@inproceedings{chen-etal-2026-understanding,
title = "Understanding Secret Leakage Risks in Code {LLM}s: A Tokenization Perspective",
author = "Chen, Meifang and
Yang, Zhe and
Nianchen, Huang and
Huang, Yizhan and
LI, Yichen and
Li, Zihan and
Lyu, Michael R.",
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.6/",
pages = "108--119",
ISBN = "979-8-89176-395-1",
abstract = "Code secrets are sensitive assets for software developers, and their leakage poses significant cybersecurity risks. While the rapid development of AI code assistants powered by Code Large Language Models (CLLMs), CLLMs are shown to inadvertently leak such secrets due to a notorious memorization phenomenon. This study first reveals that Byte-Pair Encoding (BPE) tokenization leads to unexpected behavior of secret memorization, which we term as \textit{gibberish bias}. Specifically, we identified that some secrets are among the easiest for CLLMs to memorize. These secrets yield high character-level entropy, but low token-level entropy. Then, this paper supports the biased claim with numerical data. We identified that the roots of the bias are the token distribution shift between the CLLM training data and the secret data. We further discuss how gibberish bias manifests under the ``larger vocabulary'' trend. To conclude the paper, we discuss potential mitigation strategies and the broader implications on current tokenizer design."
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<abstract>Code secrets are sensitive assets for software developers, and their leakage poses significant cybersecurity risks. While the rapid development of AI code assistants powered by Code Large Language Models (CLLMs), CLLMs are shown to inadvertently leak such secrets due to a notorious memorization phenomenon. This study first reveals that Byte-Pair Encoding (BPE) tokenization leads to unexpected behavior of secret memorization, which we term as gibberish bias. Specifically, we identified that some secrets are among the easiest for CLLMs to memorize. These secrets yield high character-level entropy, but low token-level entropy. Then, this paper supports the biased claim with numerical data. We identified that the roots of the bias are the token distribution shift between the CLLM training data and the secret data. We further discuss how gibberish bias manifests under the “larger vocabulary” trend. To conclude the paper, we discuss potential mitigation strategies and the broader implications on current tokenizer design.</abstract>
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%0 Conference Proceedings
%T Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective
%A Chen, Meifang
%A Yang, Zhe
%A Nianchen, Huang
%A Huang, Yizhan
%A LI, Yichen
%A Li, Zihan
%A Lyu, Michael R.
%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 chen-etal-2026-understanding
%X Code secrets are sensitive assets for software developers, and their leakage poses significant cybersecurity risks. While the rapid development of AI code assistants powered by Code Large Language Models (CLLMs), CLLMs are shown to inadvertently leak such secrets due to a notorious memorization phenomenon. This study first reveals that Byte-Pair Encoding (BPE) tokenization leads to unexpected behavior of secret memorization, which we term as gibberish bias. Specifically, we identified that some secrets are among the easiest for CLLMs to memorize. These secrets yield high character-level entropy, but low token-level entropy. Then, this paper supports the biased claim with numerical data. We identified that the roots of the bias are the token distribution shift between the CLLM training data and the secret data. We further discuss how gibberish bias manifests under the “larger vocabulary” trend. To conclude the paper, we discuss potential mitigation strategies and the broader implications on current tokenizer design.
%U https://aclanthology.org/2026.findings-acl.6/
%P 108-119
Markdown (Informal)
[Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective](https://aclanthology.org/2026.findings-acl.6/) (Chen et al., Findings 2026)
ACL
- Meifang Chen, Zhe Yang, Huang Nianchen, Yizhan Huang, Yichen LI, Zihan Li, and Michael R. Lyu. 2026. Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective. In Findings of the Association for Computational Linguistics: ACL 2026, pages 108–119, San Diego, California, United States. Association for Computational Linguistics.