Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective
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 numerica...
| Authors | Meifang Chen, Zhe Yang, Huang Nianchen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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