While citations are essential to scholarly communication, their role in assessing academic success has contributed to various forms of misuse. Citation cartels and citation padding are examples of such misuse, yielding irrelevant references and corrupting the academic record. Here, we introduce a tool that we developed and use internally for detecting problematic citation behaviors, called Cite Lens (sample code available on GitHub: https://github.com/MDPI-AG/citelens.), which analyzes citations using vector (embedding) similarity. This tool can either detect misalignment between an article and its references (article–reference similarity), or the reference and the paragraph in which it is cited (context–reference similarity). We analyze the citation patterns across multiple publishers and topics and show the capability of this approach to detect problematic citations. This tool aims to support MDPI’s editorial screening and help prevent unethical or manipulative citation behavior.