mlsanityData to 7 Oct 2026

Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes

arXiv 1912.09064 ↗First posted 19 Dec 2019cs.CR cs.CVcited by 31

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Abstract (arXiv)

Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we propose an attack that interweaves binary-diversification techniques and optimization frameworks to mislead such DNNs while preserving the functionality of binaries. Unlike prior attacks, ours manipulates instructions that are a functional part of the binary, which makes it particularly challenging to defend against. We evaluated our attack against three DNNs in white- and black-box settings, and found that it often achieved success rates near 100%. Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%. We explored several defenses, both new and old, and identified some that can foil over 80% of our evasion attempts. However, these defenses may still be susceptible to evasion by attacks, and so we advocate for augmenting malware-detection systems with methods that do not rely on machine learning.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 7; 2024: 9; 2025: 4; 2026: 4.

31 papers.

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  1. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  2. 2023RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletioncited by 17
  3. 2023Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothingcited by 5
  4. 2024A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothingcited by 4
  5. 2024SLIFER: Investigating Performance and Robustness of Malware Detection Pipelinescited by 4
  6. 2021Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weightscited by 3
  7. 2023Adversarial Attacks against Binary Similarity Systemscited by 3
  8. 2023DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustnesscited by 3
  9. 2023URET: Universal Robustness Evaluation Toolkit (for Evasion)cited by 3
  10. 2022Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detectioncited by 2
  11. 2022Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malwarecited by 2
  12. 2024On the Lack of Robustness of Binary Function Similarity Systemscited by 2
19 more
  1. 2025Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systemscited by 2
  2. 2022On deceiving malware classification with section injectioncited by 1
  3. 2023Hyper-parameter Tuning for Adversarially Robust Modelscited by 1
  4. 2024Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Researchcited by 1
  5. 2024A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detectioncited by 1
  6. 2024PBP: Post-training Backdoor Purification for Malware Classifierscited by 1
  7. 2024On the Effectiveness of Adversarial Training on Malware Classifierscited by 1
  8. 2025On the Security Risks of ML-based Malware Detection Systems: A Surveycited by 1
  9. 2026Fool Me If You Can: On the Robustness of Binary Code Similarity Detection Models against Semantics-preserving Transformationscited by 1
  10. 2021A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binariescited by 0
  11. 2022Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detectioncited by 0
  12. 2023On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectorscited by 0
  13. 2024Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothingcited by 0
  14. 2024On the Robustness of Malware Detectors to Adversarial Samplescited by 0
  15. 2025Sy-FAR: Symmetry-based Fair Adversarial Robustnesscited by 0
  16. 2025Attacking Autonomous Driving Agents with Adversarial Machine Learning: A Holistic Evaluation with the CARLA Leaderboardcited by 0
  17. 2026Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformationscited by 0
  18. 2026Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifierscited by 0
  19. 2026EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usabilitycited by 0