Recent advancements in language model technology have significantly enhanced the ability to edit factual information. Yet, the modification of moral judgments—a crucial aspect of aligning models with human values—has garnered less attention. In this work, we introduce COUNTERMORAL, a novel dataset crafted to assess how well current model editing techniques modify moral judgments across diverse ethical frameworks. We apply various editing techniques to multiple language models and evaluate their performance. Our findings illuminate significant insights and challenges, paving the way for future research in developing ethically aligned language models.