| Title | Venue | Year | Paper | Slide | Video | Github |
|---|---|---|---|---|---|---|
| Closing the Gap: A User Study on the Real-world Usefulness of AI-powered Vulnerability Detection & Repair in the IDE | ICSE | 2025 | link | link | ||
| Vulnerability Detection with Code Language Models: How Far Are We? | ICSE | 2025 | link | link | ||
| Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications | ICSE | 2025 | link | |||
| LAMD: Context-driven Android Malware Detection and Classification with LLMs | 2025 | link | ||||
| LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights | 2025 | link | link | |||
| One-for-All Does Not Work! Enhancing Vulnerability Detection by Mixture-of-Experts (MoE) | 2025 | link | ||||
| Leveraging Semantic Relations in Code and Data to Enhance Taint Analysis of Embedded Systems | Usenix | 2024 | link | link | ||
| Effective Vulnerable Function Identification based on CVE Description Empowered by Large Language Models | ASE | 2024 | link | link | ||
| SCALE: Constructing Structured Natural Language Comment Trees for Software Vulnerability Detection | ISSTA | 2024 | link | link | ||
| LLMDFA: Analyzing Dataflow in Code with Large Language Model | NeurIPS | 2024 | link | link | ||
| Learning to Detect and Localize Multilingual Bugs | FSE | 2024 | link | |||
| GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis | ICSE | 2024 | link | |||
| Sanitizing Large Language Models in Bug Detection with Data-Flow | EMNLP | 2024 | link | link | ||
| RealVul: Can We Detect Vulnerabilities in Web Applications with LLM? | EMNLP | 2024 | link | |||
| Where is it? Tracing the Vulnerability-relevant Files from Vulnerability Reports | ICSE | 2024 | link | link | ||
| Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection | ICSE | 2024 | link | link | ||
| Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks | ICSE | 2024 | link | link | ||
| Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study | 2024 | link | link | |||
| CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics | 2024 | link | link | |||
| An Empirical Study of Vulnerability Detection using Federated Learning | 2024 | link | ||||
| LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection | 2024 | link | link | |||
| Advancing Bug Detection in Fastjson2 with Large Language Models Driven Unit Test Generation | 2024 | link | ||||
| Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead | 2024 | link | ||||
| StagedVulBERT: Multi-Granular Vulnerability Detection with a Novel Pre-trained Code Model | 2024 | link | link | |||
| LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning | 2024 | link | ||||
| Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs | 2024 | link | ||||
| Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection | 2024 | link | ||||
| ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data | 2024 | link | ||||
| Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection | 2024 | link | ||||
| Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection | 2024 | link | ||||
| Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG | 2024 | link | ||||
| Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models | 2024 | link | ||||
| Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning | ACL | 2024 | link | link | ||
| M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection | 2024 | link | link | |||
| VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models | 2024 | link | link | |||
| LLM-Assisted Static Analysis for Detecting Security Vulnerabilities | 2024 | link | ||||
| Multi-role Consensus through LLMs Discussions for Vulnerability Detection | 2024 | link | link | |||
| LLMs Cannot Reliably Identify and Reason About Security Vulnerabilities (Yet?): A Comprehensive Evaluation, Framework, and Benchmarks | IEEE S&P | 2024 | link | link | ||
| Large Language Model for Vulnerability Detection: Emerging Results and Future Directions | ICSE | 2024 | link | |||
| Prompt-Enhanced Software Vulnerability Detection Using ChatGPT | ICSE | 2024 | link | |||
| DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection | 2024 | link | ||||
| Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study | 2024 | link | ||||
| Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach | OOPSLA | 2024 | link | link | link | |
| Source Code Vulnerability Detection: Combining Code Language Models and Code Property Graphs | 2024 | link | link | |||
| Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation | LCTES | 2024 | link | |||
| VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection | 2024 | link | ||||
| Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead | 2024 | link | ||||
| A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection | 2024 | link | ||||
| Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities | 2024 | link | ||||
| Finetuning Large Language Models for Vulnerability Detection | 2024 | link | link | |||
| How Far Have We Gone in Vulnerability Detection Using Large Language Models | 2023 | link | link | |||
| Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning? | 2023 | link | ||||
| Software Vulnerability Detection using Large Language Models | IEEE | 2023 | link | |||
| DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection | RAID | 2023 | link | |||
| VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection | IEEE | 2022 | link | |||
| Deep Learning Based Vulnerability Detection: Are We There Yet? | IEEE | 2022 | link | |||
| Transformer-Based Language Models for Software Vulnerability Detection | ACSAC | 2022 | link | |||
| Software Vulnerability Detection Using Deep Neural Networks: A Survey | IEEE | 2020 | link | |||
| Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks | NeurIPS | 2019 | link | |||
| μμVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection | IEEE | 2019 | link | |||
| VulDeePecker: A Deep Learning-Based System for Vulnerability Detection | NDSS | 2018 | link |
Automated daily capture and update of Arxiv papers for specified keywords through workflows.
The project's Updated Arxiv Papers Daily workflow borrows from this project LLM4SE. I refactored its original code by using the arxiv library.
