The Midas Touch: Triggering the Capability of LLMs for RM-API Misuse Detection
Yi Xian Yang, Jinghua Liu, Kai Chen, Miaoqian Lin · 2025
detectors.By employing a static detector CodeQL, we ethically report 115 security bugs on the applications integrating on six popular libraries to the developers, which may result in severe issues, such as Denial-of-Services (DoS) and memory corruption.Compared with the end-to-end benchmark method, the result shows that ChatDetector can retrieve at least 47% more RM sentences and 80.85% more RM-API constraints.Since no work exists specified in utilizing LLMs for RM-API misuse detection to our best knowledge, the inspiring results show that LLMs can assist in generating more constraints beyond expertise and can be used for bug detection.It also indicates that future research could transfer from overcoming the bottlenecks of traditional NLP tools to creatively utilizing LLMs for security research.