Prior work on adversarial example generation with syntactically controlled paraphrase networks requires large-scale paraphrase parallel corpora to train models. The performance of the model is seriously limited by the domain and scale of paraphrase parallel corpus. To solve this problem, this paper proposes an unsuprervised syntactically controlled paraphrase model to generate adversarial examples which only needs monolingual data. Specifically, variational autoencoder is used to learn model, which maps a sentence and a syntactic parse tree into semantic and syntactic variables, respectively. By learning to reconstruct the input sentence from syntactic and semantic variables, the model effectively learns to generate syntactic paraphrases without using any parallel data. Experiment results on unsupervised sentence paraphrasing and adversarial example generation demonstrate that the proposed model achieves new state-of-the-art results on unsupervised paraphrase generation and generate effective adversarial examples. These examples can be used to improve the robustness and generalization of NLP (natural language processing) model.