# Awesome-LLMs-for-Vulnerability-Detection

**Repository Path**: jiumao-admin/Awesome-LLMs-for-Vulnerability-Detection

## Basic Information

- **Project Name**: Awesome-LLMs-for-Vulnerability-Detection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-01-06
- **Last Updated**: 2026-01-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Awesome Large Language Models for Vulnerability Detection 

| Title | Venue | Year | Paper | Github |
| :----------------------------------------------------------: | :----------: | :--: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| From Large to Mammoth: A Comparative Evaluation of Large Language Models in Vulnerability Detection | NDSS | 2025 | [link](https://www.ndss-symposium.org/ndss-paper/from-large-to-mammoth-a-comparative-evaluation-of-large-language-models-in-vulnerability-detection/) | |
| Benchmarking LLMs and LLM-based Agents in Practical Vulnerability Detection for Code Repositories | ACL | 2025 | [link](https://aclanthology.org/2025.acl-long.1490/) | [link](https://github.com/alperen21/JitVul) |
| A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models |  | 2025 | [link](https://arxiv.org/abs/2507.22659) | [link](https://github.com/hs-esslingen-it-security/Awesome-LLM4SVD) |
| LLMxCPG: Context-Aware Vulnerability Detection Through Code Property Graph-Guided Large Language Models | Usenix | 2025 | [link](https://arxiv.org/abs/2507.16585) | [link](https://github.com/qcri/llmxcpg) |
| CLeVeR: Multi-modal Contrastive Learning for Vulnerability Code Representation | ACL Findings | 2025 | [link](https://aclanthology.org/2025.findings-acl.414/) | [link](https://github.com/yoimiya-nlp/CLeVeR) |
| Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond |  | 2025 | [link](https://arxiv.org/abs/2506.03651) | [link](https://github.com/vul337/mono) |
| Learning to Focus: Context Extraction for Efficient Code Vulnerability Detection with Language Models |  | 2025 | [link](https://arxiv.org/abs/2505.17460) |  |
| SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability Analysis | SP | 2025 | [link](https://arxiv.org/abs/2505.20630) | [link](https://github.com/Jackline97/SV-TrustEval-C) |
| SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection |  | 2025 | [link](https://arxiv.org/abs/2505.19828) | [link](https://github.com/basimbd/secvuleval) |
| CVE-Bench: Benchmarking LLM-based Software Engineering Agent’s Ability to Repair Real-World CVE Vulnerabilities | NAACL | 2025 | [link](https://aclanthology.org/2025.naacl-long.212/) | [link](https://github.com/WhileBug/CVEBench) |
| R2Vul: Learning to Reason about Software Vulnerabilities with Reinforcement Learning and Structured Reasoning Distillation |  | 2025 | [link](https://arxiv.org/abs/2504.04699) | [link](https://github.com/martin-wey/R2Vul) |
| Automated static vulnerability detection via a holistic neuro-symbolic approach |  | 2025 | [link](https://arxiv.org/abs/2504.16057) |  |
| Context-Enhanced Vulnerability Detection Based on Large Language Model |  | 2025 | [link](https://arxiv.org/abs/2504.16877) |  |
| Everything you wanted to know about LLM-based vulnerability detection but were afraid to ask |  | 2025 | [link](https://arxiv.org/abs/2504.13474) | [link](https://anonymous.4open.science/r/CORRECT/README.md) |
| MOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models |  | 2025 | [link](https://arxiv.org/abs/2504.12234) |  |
| Abundant modalities offer more nutrients: multi-modal-based function-level vulnerability detection | TOSEM | 2025 | [link](https://dl.acm.org/doi/10.1145/3731557) | [link](https://github.com/vinci-grape/MVulD) |
| Generative Large Language Model usage in Smart Contract Vulnerability Detection |  | 2025 | [link](https://arxiv.org/abs/2504.04685) |  |
| Closing the Gap: A User Study on the Real-world Usefulness of AI-powered Vulnerability Detection & Repair in the IDE | ICSE | 2025 | [link](https://www.arxiv.org/abs/2412.14306) | [link](https://doi.org/10.6084/m9.figshare.26367139) |
| Vulnerability Detection with Code Language Models: How Far Are We? | ICSE | 2025 | [link](https://arxiv.org/abs/2403.18624) | [link](https://github.com/DLVulDet/PrimeVul) |
| Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications | ICSE | 2025 | [link](https://arxiv.org/abs/2403.16073) |  |
| LAMD: Context-driven Android Malware Detection and Classification with LLMs |  | 2025 | [link](http://arxiv.org/abs/2502.13055) |  |
| LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights |  | 2025 | [link](https://arxiv.org/abs/2502.07049) | [link](https://github.com/OwenSanzas/LLM-For-Software-Security) |
| One-for-All Does Not Work! Enhancing Vulnerability Detection by Mixture-of-Experts (MoE) |  | 2025 | [link](https://arxiv.org/abs/2501.16454) |  |
| Leveraging Semantic Relations in Code and Data to Enhance Taint Analysis of Embedded Systems | Usenix | 2024 | [link](https://www.usenix.org/system/files/usenixsecurity24-zhao.pdf) | [link](https://sites.google.com/view/lara-data) |
| Effective Vulnerable Function Identification based on CVE Description Empowered by Large Language Models | ASE | 2024 | [link](https://doi.org/10.1145/3691620.3695013) | [link](https://github.com/CGCL-codes/VFFinder) |
| SCALE: Constructing Structured Natural Language Comment Trees for Software Vulnerability Detection | ISSTA | 2024 | [link](https://doi.org/10.1145/3650212.3652124) | [link](https://github.com/Xin-Cheng-Wen/Comment4Vul) |
| LLMDFA: Analyzing Dataflow in Code with Large Language Model | NeurIPS | 2024 | [link](https://chengpeng-wang.github.io/publications/LLMDFA_NeurIPS2024.pdf) | [link](https://github.com/chengpeng-wang/LLMDFA) |
| Learning to Detect and Localize Multilingual Bugs | FSE | 2024 | [link](https://doi.org/10.1145/3660804) |  |
| GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis | ICSE | 2024 | [link](https://doi.org/10.1145/3597503.3639117) |  |
| Sanitizing Large Language Models in Bug Detection with Data-Flow | EMNLP | 2024 | [link](https://aclanthology.org/2024.findings-emnlp.217/) | [link](https://github.com/chengpeng-wang/LLMSAN) |
| RealVul: Can We Detect Vulnerabilities in Web Applications with LLM? | EMNLP | 2024 | [link](https://aclanthology.org/2024.emnlp-main.472) |  |
| Where is it? Tracing the Vulnerability-relevant Files from Vulnerability Reports | ICSE | 2024 | [link](https://doi.org/10.1145/3597503.3639202) | [link](https://github.com/anonymous-77400046/vulnerability_file_trace) |
| Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection | ICSE | 2024 | [link](https://doi.org/10.1145/3597503.3623345) | [link](https://doi.org/10.6084/m9.figshare.21225413) |
| Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks | ICSE | 2024 | [link](https://doi.org/10.1145/3597503.3639142) | [link](https://github.com/ZJU-CTAG/PDBERT) |
| Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study |  | 2024 | [link](https://arxiv.org/abs/2412.18260) | [link](https://github.com/SakiRinn/LLM4CVD) |
| CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics |  | 2024 | [link](https://arxiv.org/abs/2411.17274) | [link](https://github.com/yikun-li/CleanVul) |
| An Empirical Study of Vulnerability Detection using Federated Learning |  | 2024 | [link](https://arxiv.org/abs/2411.16099) |  |
| LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2410.09381) | [link](https://github.com/LLMAudit/LLMSmartAuditTool) |
| Advancing Bug Detection in Fastjson2 with Large Language Models Driven Unit Test Generation |  | 2024 | [link](https://arxiv.org/abs/2410.09414) |  |
| Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead |  | 2024 | [link](https://arxiv.org/abs/2404.02525) |  |
| StagedVulBERT: Multi-Granular Vulnerability Detection with a Novel Pre-trained Code Model |  | 2024 | [link](https://arxiv.org/abs/2410.05766) | [link](https://github.com/YuanJiangGit/StagedVulBERT) |
| LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning |  | 2024 | [link](https://arxiv.org/abs/2401.16185) |  |
| Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs |  | 2024 | [link](https://arxiv.org/abs/2409.00571) |  |
| Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2408.16400) |  |
| ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data |  | 2024 | [link](https://arxiv.org/abs/2408.16028) |  |
| Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2408.12986) |  |
| Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2407.16235) |  |
| Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG |  | 2024 | [link](https://arxiv.org/abs/2406.11147) |  |
| Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models |  | 2024 | [link](https://arxiv.org/abs/2406.05892) |  |
| Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning | ACL Findings | 2024 | [link](https://aclanthology.org/2024.findings-acl.625/) | [link](https://github.com/CGCL-codes/VulLLM) |
