mlsanityData to 7 Oct 2026

Large Language Model for Vulnerability Detection: Emerging Results and Future Directions

arXiv 2401.15468 ↗First posted 27 Jan 2024cs.SEcited by 44

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Abstract (arXiv)

Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 13; 2025: 21; 2026: 10.

44 papers.

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  1. 2024Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAGcited by 67
  2. 2024Source Code Summarization in the Era of Large Language Modelscited by 30
  3. 2025KNighter: Transforming Static Analysis with LLM-Synthesized Checkerscited by 22
  4. 2025Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agentscited by 20
  5. 2024Multi-role Consensus through LLMs Discussions for Vulnerability Detectioncited by 19
  6. 2024Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Studycited by 17
  7. 2025Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studiescited by 12
  8. 2024VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Modelscited by 11
  9. 2024Are Large Language Models Memorizing Bug Benchmarks?cited by 11
  10. 2024Investigating Large Language Models for Code Vulnerability Detection: An Experimental Studycited by 11
  11. 2025Do LLMs Consider Security? An Empirical Study on Responses to Programming Questionscited by 8
  12. 2025A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Modelscited by 8
32 more
  1. 2025LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLMcited by 8
  2. 2025Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigmscited by 7
  3. 2025MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinementcited by 6
  4. 2026Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agentscited by 6
  5. 2024CoDefeater: Using LLMs To Find Defeaters in Assurance Casescited by 5
  6. 2025Large Language Models for In-File Vulnerability Localization Can Be "Lost in the End"cited by 5
  7. 2025Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmapcited by 5
  8. 2026Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industrycited by 5
  9. 2026Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filteringcited by 5
  10. 2025LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMscited by 4
  11. 2025A Mixture of Linear Corrections Generates Secure Codecited by 4
  12. 2025VulInstruct: Teaching LLMs Root-Cause Reasoning for Vulnerability Detection via Security Specificationscited by 4
  13. 2025A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detectioncited by 4
  14. 2026AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detectioncited by 4
  15. 2025Revisiting Pre-trained Language Models for Vulnerability Detectioncited by 3
  16. 2025OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Modelscited by 3
  17. 2026Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Reviewcited by 3
  18. 2024Generative transformations and patterns in LLM-native approaches for software verification and falsificationcited by 2
  19. 2025CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detectioncited by 2
  20. 2026VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilogcited by 2
  21. 2024Comparing Unidirectional, Bidirectional, and Word2vec Models for Discovering Vulnerabilities in Compiled Lifted Codecited by 1
  22. 2024What You See Is Not Always What You Get: Evaluating GPT's Comprehension of Source Codecited by 1
  23. 2026Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebasescited by 1
  24. 2024In-Context Code-Text Learning for Bimodal Software Engineeringcited by 0
  25. 2024Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Pointscited by 0
  26. 2025Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generationcited by 0
  27. 2025Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimizationcited by 0
  28. 2025Utilizing Large Language Models for Machine Learning Explainabilitycited by 0
  29. 2025SoK: Understanding (New) Security Issues Across AI4Code Use Casescited by 0
  30. 2026Separating Secrets from Placeholders: A Hybrid CNN-CodeBERT Framework for Three-Class Credential Leakage Detectioncited by 0
  31. 2026Evaluating LLMs for Real-World Web Vulnerability Detectioncited by 0
  32. 2026Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Taskcited by 0