Our research explores the fundamental connection between static code representation and runtime behavior. By analyzing Abstract Syntax Tree structures across distributed microservice architectures, we're developing novel methods to extract semantic understanding from API implementations. This approach enables our custom language models to generate precise security test cases that target business logic vulnerabilities—creating a continuous security thread from development to deployment environments that traditional tools cannot achieve.

Building on foundational work like Microsoft's REST-ler studies, we're advancing reinforcement learning techniques to enable autonomous security testing of modern applications. Our research demonstrates that AI agents can learn application behavior through exploratory interaction, allowing them to discover complex attack chains and business logic flaws that rule-based scanners consistently miss. Empirical evaluations show a 4000% increase in vulnerability detection coverage compared to conventional approaches, particularly for privilege escalation and access control vulnerabilities.

We're investigating how large language models can bridge the remediation gap between vulnerability discovery and code correction. Our research demonstrates that by creating direct linkages between runtime security findings and their source code origins, we can reduce remediation friction by 91%. This work addresses one of the most persistent challenges in application security: translating vulnerability information into actionable code changes that developers can implement with minimal disruption.