When AI Coding Assistants Leak Training Data: A Study of LLM Memorization in Code Generation

Abstract

Large Language Models (LLMs) for code generation risk memorizing and reproducing sensitive training data, including licensed code and proprietary information. We investigate memorization behavior in recent open-weight LLMs in code generation using a two-stage memorization evaluation pipeline, which combines a similarity-based extractability filter with a targeted data extraction attack. We evaluate four models (StarCoder2-3B, StarCoder2-7B, Llama3-8B, and DeepSeek-R1-distilled-Llama-8B) on a custom dataset of 30,000+ Python files. Our results reveal memorization rates of 42-64%, with code-specialized models exhibiting higher rates than general-purpose models. Categorical analysis shows that repetitive content (license headers, documentation) is memorized at rates up to 70%, while complex code exhibits lower susceptibility. Notably, realistic code completion scenarios trigger unintentional memorization in 13-14% of cases, posing practical risks for AI coding assistants. We demonstrate that knowledge distillation reduces extraction rates by approximately 19%, offering a cost-effective mitigation approach. Our findings confirm that memorization persists in modern LLMs and is influenced more by a complex interplay of training domain, dataset composition, architectural choices, and content characteristics, rather than parameter count alone.