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- Vol. 5 Issue 2
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AI-Powered Vulnerability Scanner for Spring Boot Applications
Authors
1 2 3 4 Department of Computer Engineering, M. E. S. Wadia College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India. 5 Professor, Department of Computer Engineering, M. E. S. Wadia College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
Article Information
- Published In
- Volume 5, Issue 2 (2026)
- Published Online
- May-August 2026
- Pages
- 741-752
- Article Type
- Original Article
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Abstract
View PDFModern web-based applications built with Spring Bootframework are widely used, but they are still vulnerable to common configuration and coding mistakes. We present an AI-assisted scanner that combines static rule-based analysis with lightweight dynamic API probes and attaches concise AI explanations for each finding. The system runs locally by default using a WebLLM (WebGPU-enabled) model for privacy; a cloud model may be used optionally for short, redacted snippets when higher-quality reasoning is required. We describe detector design, runtime probes, the explanation pipeline, and a prototype implementation. An initial evaluation on small Spring Boot samples demonstrates the workflow and trade-offs. The focus is practical developer help and privacy-aware explainability rather than claiming enterprise-grade coverage.
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