Text detoxification is the task of transforming offensive or toxic content into a non-offensive form while preserving the original meaning. Despite increasing research interest in detoxification across various languages, no resources or benchmarks exist for Hebrew, a Semitic language with unique morphological, syntactic, and cultural characteristics. This paper introduces HeDetox, the first annotated dataset for text detoxification in Hebrew. HeDetox contains 600 sentence pairs, each consisting of an offensive source text and a non-offensive text rewritten with LLM and human intervention. We present a detailed dataset analysis and evaluation showing that the dataset benefits offensive language detection. HeDetox offers a foundational resource for Hebrew natural language processing, advancing research in offensive language mitigation and controllable text generation.