arXiv (All)
open access
Automated Vulnerability Injection in Smart Contracts Using Large Language Models
Assessing vulnerability detection tools for smart contracts requires datasets with known ground truth, yet such datasets are scarce and difficult to build by hand. We propose an approach that uses Large Language Models (LLMs) to automatically inject vulnerabilities into Solidity smart contracts, and
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
- Automated Vulnerability Injection in Smart Contracts Using Large Language Models#920540
- Transforming Natural Language Stateless Commercial Contracts into Computer Computable Contracts#643814
- Automated Vulnerability Injection in Smart Contracts Using Large Language Models#632767
- Solidity Defects and Bugs Analysis (SDABA): A novel framework for smart contract vulnerability detection and benchmarking#336502
- AI‑driven framework for contract risk automation and compliance in oracle CPQ.#610408
Record · ID 924774
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