Research
Security in LLM-Assisted Coding: A Systematic Literature Review
Publication date unknown · Discovered arxiv.org
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Publication date unknown · First observed: 15 Oct 2026
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Single-source research — one report is available.
Why it matters
LLM-assisted coding tools are widely deployed, and this review documents systemic weaknesses—insecure generated code, weak evaluation benchmarks, and models vulnerable to direct attack—that defenders must account for when integrating AI into development pipelines.
A systematic literature review titled "Security in LLM-Assisted Coding" by Yuan et al. synthesizes research on security risks in LLM-based code generation and automated program repair, finding that models frequently produce insecure code and faulty patches due to training-data biases, are themselves susceptible to direct attacks, and that current benchmarks suffer from data contamination and lack robust security metrics. The review argues human oversight remains essential and calls for security-aware architectures and stronger security-specific benchmarks.