| M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2406.05940) | [link](https://github.com/HotFrom/M2CVD) |
| VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models |  | 2024 | [link](https://arxiv.org/abs/2406.07595) | [link](https://github.com/Sweetaroo/VulDetectBench) |
| LLM-Assisted Static Analysis for Detecting Security Vulnerabilities |  | 2024 | [link](https://arxiv.org/abs/2405.17238) |  |
| Multi-role Consensus through LLMs Discussions for Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2403.14274) | [link](https://github.com/rockmao45/llmvulndetection) |
| LLMs Cannot Reliably Identify and Reason About Security Vulnerabilities (Yet?): A Comprehensive Evaluation, Framework, and Benchmarks | IEEE S&P | 2024 | [link](https://www.computer.org/csdl/proceedings-article/sp/2024/313000a019/1RjE9Wb4Wze) | [link](https://github.com/ai4cloudops/secllmholmes) |
| Large Language Model for Vulnerability Detection: Emerging Results and Future Directions | ICSE | 2024 | [link](https://dl.acm.org/doi/abs/10.1145/3639476.3639762) |  |
| Prompt-Enhanced Software Vulnerability Detection Using ChatGPT | ICSE | 2024 | [link](https://dl.acm.org/doi/10.1145/3639478.3643065) |  |
| DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2405.01202) |  |
| Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study |  | 2024 | [link](https://arxiv.org/abs/2405.15614) |  |
| Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach | OOPSLA | 2024 | [link](https://dl.acm.org/doi/10.1145/3649828) | [link](https://github.com/seclab-ucr/LLift) |
| Source Code Vulnerability Detection: Combining Code Language Models and Code Property Graphs |  | 2024 | [link](https://arxiv.org/abs/2404.14719) | [link](https://github.com/vul-lmgnn/vul-lmggnn) |
| Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation | LCTES | 2024 | [link](https://dl.acm.org/doi/10.1145/3652032.3657564) |  |
| VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2404.15596) |  |
| Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead |  | 2024 | [link](https://arxiv.org/abs/2404.02525) |  |
| A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2403.17218) |  |
| Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities |  | 2024 | [link](https://arxiv.org/abs/2402.17230) |  |
| Finetuning Large Language Models for Vulnerability Detection |  | 2024 | [link](https://arxiv.org/abs/2401.17010) | [link](https://github.com/rmusab/vul-llm-finetune) |
| How Far Have We Gone in Vulnerability Detection Using Large Language Models |  | 2023 | [link](https://arxiv.org/abs/2311.12420) | [link](https://github.com/Hustcw/VulBench) |
| Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning? |  | 2023 | [link](https://arxiv.org/abs/2306.01754) |  |
| Software Vulnerability Detection using Large Language Models | IEEE | 2023 | [link](https://ieeexplore.ieee.org/abstract/document/10301302) |  |
| DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection | RAID | 2023 | [link](https://dl.acm.org/doi/abs/10.1145/3607199.3607242) |  |
| VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection | IEEE | 2022 | [link](https://ieeexplore.ieee.org/abstract/document/9892280) |  |
| Deep Learning Based Vulnerability Detection: Are We There Yet? | IEEE | 2022 | [link](https://ieeexplore.ieee.org/abstract/document/9448435) |  |
| Transformer-Based Language Models for Software Vulnerability Detection | ACSAC | 2022 | [link](https://dl.acm.org/doi/abs/10.1145/3564625.3567985) |  |
| Software Vulnerability Detection Using Deep Neural Networks: A Survey | IEEE | 2020 | [link](https://ieeexplore.ieee.org/abstract/document/9108283) |  |
| Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks | NeurIPS | 2019 | [link](https://dl.acm.org/doi/abs/10.5555/3454287.3455202) |  |
| μμVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection | IEEE | 2019 | [link](https://ieeexplore.ieee.org/abstract/document/8846081) |  |
| VulDeePecker: A Deep Learning-Based System for Vulnerability Detection | NDSS | 2018 | [link](https://www.ndss-symposium.org/wp-content/uploads/2018/02/ndss2018_03A-2_Li_paper.pdf) |  |

# arxiv.md

Automated daily capture and update of Arxiv papers for specified keywords through workflows.

# Acknowledgements

The project's Updated Arxiv Papers Daily workflow borrows from this project [LLM4SE](https://github.com/gai4se/LLM4SE). I refactored its original code by using the [arxiv](https://github.com/lukasschwab/arxiv.py) library.