Detecting Functional Memorization in Code Language Models

arXiv:2606.12764v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to generate code at scale. Meanwhile, prior work has investigated whether training data may be recoverable from model outputs, by auditing the textual overlap between training examples and model generations. Code, however, can preserve the same logic while differing substantially in syntax and structure. We here study functional memorization: the leakage of training data logic from LLM generations in ways that textual audits fail to detect. We leverage AI coding agents to generate diverse test inputs for training data functionality and evaluate whether model-generated continuations produce the same outputs. We formalize this through a counterfactual framework, comparing target models (exposed to specific code) against reference models (not exposed) and requiring functional equivalence only for the target. We instantiate this framework across 4 open-source models and explicitly filter for functions with meaningful logic in 5 programming languages. We find that 0.3-3.4% of filtered functions are counterfactually functionally memorized, i.e., reproduced with equivalent behavior in restructured code that textual metrics fail to detect. We further show that LLM-based judges offer a scalable proxy for execution-based testing, achieving a true positive rate of 68% at 1% false positive rate, and find that functional memorization is associated with semantic duplication in the training corpus.

Abstract:Large language models (LLMs) are increasingly used to generate code at scale. Meanwhile, prior work has investigated whether training data may be recoverable from model outputs, by auditing the textual overlap between training examples and model generations. Code, however, can preserve the same logic while differing substantially in syntax and structure. We here study functional memorization: the leakage of training data logic from LLM generations in ways that textual audits fail to detect. We leverage AI coding agents to generate diverse test inputs for training data functionality and evaluate whether model-generated continuations produce the same outputs. We formalize this through a counterfactual framework, comparing target models (exposed to specific code) against reference models (not exposed) and requiring functional equivalence only for the target. We instantiate this framework across 4 open-source models and explicitly filter for functions with meaningful logic in 5 programming languages. We find that 0.3-3.4% of filtered functions are counterfactually functionally memorized, i.e., reproduced with equivalent behavior in restructured code that textual metrics fail to detect. We further show that LLM-based judges offer a scalable proxy for execution-based testing, achieving a true positive rate of 68% at 1% false positive rate, and find that functional memorization is associated with semantic duplication in the training corpus.

Machine Learning (cs.LG); Computation and Language (cs.CL); Cryptography and Security (cs.CR)

arXiv:2606.12764 [cs.LG]

(or arXiv:2606.12764v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.12764 Focus to learn more arXiv-issued DOI via DataCite

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Detecting Functional Memorization in Code Language Models

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