Automated Vulnerability Injection in Smart Contracts Using Large Language Models
Using LLMs to automatically inject known vulnerabilities into smart contracts tackles the ground-truth dataset scarcity that has hampered reliable benchmarking of blockchain security tools.
Summary written by editorial AI · Source link below
arXiv:2609.02624v1 Announce Type: cross Abstract: 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 demonstrate it in a case study targeting 49 vulnerability types from OpenSCV. Injected contracts are validated through a multi-step pipeline checkin
Editorial Analysis
Better benchmarking datasets improve confidence in smart-contract audit tools, which matters as DeFi exposure grows in enterprise treasury and settlement operations.
If your organisation audits smart contracts, evaluate LLM-generated vulnerability datasets as a supplement to manual test suites.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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