.com/paper/2301.00511/) (2023)

## Built on by (most cited first, 4 of 4)

- [Is Function Similarity Over-Engineered? Building a Benchmark](https://mlsanity.com/paper/2410.22677/) (2024)
- [VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity](https://mlsanity.com/paper/2312.00507/) (2023)
- [Can Large Language Models Understand Intermediate Representations in Compilers?](https://mlsanity.com/paper/2502.06854/) (2025)
- [Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets](https://mlsanity.com/paper/2406.02606/) (2024)

## Abstract (arXiv)

Being able to identify functions of interest in cross-architecture software is useful whether you are analysing for malware, securing the software supply chain or conducting vulnerability research. Cross-Architecture Binary Code Similarity Search has been explored in numerous studies and has used a wide range of different data sources to achieve its goals. The data sources typically used draw on common structures derived from binaries such as function control flow graphs or binary level call graphs, the output of the disassembly process or the outputs of a dynamic analysis approach. One data source which has received less attention is binary intermediate representations. Binary Intermediate representations possess two interesting properties: they are cross architecture by their very nature and encode the semantics of a function explicitly to support downstream usage. Within this paper we propose Function as a String Encoded Representation (FASER) which combines long document transformers with the use of intermediate representations to create a model capable of cross architecture function search without the need for manual feature engineering, pre-training or a dynamic analysis step. We compare our approach against a series of baseline approaches for two tasks; A general function search task and a targeted vulnerability search task. Our approach demonstrates strong performance across both tasks, performing better than all baseline approaches.