mlsanityData to 7 Oct 2026

VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detector

arXiv 2001.02350 ↗First posted 8 Jan 2020cs.CRcited by 37

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Abstract (arXiv)

Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities; the other two are not reported in the National Vulnerability Database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jan 2020.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 5; 2024: 15; 2025: 6.

37 papers.

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  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  2. 2022An Empirical Study of Deep Learning Models for Vulnerability Detectioncited by 44
  3. 2025LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insightscited by 44
  4. 2022No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligencecited by 41
  5. 2022Transformer-Based Language Models for Software Vulnerability Detectioncited by 39
  6. 2021The Threat of Offensive AI to Organizationscited by 37
  7. 2024An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detectioncited by 32
  8. 2023How Far Have We Gone in Vulnerability Detection Using Large Language Modelscited by 27
  9. 2021VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatementscited by 20
  10. 2024LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Rewardcited by 16
  11. 2024Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Modelscited by 10
  12. 2025Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overviewcited by 7
25 more
  1. 2022Deep-Learning-based Vulnerability Detection in Binary Executablescited by 6
  2. 2024Harnessing the Power of LLMs in Source Code Vulnerability Detectioncited by 6
  3. 2022On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Modelscited by 5
  4. 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilitiescited by 4
  5. 2024A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Taskscited by 4
  6. 2024Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairscited by 4
  7. 2024EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Codecited by 4
  8. 2025Improving LLM Reasoning for Vulnerability Detection via Group Relative Policy Optimizationcited by 4
  9. 2024AI for DevSecOps: A Landscape and Future Opportunitiescited by 3
  10. 2025Revisiting Pre-trained Language Models for Vulnerability Detectioncited by 3
  11. 2021Literature review on vulnerability detection using NLP technologycited by 2
  12. 2022Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learningcited by 2
  13. 2022A Hierarchical Deep Neural Network for Detecting Lines of Codes with Vulnerabilitiescited by 2
  14. 2023DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilitiescited by 2
  15. 2024Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detectioncited by 2
  16. 2024Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Codecited by 2
  17. 2025CoTDeceptor:Adversarial Code Obfuscation Against CoT-Enhanced LLM Code Agentscited by 2
  18. 2023VMCDL: Vulnerability Mining Based on Cascaded Deep Learning Under Source Control Flowcited by 0
  19. 2023Sequential Graph Neural Networks for Source Code Vulnerability Identificationcited by 0
  20. 2024Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectorscited by 0
  21. 2024Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysiscited by 0
  22. 2024CLNX: Bridging Code and Natural Language for C/C++ Vulnerability-Contributing Commits Identificationcited by 0
  23. 2024Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?cited by 0
  24. 2024Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Pointscited by 0
  25. 2025Evaluating Line-level Localization Ability of Learning-based Code Vulnerability Detection Modelscited by 0