Neeraj Karamchandani
Security researcher building offensive tools for AI and distributed systems.
Ph.D. candidate at Penn State focused on adversarial ML, agentic AI security, federated learning and vulnerablity research. I also help teach computer + network security and system courses, and have been recognized with multiple Best TA awards.
Research Interests
Selected Work
Novel memory-poisoning attacks targeting LLM-based agents with persistent memory, RAG, and tool use. Includes a new architectural defense evaluated across clinical, QA, and e-commerce domains.
Red-team framework for stealthy control of backdoors in federated learning, demonstrating evasion of modern aggregation defenses.
Content moderation and privacy in GANs using text prompts, enabling concept unlearning without annotated data. (Second Author)
Experience
Ph.D. research in offensive security for ML systems. Taught and TA'd courses in computer/network security (CSE 543, CMPSC 443), system programming, data structures, and discrete mathematics for classes of 400+ students. Best TA award 2021–22 and 2024–25.
Web3 security research: vulnerability detection, threat modeling, adversary emulation, and smart contract static analysis. Built the company's first real-time attack detection pipeline.
Built a mutation-based fuzzing framework for IoT protocol testing, discovering DoS, buffer overflow, and session hijacking vulnerabilities.