mlsanityData to 14 Oct 2026

jTrans: Jump-Aware Transformer for Binary Code Similarity

arXiv 2205.12713 ↗First posted 25 May 2022cs.CR cs.SEcited by 44

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Abstract (arXiv)

Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Transformer-based approach, namely jTrans, to learn representations of binary code. It is the first solution that embeds control flow information of binary code into Transformer-based language models, by using a novel jump-aware representation of the analyzed binaries and a newly-designed pre-training task. Additionally, we release to the community a newly-created large dataset of binaries, BinaryCorp, which is the most diverse to date. Evaluation results show that jTrans outperforms state-of-the-art (SOTA) approaches on this more challenging dataset by 30.5% (i.e., from 32.0% to 62.5%). In a real-world task of known vulnerability searching, jTrans achieves a recall that is 2X higher than existing SOTA baselines.

Key facts. Cited by 44 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2022.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 14; 2025: 8; 2026: 11.

44 papers.

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  1. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 71
  2. 2023How Far Have We Gone in Vulnerability Detection Using Large Language Modelscited by 27
  3. 2024CLAP: Learning Transferable Binary Code Representations with Natural Language Supervisioncited by 17
  4. 2025Large Language Models (LLMs) for Source Code Analysis: applications, models and datasetscited by 16
  5. 2023Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Modelscited by 13
  6. 2023Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learningcited by 12
  7. 2024Assemblage: Automatic Binary Dataset Construction for Machine Learningcited by 12
  8. 2023kTrans: Knowledge-Aware Transformer for Binary Code Embeddingcited by 10
  9. 2024EyeTrans: Merging Human and Machine Attention for Neural Code Summarizationcited by 9
  10. 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Basescited by 8
  11. 2026ShieldedCode: Learning Robust Representations for Virtual Machine Protected Codecited by 8
  12. 2024CodeArt: Better Code Models by Attention Regularization When Symbols Are Lackingcited by 7
32 more
  1. 2023EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysiscited by 6
  2. 2024Is Function Similarity Over-Engineered? Building a Benchmarkcited by 6
  3. 2023FASER: Binary Code Similarity Search through the use of Intermediate Representationscited by 4
  4. 2025ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuningcited by 4
  5. 2022FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methodscited by 3
  6. 2022Leveraging Artificial Intelligence on Binary Code Comprehensioncited by 3
  7. 2022UniASM: Binary Code Similarity Detection without Fine-tuningcited by 3
  8. 2023Adversarial Attacks against Binary Similarity Systemscited by 3
  9. 2024BLens: Contrastive Captioning of Binary Functions using Ensemble Embeddingcited by 3
  10. 2024ViC: Virtual Compiler Is All You Need For Assembly Code Searchcited by 2
  11. 2024On the Lack of Robustness of Binary Function Similarity Systemscited by 2
  12. 2025Tady: A Neural Disassembler without Structural Constraint Violationscited by 2
  13. 2023VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similaritycited by 1
  14. 2024Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLMcited by 1
  15. 2024A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfercited by 1
  16. 2025Cross-modal Retrieval Models for Stripped Binary Analysiscited by 1
  17. 2025A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysiscited by 1
  18. 2026Fool Me If You Can: On the Robustness of Binary Code Similarity Detection Models against Semantics-preserving Transformationscited by 1
  19. 2024Multi-modal Learning for WebAssembly Reverse Engineeringcited by 0
  20. 2024Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphletscited by 0
  21. 2025Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guaranteescited by 0
  22. 2025Esim: EVM Bytecode Similarity Detection Based on Stable-Semantic Graphcited by 0
  23. 2025C-ing Clearly: Enhanced Binary Code Explanations using C codecited by 0
  24. 2026The New Compiler Stack: A Survey on the Synergy of LLMs and Compilerscited by 0
  25. 2026EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wildcited by 0
  26. 2026Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifierscited by 0
  27. 2026ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coveragecited by 0
  28. 2026SoK: AI-Augmented Binary Reversingcited by 0
  29. 2026OASIF: An Efficient Obfuscation-Aware Self-Improving Framework for LLM-Based Assembly Code Instruction Following and Comprehensioncited by 0
  30. 2026Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Contextcited by 0
  31. 2026Instruction Alignment for Binary Code Representation Learningcited by 0
  32. 2026Reveree: Diagnosing LLM Reverse-Engineering Agentscited by 0