Projects sharing features with StagedVulBERT

LLMDFA is an LLM-powered data-flow analysis framework. Specifically, it instantiates bottom-up summary-based data-flow analysis by interpreting intra-procedural data-flow facts with LLMs. With the specified sources/sinks and data-flow transfer functions (i.e., rules of propagating data-flow…

LLMSAN is a tool for prompting-based bug detection. Equipped with the sanitization technique, LLMSAN can recognize false positives in the reported bugs without introducing huge additional token costs.

📜 Overview | 📚 Dataset | 💻 Experiments | 📝 Citation

Code Vulnerability Detection with Devign and RV Datasets This repository contains code for training and evaluating a vulnerability detection model using the Devign and RV datasets. The model is based on RoBERTa and UnixCoder, leveraging pre-trained models for code defect prediction tasks. This…

This repository contains materials for the paper "How Far Have We Gone in Vulnerability Detection Using Large Language Model".

SV‑TrustEval‑C is the first reasoning‑based benchmark designed to rigorously evaluate Large Language Models (LLMs) on both structure (control/data flow) and semantic reasoning for vulnerability analysis in C source code. Unlike existing benchmarks that focus on pattern recognition,…

LLM-SmartAudit is a cutting-edge tool designed to revolutionize smart contract auditing using advanced language models.

This is the replication package accompanying our paper "R2Vul: Learning to Reason about Software Vulnerabilities with Reinforcement Learning and Structured Reasoning Distillation".

Role-Specific Evaluation of LLMs for Security Vulnerability Detection Paper · Docs · Quick Start · Results

2025.11 LLMxCPG wins the MENA CSAW Applied Research Competition (ARC) 2025. - 2025.10 LLMxCPG is accepted to the MENA CSAW Applied Research Competition (ARC) 2025. - 2025.08 LLMxCPG is presented at USENIX Security 2025.

This repository contains the necessary code for reproducing the experiments conducted in the paper "Finetuning Large Language Models for Vulnerability Detection": https://doi.org/10.1109/ACCESS.2025.3546700

The complete record of the discussion example introduced in the paper "Multi-role Consensus through LLMs Discussions for Vulnerability Detection".

This is the codebase for the paper "Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study", which is submitted to IEEE TIFS. The arxiv version of this work is publicly available via this link. We also provide the 🤗Huggingface repository for all evaluated…

This repo contains all the code and test cases for the LLift project

VulDetectBench is a benchmark designed to evaluate the vulnerability detection capabilities of Large Language Models (LLMs).

VulnGym: A Real-World, Project-Level Vulnerability Benchmark for White-Box Vulnerability-Hunting Agents

Software vulnerabilities are the weaknesses in software systems, that leads to serious cybersecurity problems. Recently, many deep learning-based approaches have been proposed to detect vulnerabilities at the function level by using one or a few different modalities (e.g., text representation,…

An intelligent vulnerability detection framework based on Large Language Models and static code analysis, supporting multi-dataset evaluation and ablation studies.

Code for the paper - Source Code Vulnerability Detection: Combining Code Language Models and Code Property Graph

mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond

First please download and preprocess the CVEFixes database: `shell mkdir data cd data wget https://zenodo.org/records/13118970/files/CVEfixesv1.0.8.zip unzip CVEfixesv1.0.8.zip gzip -d CVEfixesv1.0.8/Data/CVEfixesv1.0.8.sql.gz sqlite3 CVEfixes.db < CVEfixesv1.0.8/Data/CVEfixesv1.0.8.sql `

Recently, there has been a growing interest in automatic software vulnerability detection. Pre-trained model-based approaches have demonstrated superior performance than other Deep Learning (DL)-based approaches in detecting vulnerabilities. However, the existing pre-trained model-based…

📜 Overview | 📚 Dataset

CLeVeR is a novel approach that leverages contrastive learning to generate precise vulnerability code representations under the supervision of vulnerability descriptions. Specifically, CLeVeR introduces an Adapter, a Representation Refinement module, and a Description Simulator to mitigate the…