ConceptGuard: Benchmarking Context-Sensitive Unlearning in Large Language Models

Large Language Models (LLMs) often require selective removal of harmful or sensitive knowledge, called unlearning, yet existing benchmarks evaluate this capability incompletely. They rely on disjoint forget and retain sets of independent facts, and measure success by simple factual recall. This misses a key requirement of unlearning from a safety perspective, namely eliminating harmful behaviours while preserving benign and beneficial knowledge. We argue that effective unlearning must operate at the level of concepts, removing unsafe applications while maintaining correct and useful usage. To evaluate this, we operationalize dual-use concepts, i.e., concepts that can be used in both harmful and benign contexts, and construct ConceptGuard, a benchmark of 2,583 harmful--benign pairs over 68 concepts, where forget and retain sets are explicitly paired in concept usage. Evaluation is intent-sensitive and centres on contextual separation, which measures how well a model suppresses harmful behaviour while preserving benign usage of the same concept. Across four unlearning methods and four models, we find that current techniques reduce harmful memorization but show strong forgetting--utility trade-offs and limited contextual separation, well below a retain-only reference. Separation trends also depend more on the concept and model than on the method. Our results point to the need for unlearning approaches that better align with real-world safety requirements. Our dataset is publicly available.