AIBearisharXiv – CS AI · May 17/10
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Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
Researchers demonstrate a novel attack that steals sensitive secrets (API keys, personal identifiers, financial records) from locally fine-tuned language models by embedding malicious code in model architectures. The attack achieves over 98% success rate and bypasses current defense mechanisms including differential privacy and code auditing, exposing a critical supply-chain vulnerability in AI model development.
