I build security controls around AI/ML systems: model supply-chain checks, LLM/agent security monitoring, adversarial robustness experiments, dataset-poisoning detection, model privacy evaluation, and secure ML serving.
| Project | What it demonstrates | Status |
|---|---|---|
| hf-model-provenance-scanner | Hugging Face model provenance, impersonation, pickle-risk, and supply-chain signal checks. | Public repo |
| mcp-security-gateway-monitor | MCP tool-call monitoring for prompt injection, PII leakage, shadow servers, and exfiltration patterns. | Public repo |
| llm-redteam-framework | Adversarial prompt generation and offline detector experiments for LLM red-team workflows. | Public repo |
| dataset-poisoning-detector | Dataset poisoning and anomalous-sample detection experiments. | Public repo |
| model-privacy-attacks | Membership-inference and model-privacy attack evaluation. | Public repo |
| adversarial-ml-lab | FGSM, PGD, and C&W adversarial ML attacks plus adversarial-training defenses. | Prototype |
| PulseNet-RUL-Forecasting | RUL forecasting and FDIA attack verification. | Public repo |
- Model supply chain: provenance checks, pickle-risk detection, typosquat/homoglyph signals, SBOM-oriented workflows
- LLM and agent security: prompt-injection monitoring, tool-call inspection, exfiltration and PII-leak detection
- Adversarial ML: FGSM, PGD, C&W attacks; adversarial training; CI-oriented robustness checks
- Data security: dataset-poisoning detection and per-sample anomaly attribution
- Secure serving: JWT auth, RBAC, rate limiting, audit logging, Prometheus metrics
- Stack: Python 3.11+, PyTorch, scikit-learn, FastAPI, Docker, GitHub Actions
Cybersecurity Innovation Researcher - TEM 598 Technology Innovation Lab, Arizona State University x Honeywell Aerospace Innovation Hub. Contributed to a graduate research practicum exploring AI and cybersecurity challenges for aerospace systems.
- Personalized E-learning System Using Reinforcement Learning Through Satellite
IEEE Xplore, 2024: https://ieeexplore.ieee.org/document/10440852 - Smart Charge Pro Empowering Future Mobility With Advanced Safety And Efficiency In Electric Vehicle Charging Infrastructure
IOSR Journal of Computer Engineering, 2023: https://www.iosrjournals.org/iosr-jce/pages/25(4)Series-1.html
- Location: Greater Phoenix Area, AZ
- Visa: F-1 OPT; H-1B sponsorship needed
- Target roles: ML Security Engineer, AI Security Engineer, Applied ML Security Engineer
- Email: poojakiranbhardwaj@gmail.com
- GitHub: github.com/poojakira
- LinkedIn: linkedin.com/in/poojakiran
Last updated: July 2026. Claims are limited to public, inspectable work.
Verified profile-maintenance path uses RUNBOOK.md. Minimal validation:
py -3.12 -m pip install -r requirements-dev.txt
py -3.12 -m pytest tests -qScope note: this profile README links to public work; claims should remain limited to inspectable repositories and verified artifacts.