@inproceedings{wang-etal-2026-cacheprune,
    title = "{C}ache{P}rune: Teaching {LLM}s What Not to Follow via {KV}-Cache Editing",
    author = "Wang, Rui  and
      Wu, Junda  and
      Xia, Yu  and
      Yu, Tong  and
      Zhang, Ruiyi  and
      Rossi, Ryan A.  and
      Mitra, Subrata  and
      Yao, Lina  and
      McAuley, Julian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.70/",
    pages = "1551--1570",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) are susceptible to indirect prompt injection attack, where the model inadvertently responds to instructions injected into the prompt context. This vulnerability stems from LLMs' inability to distinguish between data and instructions within a prompt. We propose CachePrune that defends against this attack by identifying and pruning neurons associated with instruction-following, during KV cache encoding of the prompt context. The pruning steers the LLM toward interpreting the context purely as data rather than as instructions to follow. To identify these neurons, we introduce a neural attribution mechanism guided by a preferential attribution loss, and theoretically connect this loss to an upper bound of the Direct Preference Optimization (DPO) objective. Further, we improve on the fidelity of neural attribution by leveraging an observed triggering effect in instruction-following. Our approach does not interfere with prompt formatting or incur test-time overhead in response generation. Experiments show that CachePrune significantly reduces the attack success rate while preserving the LLM{'}s ability to follow user instructions."
}