Researchers Fine-Tune LLMs to Reduce Vulnerabilities in Auto-Completed Smart Contract Code

Researchers from the Norwegian University of Science and Technology in Nanjing have unveiled a novel method, “vulnerability-constrained decoding,” to tackle vulnerabilities in auto-completed smart contract code, especially within Ethereum Blockchain. This technique harnesses a curated dataset of known vulnerable code lines to fine-tune large language models (LLMs), reducing potential code breaches. Remarkably, their efficient fine-tuning process takes only an hour, compared to traditional methods which might last a week. When tested on Ethereum smart contracts, the modified model demonstrated a significant 30% reduction in vulnerabilities. This groundbreaking research promises enhanced digital security in coding and paves the way for future security-focused studies in the tech domain.

Read more — https://news.superagi.com/2023/09/19/researchers-fine-tune-llms-to-reduce-vulnerabilities-in-auto-completed-smart-contract-code/