Code Transformation's Impact on LLM Membership Inference

This article investigates the effect of semantically equivalent code transformations on the vulnerability of LLMs for code to membership inference attacks. Understanding this relationship is crucial for improving the privacy and security of LLMs used in software development.

• •The research likely explores how different code styles and structures affect the ability to infer if a particular code snippet was part of the training data.
• •Findings could inform strategies to make LLMs for code more resistant to privacy attacks.
• •This work contributes to the growing body of research on LLM security and privacy in the context of code generation and analysis.