Browser extensions are widely used by millions of users. However, large amount of extensions can be downloaded from webstores without sufficient trust or safety scrutiny, which keeps users from differentiating benign extensions from malicious ones. In this paper, we propose an aspect-level behavior clustering approach to enhancing the safety management of extensions. We decompose an extension's runtime behavior into several pieces, denoted as AEBs (Aspects of Extension Behavior). Similar AEBs of different extensions are grouped into an "AEB cluster" based on subgraph isomorphism. We then build profiles of AEB clusters for both extensions and categories (of extensions) to detect suspicious extensions. To the best of our knowledge, this is the first study to do aspect-level extension clustering based on runtime behaviors. We evaluate our approach with more than 1,000 extensions and demonstrate that it can effectively and efficiently detect suspicious extensions.