@inproceedings{liang-liao-2026-ls,
    title = "{LS}-Guard: Adaptive Safety Guardrails Tailored to Individual {LLM}s",
    author = "Liang, Jinggui  and
      Liao, Lizi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.findings-acl.989/",
    pages = "19759--19772",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) excel at diverse tasks, but remain vulnerable to malicious inputs such as jailbreak attacks. Current one-size-fits-all safety guardrails built from static datasets ignore each model{'}s unique safety profile and often force trade-offs between safety and utility. To address this gap, we propose LS-Guard, a framework for learning model-specific guardrails tailored to each LLM{'}s vulnerabilities. LS-Guard operates in two stages: First, it dynamically profiles a given LLM by probing it with malicious prompts to elicit the model{'}s responses, which are then dynamically labeled to reveal model-specific failure modes. Second, it uses this data to train a safety classifier with a collaborative multi-LoRA architecture. An orthogonality-constrained multi-task loss enables a central expert to learn general safety features while each subject-specific expert encodes the distinctive vulnerability patterns of one LLM. During inference, LS-Guard activates the central expert together with its model-specific expert to perform content moderation, yielding reliable safety decisions. Extensive experiments on multiple real-world LLMs demonstrate that LS-Guard significantly outperforms strong baseline guardrails, achieving superior robustness, adaptability, and generalization."
}