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How to Compare the Security of Code Written by Humans to LLM-generated Code

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arXiv:2606. 00186v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly transforming how software is created and maintained.

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Authors: Rebecca Balebako, Jasmine Egl
Released: 2026-06-02 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because How to Compare the Security of Code Written by Humans to LLM-generated Code overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, soft, source, line, rate, implement, compare, language. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.00186

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