Yeting Li

Yeting Li is a researcher at the Institute of Information Engineering, Chinese Academy of Sciences, with academic affiliation at the University of Chinese Academy of Sciences. Their work bridges software security and artificial intelligence, focusing on practical vulnerabilities in modern systems.

Research spans vulnerability analysis in Kubernetes ecosystems, AI-driven binary similarity detection, and semantic-enhanced static analysis for baseband firmware. Recent work explores large language models for security applications including fuzz driver generation and data contamination mitigation in benchmarking, alongside accessibility-focused testing for speech recognition systems.

Publications reveal a clear trajectory toward integrating AI with traditional security analysis, particularly in containerized environments and binary code analysis. Emerging themes include LLM-based tooling for vulnerability identification and specialized testing methodologies for emerging technologies like automatic speech recognition and deep learning operators.

Key contributions include Kubernetes resource injection vulnerability studies, Aster for stutterer accessibility testing, and ACETest for deep learning operator validation. Research demonstrates consistent focus on empirical evaluation of security tools across ASE, ICSE, and ISSTA venues from 2023-2025.