alibaba/nacos
Mar 2026 – Mar 2026
1 Month active
Languages Used
Java
Technical Skills
AI IntegrationAI Security ScanningAI integrationAPI DevelopmentJavaJava Development
qiacheng7
PROFILE
Qiacheng7
Chenxinyu worked on enhancing security and reporting features for the Skill-Scanner component in the alibaba/nacos repository. They integrated Cisco AI Defense to automatically block high and critical risks during skill publishing, and introduced an optional LLM-based semantic scanning capability to detect prompt injection and data exfiltration threats. Using Java and focusing on backend development, Chenxinyu restructured the scanning command interface and configuration, and delivered structured Markdown reporting to improve readability and auditability. The work emphasized robust API development, expanded test coverage, and improved maintainability, resulting in a more secure and observable scanning pipeline tailored for compliance and risk reduction.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
1,755
Activity Months1
Your Network
400 peopleSame Organization
@alibaba-inc.com333
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67
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Work History
March 2026
5 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Security and reporting enhancements for Skill-Scanner in the alibaba/nacos repo. Implemented Cisco AI Defense integration to block high/critical risks in skill publishing, added optional LLM semantic scanning with a new config and scanning command structure, and delivered structured Markdown reporting for scan outputs to boost readability, test coverage, and auditability. Executed across five commits focused on integration, testing, and output formatting, resulting in a more secure, observable, and maintainable scanning pipeline with clear business value for risk reduction and compliance.
5 Commits • 2 Features
Mar 1, 2026Month: 2026-03 — Security and reporting enhancements for Skill-Scanner in the alibaba/nacos repo. Implemented Cisco AI Defense integration to block high/critical risks in skill publishing, added optional LLM semantic scanning with a new config and scanning command structure, and delivered structured Markdown reporting for scan outputs to boost readability, test coverage, and auditability. Executed across five commits focused on integration, testing, and output formatting, resulting in a more secure, observable, and maintainable scanning pipeline with clear business value for risk reduction and compliance.
March 2026
Activity
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Quality Metrics
Correctness88.0%
Maintainability80.0%
Architecture84.0%
Performance80.0%
AI Usage64.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
AI IntegrationAI Security ScanningAI integrationAPI DevelopmentJavaJava DevelopmentSecurity ScanningSoftware DevelopmentUnit Testingbackend development
Repositories Contributed To
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