mlsanityData to 7 Oct 2026

SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

arXiv 1807.06756 ↗First posted 18 Jul 2018cs.LG cs.AI cs.CRcited by 91

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Abstract (arXiv)

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily basis. This calls for machine learning methods for vulnerability detection. Deep learning is attractive for this purpose because it alleviates the requirement to manually define features. Despite the tremendous success of deep learning in other application domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities in C/C++ programs with source code. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the pertinent software products.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 11; 2023: 17; 2024: 22; 2025: 22; 2026: 12.

91 papers.

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  1. 2024Large Language Model-Based Agents for Software Engineering: A Surveycited by 173
  2. 2023Large Language Models for Code: Security Hardening and Adversarial Testingcited by 149
  3. 2024Vulnerability Detection with Code Language Models: How Far Are We?cited by 133
  4. 2023DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detectioncited by 98
  5. 2022VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detectioncited by 51
  6. 2022An Empirical Study of Deep Learning Models for Vulnerability Detectioncited by 44
  7. 2025LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insightscited by 44
  8. 2022Transformer-Based Language Models for Software Vulnerability Detectioncited by 39
  9. 2024An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detectioncited by 32
  10. 2024Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuningcited by 30
  11. 2024NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctnesscited by 23
  12. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
38 more
  1. 2021VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatementscited by 20
  2. 2024Multi-role Consensus through LLMs Discussions for Vulnerability Detectioncited by 19
  3. 2021Towards Learning (Dis)-Similarity of Source Code from Program Contrastscited by 17
  4. 2025Correctness Assessment of Code Generated by Large Language Models Using Internal Representationscited by 17
  5. 2025From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEscited by 17
  6. 2025A Framework for Evaluating Emerging Cyberattack Capabilities of AIcited by 15
  7. 2023Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?cited by 13
  8. 2023SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection with LLMs?cited by 13
  9. 2024VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Modelscited by 11
  10. 2024Investigating Large Language Models for Code Vulnerability Detection: An Experimental Studycited by 11
  11. 2021On using distributed representations of source code for the detection of C security vulnerabilitiescited by 10
  12. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  13. 2022Poison Attack and Defense on Deep Source Code Processing Modelscited by 8
  14. 2023When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detectioncited by 8
  15. 2025VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detectioncited by 8
  16. 2023Just-in-Time Detection of Silent Security Patchescited by 7
  17. 2025Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overviewcited by 7
  18. 2025A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Taskscited by 7
  19. 2022Pre-Training Representations of Binary Code Using Contrastive Learningcited by 6
  20. 2024Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasetscited by 6
  21. 2024Harnessing the Power of LLMs in Source Code Vulnerability Detectioncited by 6
  22. 2026MulVul: Retrieval-augmented Multi-Agent Code Vulnerability Detection via Cross-Model Prompt Evolutioncited by 6
  23. 2021Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representationcited by 5
  24. 2022On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Modelscited by 5
  25. 2023An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graphcited by 5
  26. 2024Data Quality Issues in Vulnerability Detection Datasetscited by 5
  27. 2025Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Predictioncited by 5
  28. 2021Towards Making Deep Learning-based Vulnerability Detectors Robustcited by 4
  29. 2023XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detectioncited by 4
  30. 2024A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Taskscited by 4
  31. 2024EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Codecited by 4
  32. 2025Improving LLM Reasoning for Vulnerability Detection via Group Relative Policy Optimizationcited by 4
  33. 2021Multi-context Attention Fusion Neural Network for Software Vulnerability Identificationcited by 3
  34. 2023LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Typescited by 3
  35. 2024RealVul: Can We Detect Vulnerabilities in Web Applications with LLM?cited by 3
  36. 2025Evaluating LLaMA 3.2 for Software Vulnerability Detectioncited by 3
  37. 2025Towards Practical Defect-Focused Automated Code Reviewcited by 3
  38. 2025Revisiting Pre-trained Language Models for Vulnerability Detectioncited by 3

50 most cited of 91 All 91 (JSON)