AI-Driven Code Security Detection Engine
Design and implement a multi-agent collaborative code auditing system covering vulnerability detection, malicious code identification, and sensitive information leakage scenarios; lead the role decomposition of Planners/Executors/Critics, tool invocation chains, and cross-agent state synchronization mechanism design
Integrate RAG, Chain-of-Thought, Reflection, and other technologies into security audit agents. Continuously optimize detection accuracy and recall rates while establishing a quantifiable evaluation and iteration framework
Deeply integrate with DevSecOps workflows. Develop plugins for mainstream pipelines like GitLab CI/CD, Tekton, and Jenkins to achieve “audit-on-commit.”
AI System Security Protection and Threat Response
Responsible for constructing a security protection framework for large language model applications, covering three dimensions: input layer (prompt injection, jailbreak detection), output layer (sensitive information leakage, compliance auditing), and runtime (tool invocation sandboxing, anomaly behavior circuit breaking)
Develop Agent workflows for automated alert classification, contextual correlation, and false positive filtering. Integrate RAG-driven threat intelligence retrieval to generate automated analysis conclusions, supporting SOAR platform integration
Design human-machine collaboration intervention mechanisms and Agent behavior audit systems to ensure observability, traceability, and intervenability of Agent actions in production environments, adhering to industry standards like the OWASP Top 10 Risks for LLMs
Engineering Development and Platform Services
Construct a highly available, scalable Agent service architecture supporting large-scale concurrent scanning task scheduling and fault tolerance
Oversee standardized API output for detection capabilities, building closed-loop systems for rule management, result visualization, and false positive feedback