mlsanityData to 7 Oct 2026

Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers

arXiv 2605.18231 ↗First posted 18 May 2026cs.LGcited by 0

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Abstract (arXiv)

Binary function classifiers play a crucial role in maintaining the security and integrity of software systems by detecting malicious code and unauthorized modifications. However, machine learning-based classifiers are vulnerable to adversarial attacks that can evade detection. In this study, we present Kelpie, a novel framework for executing mimicry attacks, a stronger type of targeted evasion attacks, on binary function classifiers in a black-box, zero-query setting. Unlike previous approaches that rely on querying the target classifier to refine untargeted evasion attacks, Kelpie leverages code transformations that preserve the functionality of malicious payloads while causing them to be misclassified as we want. Through extensive experimentation, we demonstrate that Kelpie can successfully execute mimicry attacks against six state-of-the-art binary function classifiers representing different model architectures without requiring direct interaction with them. We further validate our approach with a practical demonstration, involving a keylogger and a wiper concealed within benign-looking functions embedded in an application. This work, to our best knowledge, is the first to demonstrate such a mimicry attack in a black-box, zero-query context, raising important questions about the reliability and security of existing machine learning-based binary function classifiers.

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References · 35

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  12. 2019Sign-OPT: A Query-Efficient Hard-label Adversarial Attackcited by 118
23 more
  1. 2017Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detectioncited by 65
  2. 2018Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executablescited by 60
  3. 2020Functionality-preserving Black-box Optimization of Adversarial Windows Malwarecited by 58
  4. 2019Stateful Detection of Black-Box Adversarial Attackscited by 55
  5. 2019Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimizationcited by 49
  6. 2021PalmTree: Learning an Assembly Language Model for Instruction Embeddingcited by 49
  7. 2022jTrans: Jump-Aware Transformer for Binary Code Similaritycited by 44
  8. 2018SAFE: Self-Attentive Function Embeddings for Binary Similaritycited by 33
  9. 2019Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queriescited by 33
  10. 2019Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytescited by 31
  11. 2018Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairscited by 25
  12. 2020Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detectioncited by 18
  13. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  14. 2020Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimescited by 17
  15. 2024CLAP: Learning Transferable Binary Code Representations with Natural Language Supervisioncited by 17
  16. 2023Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performancecited by 12
  17. 2017Evading Classifiers by Morphing in the Darkcited by 8
  18. 2018Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attackscited by 5
  19. 2022Black-box Attacks Against Neural Binary Function Detectioncited by 3
  20. 2022Scalable Program Clone Search Through Spectral Analysiscited by 3
  21. 2023Adversarial Attacks against Binary Similarity Systemscited by 3
  22. 2023The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learningcited by 3
  23. 2024StrTune: Data Dependence-based Code Slicing for Binary Similarity Detection with Fine-tuned Representationcited by 1

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