The goal of this dissertation is to support developers in applying security checks using community knowledge. Artificial intelligence approaches combined with natural language processing techniques are employed to identify security-related information from community websites such as Stack Overflow or Git Hub. All security-related information is stored in a security knowledge base. This knowledge base provides code fragments that represent the community's knowledge about vulnerabilities, security-patches, and exploits. Comprehensive knowledge is required to carry out security checks on software artifacts, such as data covering known vulnerabilities and their manifestation in the source code as well as possible attack strategies. Approaches that check software libraries and source code fragments are provided for the automated use of the data. Insecure software libraries can be detected using the NVD combined with metadata and library file hash approaches introduced in this dissertation. Vulnerable source code fragments can be identified using community knowledge represented by code fragments extracted from the largest coding community websites: Stack Overflow and Git Hub. A state-of-the-art clone detection approach is modified and enriched by several heuristics to enable vulnerability detection and leverage community knowledge while maintaining good performance. Using various case studies, the approaches implemented in Eclipse plugins and a JIRA plugin are adapted to the users' needs and evaluated.