@inproceedings{li-etal-2026-srdetection,
title = "{S}r{D}etection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models",
author = "Li, Shuaimin and
Fan, Liyang and
li, Zeyang and
Wan, Zhuoyue and
Lin, Yufang and
Ni, Shiwen and
Fang, Feiteng and
Alinejad-Rokny, Hamid and
Song, Yuanfeng and
Jing, Kun and
Zhang, Chen Jason and
Yang, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.252/",
pages = "5117--5129",
ISBN = "979-8-89176-395-1",
abstract = "Evaluating code large language models (Code LLMs) requires reliable detection of data leakage, where benchmark performance is artificially inflated by exposure to benchmark data during pre-training. Existing approaches either assume access to proprietary training corpora, rely on brittle heuristics such as timestamp filtering, or use external reference sets with manually tuned, non-generalizable thresholds. To address these limitations, we introduce \textbf{SrDetection}, a unified \textbf{s}elf-\textbf{r}eferential leakage detection framework for both gray-box (access to model logits) and black-box (access to model outputs) settings. SrDetection generates semantically equivalent variants of a benchmark sample and detects leakage by contrasting the model{'}s behavior on the original versus its variants, flagging cases where the original is disproportionately easier for the model. We further design a controlled leakage detection testbed and evaluate SrDetection in this environment. Across different models and training stages, SrDetection improves average F1 by 21.52 points in the gray-box setting and 14.46 points in the black-box setting over strong baselines, demonstrating robust, threshold-independent leakage detection. Finally, a gray-box study of 15 widely used Code LLMs on four popular benchmarks reveals benchmark-specific leakage patterns beyond prior overlap-based analyses[Source code and data are available at {\ensuremath{<}}https://github.com/SMinL/SrDetectionCode{\ensuremath{>}}]."
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<abstract>Evaluating code large language models (Code LLMs) requires reliable detection of data leakage, where benchmark performance is artificially inflated by exposure to benchmark data during pre-training. Existing approaches either assume access to proprietary training corpora, rely on brittle heuristics such as timestamp filtering, or use external reference sets with manually tuned, non-generalizable thresholds. To address these limitations, we introduce SrDetection, a unified self-referential leakage detection framework for both gray-box (access to model logits) and black-box (access to model outputs) settings. SrDetection generates semantically equivalent variants of a benchmark sample and detects leakage by contrasting the model’s behavior on the original versus its variants, flagging cases where the original is disproportionately easier for the model. We further design a controlled leakage detection testbed and evaluate SrDetection in this environment. Across different models and training stages, SrDetection improves average F1 by 21.52 points in the gray-box setting and 14.46 points in the black-box setting over strong baselines, demonstrating robust, threshold-independent leakage detection. Finally, a gray-box study of 15 widely used Code LLMs on four popular benchmarks reveals benchmark-specific leakage patterns beyond prior overlap-based analyses[Source code and data are available at \ensuremath<https://github.com/SMinL/SrDetectionCode\ensuremath>].</abstract>
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%0 Conference Proceedings
%T SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models
%A Li, Shuaimin
%A Fan, Liyang
%A li, Zeyang
%A Wan, Zhuoyue
%A Lin, Yufang
%A Ni, Shiwen
%A Fang, Feiteng
%A Alinejad-Rokny, Hamid
%A Song, Yuanfeng
%A Jing, Kun
%A Zhang, Chen Jason
%A Yang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-srdetection
%X Evaluating code large language models (Code LLMs) requires reliable detection of data leakage, where benchmark performance is artificially inflated by exposure to benchmark data during pre-training. Existing approaches either assume access to proprietary training corpora, rely on brittle heuristics such as timestamp filtering, or use external reference sets with manually tuned, non-generalizable thresholds. To address these limitations, we introduce SrDetection, a unified self-referential leakage detection framework for both gray-box (access to model logits) and black-box (access to model outputs) settings. SrDetection generates semantically equivalent variants of a benchmark sample and detects leakage by contrasting the model’s behavior on the original versus its variants, flagging cases where the original is disproportionately easier for the model. We further design a controlled leakage detection testbed and evaluate SrDetection in this environment. Across different models and training stages, SrDetection improves average F1 by 21.52 points in the gray-box setting and 14.46 points in the black-box setting over strong baselines, demonstrating robust, threshold-independent leakage detection. Finally, a gray-box study of 15 widely used Code LLMs on four popular benchmarks reveals benchmark-specific leakage patterns beyond prior overlap-based analyses[Source code and data are available at \ensuremath<https://github.com/SMinL/SrDetectionCode\ensuremath>].
%U https://aclanthology.org/2026.findings-acl.252/
%P 5117-5129
Markdown (Informal)
[SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models](https://aclanthology.org/2026.findings-acl.252/) (Li et al., Findings 2026)
ACL
- Shuaimin Li, Liyang Fan, Zeyang li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, and Min Yang. 2026. SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5117–5129, San Diego, California, United States. Association for Computational Linguistics.