ContextLeak: Auditing Leakage in Private In-Context Learning Methods

ArXi:2512.16059v2 Announce Type: replace-cross In-Context Learning (ICL) has become a standard technique for adapting Large Language Models (LLMs) to specialized tasks by supplying task-specific exemplars within the prompt. However, when these exemplars contain sensitive information, reliable privacy-preserving mechanisms are essential to prevent unintended leakage through model outputs. Many privacy-preserving methods have been proposed to protect against information leakage in this context, but there are fewer efforts on how to audit these methods. We