Advancing AI Security &
Quantum Cyber Defense.
My research investigates the friction points of modern cyber defense: adversarial machine learning in physical-cyber systems, post-quantum cryptographic migration, and automated DevSecOps pipelines.
Agentic AI Security & Governance
Threat modeling autonomous AI agents, evaluating prompt injection, tool-call hijacking, memory poisoning, and establishing multi-tenant LLM isolation boundaries.
Post-Quantum Cryptography (PQC)
Architecting enterprise migration paths for NIST-standardized PQC algorithms (ML-KEM/CRYSTALS-Kyber, ML-DSA/Dilithium) into active hybrid TLS pipelines.
DevSecOps & SAST Automation
Designing zero-friction security gates in CI/CD pipelines to catch vulnerabilities in pull requests before reaching production.
Academic & Industry Papers
Selected Publications
Mind the Gap: Exploiting AI Blind Spots in Fused Physical-Cyber Surveillance Systems
Formal analysis of adversarial attack surfaces in AI-augmented physical security systems. Examines model drift, confidence threshold manipulation, sensor-fusion vulnerability vectors, and cross-domain detection failures in critical infrastructure.
A Heuristic Approach to Factoid Question Generation from Sentence
Algorithmic research on automated question generation from unstructured text using syntactic parsing and heuristic semantic transformations.
Vulnerability Recognition
Responsible Disclosures
HTTP Parameter Pollution to XSS
Cross-site scripting escalation in enterprise web application.
Hidden Input Manipulation to Reflected XSS
Bypassed client-side sanitization via DOM reflection.
Contextual Reflected XSS
Exploited unescaped reflection in search endpoint.
Responsible Disclosure Citation
Identified and remediated critical security vulnerability in university infrastructure.
Collaborate on Research
Interested in joint research on AI agent threat modeling, Post-Quantum Cryptography transition, or DevSecOps automation?
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