AI Security Researcher • Incoming Cloud Security Engineer @ Wells Fargo • CTF Competitor
I'm a cybersecurity researcher transitioning into AI security research, with professional experience in cloud security, offensive security, and application security. Currently studying at the Judy Genshaft Honors College at the University of South Florida in the Bellini College of Cybersecurity and AI.
I actively compete in national CTFs with USF CyberHerd (Top 5 nationally) and founded the USF Boxing Club, which scaled to over 1,300 members-the largest combat-sports organization on campus.
My transition into AI security is motivated by bringing traditional penetration testing methodology to AI systems-systematically probing where vulnerabilities hide in interactions between ML components and infrastructure.
Current Research Areas:
Python framework automating jailbreak testing on open-source LLMs (Llama 3, Mistral) with granular telemetry capture-token distributions, latency patterns, resource metrics. Developing interactive visualizations of attack surfaces to identify predictable failure modes in smaller fine-tuned models vs. large base models.
Investigating how traditional security vulnerabilities chain with AI-specific weaknesses to create novel attack vectors. Examples: exploiting rate limiting + prompt injection to infer model architecture; chaining authentication bypass + agent tool poisoning to compromise multi-agent systems. Researching whether composite attacks follow predictable patterns that can be systematically enumerated and defended against.
End-to-end deep learning pipeline converting malware binaries into grayscale images for classification. Lightweight CNN achieving 98.90% test accuracy on Malimg dataset. Integrated GradCAM explainability for model interpretation and FGSM adversarial robustness analysis to study failure modes under attack.
Cloud Security Posture Manager detecting and remediating misconfigurations across AWS/GCP with focus on identity misconfiguration detection and visualization. Built for at-scale security posture management in multi-cloud environments.
Production-grade DevSecOps reference implementation with automated build, scan, deploy, and self-healing for containerized workloads on AWS (cloud-agnostic configurable). Integrates Trivy scanning, policy-as-code enforcement, and automated rollback.
Dynamic detection system using Azure Sentinel and KQL to monitor global RDP brute-force attempts with automated remediation workflows. Real-time threat intelligence integration and geolocation-based alerting.
Explore more projects on GitHub →
In Progress: OSCP, AWS Security Specialty, Terraform Associate