As the complexity of cyberattacks continues to increase, multistage combination attacks have become the primary method of attack. Attackers plan and organize a series of attack steps, using various attack tools to achieve specific goals. Extracting knowledge about these tools is of great significance for both defense and tracing of attacks. We have noticed that there is a wealth of security tool‐related knowledge within the open‐source community, but research in this area is limited. It is challenging to achieve large‐scale automated security tool information extraction. To address this, we propose automated knowledge graph construction architecture, named SecTKG, for open‐source security tools. Our approach involves designing a security tool ontology model to describe tools, users, and relationships, which guides the extraction of security tool knowledge. In addition, we develop advanced entity recognition and classification methods, ensuring efficient and accurate knowledge extraction. As far as we know, this work is the first to construct the large‐scale security tool knowledge graph, containing 4 million entities and 10 million relationships. Furthermore, we investigate the tendencies and particularities of security tools based on the SecTKG and developed a security tool influence‐measuring application. The research fills a gap in the field of automated security tools’ knowledge extraction and provides a foundation for future research and practical applications.