Researchers reveal supply-chain attacks can steal secrets from local LLM fine-tuning

Researchers have developed a novel method to steal sensitive information from locally fine-tuned large language models by exploiting vulnerabilities in their supply chain code. This technique moves beyond passive weight poisoning to active execution hijacking, enabling the model to memorize and leak specific secrets like API keys or personal identifiers. The attack achieves over 98% accuracy in stealing secrets without degrading the model's primary function and bypasses common defenses such as DP-SGD and code auditing. AI

IMPACT New attack vector demonstrates a significant supply-chain risk for LLM fine-tuning, potentially impacting data security and privacy.

RANK_REASON Academic paper detailing a new attack vector on LLM fine-tuning.

Read on arXiv cs.AI →

• API keys
• arXiv
• DP-SGD

• paper
• safety

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