Probing Privacy Leaks in LLM-based Code Generation via Test Generation
Code-generating LLMs leak personally identifiable information from training data through generated test cases, posing GDPR compliance risks for European enterprises using AI coding assistants.
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arXiv:2605.15248v1 Announce Type: cross Abstract: The widespread availability of large-scale code datasets has fueled the rapid development of large language models (LLMs) for code-related tasks. These datasets may include sensitive personally identifiable information (PII), which can lead to privacy leakage when LLMs memorize and reproduce it. However, existing privacy-leakage detection methods rely on ad-hoc prompt construction (manually or automatically designed). Therefore, they do not adeq
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