Confidential information firewalling with text classifier is to identify the text containing confidential information whose publication might be harmful to national security, business trade, or personal life. Traditional methods, e.g., listing a set of suspicious keywords together with regular-expression based filter, fail to solve the multi-topic phenomenon, i.e., one text containing the confidential information with different topics. In this paper, we propose a semi-supervised method, CIDetector, for multi-topic confidential information detection. We introduce coarse confidential polarity as prior knowledge into word embeddings, which can regularize the distribution of words to have a clear task classification boundary. Then we introduce a multi-attention network classifier to extract task-related features and model dependencies between features for multi-topic classification. Experiments are conducted by real-world data from WikiLeaks and demonstrated the superiority of our proposed method.