LLMSafeGuard: A Training-Free Framework for Safeguarding LLM Decoding via Context-Wise Similarity Validation

Abstract

Large Language Models (LLMs) have significantly advanced natural language processing (NLP) tasks, but also pose ethical and societal risks due to their propensity to generate harmful content. Existing methods have limitations, including the need for training specific control models and proactive intervention during text generation, which leads to quality degradation and increased computational overhead. To mitigate those limitations, we propose LLMSafeGuard, a lightweight real-time framework that integrates an external validator into decoding, rejecting unsafe outputs while allowing valid ones. We introduce a similarity-based validation approach, simplifying safety constraint validation and eliminating the need for external control model training. Additionally, LLMSafeGuard employs a context-wise timing selection strategy, which allows LLMSafeGuard to intervene in LLMs only when necessary. We evaluate LLMSafeGuard on three tasks: detoxification, copyright safeguarding, and jailbreak attack defending across six models, and the results demonstrate its superiority over SOTA baselines on all tasks. For instance, in detoxification, LLMSafeGuard reduces toxic output by at least 38.6% while preserving linguistic quality. For Jailbreak attack defending, LLMSafeGuard reduces the harmful score of the response LLMs under jailbreak attack at least by 11.4% compared to the best baseline. Additionally, its context-wise timing selection achieves a speedup of 1.7X over per-step validation, without compromising effectiveness.