Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning
New research shows open-weight LLMs can harbour dormant adversarial behaviours activated by routine fine-tuning on benign data, undermining a core safety assumption of model customisation workflows.
Summary written by editorial AI · Source link below
arXiv:2505.16567v4 Announce Type: replace-cross Abstract: Finetuning open-weight Large Language Models (LLMs) is standard practice for achieving task-specific performance improvements. Until now, finetuning has been regarded as a controlled and secure process in which training on benign datasets leads to predictable behaviors. In this paper, we demonstrate, for the first time, that an adversary can create compromised LLMs that are performant and benign, yet exhibit adversarial behaviors once fi
Editorial Analysis
Enterprises customising open-weight LLMs face a hidden supply-chain risk: adversarial behaviours planted upstream can survive and activate during innocuous fine-tuning.
Mandate adversarial safety testing both before and after any fine-tuning of open-weight models destined for production.
Fine-tuning open AI models on safe data can still trigger pre-planted malicious behaviours, adding a new dimension to AI supply-chain risk.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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