arXiv (All)
open access
LLMVul: A Vulnerability-Labeled Dataset of LLM-Generated C/C++ Functions from Real Production Repositories
Large language models (LLMs) are increasingly used to generate and assist with software development, yet existing vulnerability datasets largely focus on human-written code or controlled prompting environments. This limits the ability to study security weaknesses in LLM-generated code as it appears
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting#781206
- GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting#779831
- LLM4C2Rust: Large Language Models for Automated Memory-Safe Code Transpilation#31344
- HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation#1128547
- Official Review · ICLR 2026#787103
Record · ID 998519
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive