LS-Guard: Adaptive Safety Guardrails Tailored to Individual LLMs

Large Language Models (LLMs) excel at diverse tasks, but remain vulnerable to malicious inputs such as jailbreak attacks.Current onesize-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 modelspecific 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.1 WARNING: This paper may contain offensive or sensitive content.