Open-Source Intelligence Distinguishes Human and LLM Code, Reveals Security Pattern Differences

Jul 15, 2026

A new preprint presents a reproducible, open-source pipeline that distinguishes code written by humans from that generated by large language models (LLMs) with 93% accuracy, using samples from 31 security-sensitive programming tasks across four languages. The study finds systematic differences in security patterns between human and LLM-generated code, and shows that LLMs can repair vulnerabilities in code 77% of the time, though repairs are often only partial fixes.

Why it matters: This work advances automated code provenance attribution and highlights important security differences between human and AI-generated code, informing software supply chain security and AI-assisted development practices.

Full story at: arXiv Cryptography and Security ↗