Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
<|reference_start|>Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey: Recent research demonstrates that the nascent fine-tuning-as-a-service business model exposes serious safety concerns -- fine-tuning over a few harmful data uploaded by the users can compromise the safety alignment of the model. The attack, known as harmful fine-tuning, has raised a broad research interest among the community. However, as the attack is still new, \textbf{we observe from our miserable submission experience that there are general misunderstandings within the research community.} We in this paper aim to clear some common concerns for the attack setting, and formally establish the research problem. Specifically, we first present the threat model of the problem, and introduce the harmful fine-tuning attack and its variants. Then we systematically survey the existing literature on attacks/defenses/mechanical analysis of the problem. Finally, we outline future research directions that might contribute to the development of the field. Additionally, we present a list of questions of interest, which might be useful to refer to when reviewers in the peer review process question the realism of the experiment/attack/defense setting. A curated list of relevant papers is maintained and made accessible at: \url{https://github.com/git-disl/awesome_LLM-harmful-fine-tuning-papers}.<|reference_end|>
@article{huang2024harmful,
    title={Harmful Fine-tuning Attacks and Defenses for Large Language Models: A
  Survey},
    author={Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, Ling Liu},
    journal={arXiv preprint arXiv:2409.18169},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18169},
    primaryClass={cs.CR cs.AI cs.LG}
}