Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although "local offline fine-tuning" is often viewed as a privacy boundary, we reveal that compromised model code is sufficient to steal them. Current passive pretrained-weight poisoning attacks, while effective for natural language, fundamentally fail to capture such sparse high-entropy targets due to their reliance on probabilistic semantic prefixes. To bridge this gap, we identify and exploit a practical but overlooked supply-chain vector -- malicious model code camouflaged as standard architectural definitions to realize a paradigm shift from passive weight poi...
| Authors | Zi Li, Tian Zhou, Wenze Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 30 April 2026 |
| Last updated at source | 31 August 2026 |
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