3 citations · 3 across the 11 of their papers we have counts for
14 papers
Agentic AI Defense: Decode-Time Provenance Binding for MCP
Eric Yocam, Varghese Vaidyan, Paris Kalathas +3
Agentic AI systems using the Model Context Protocol (MCP) are vulnerable to indirect prompt injection through untrusted tool outputs. We demonstrate cross-modal trust laundering, i…
AI Security Literacy: Ecosystem-based Competency Framework and K-12 Learning Platform
Eric Yocam, Varghese Vaidyan, Paris Kalathas
Adoption of artificial intelligence in K-12 schools has outpaced guidance for students, teachers, and administrators. Existing AI-literacy frameworks emphasize understanding and us…
Human Driver Temperament and the Safety Impact of a C-V2X Denial-of-Service Flooding Attack in Mixed-Autonomy Traffic
Rasheed Bello, Gurcan Comert, Varghese Vaidyan +3
Cooperative and connected automated vehicles (CAVs) rely on Signal Phase and Timing (SPaT) messages to cross signalized intersections; a denial-of-service (DoS) flood that blocks S…
NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment
Rasheed Bello, Arthur Mukwaya, Gurcan Comert +5
Connected-vehicle safety evaluations rely on coupled traffic and network simulations, but standard channel models ignore radio resource competition in 5G NR sidelink Mode-2, report…
Side-Channel Attacks Survive Noise Cancellation in 3D Printers
Eric Yocam, Varghese Vaidyan, Micah Flack +2
Active Motor Noise Cancellation (AMNC) is a noise-reduction feature shipped in commercial fused deposition modeling (FDM) 3D printers. Because it suppresses the acoustic emissions…
Bridging the Smart City Cybersecurity Data Gap Through AI-Driven Synthetic Dataset Generation
Stephanie Polczynski, John D. Hastings, Varghese Vaidyan +1
Smart cities rely on interconnected cyber-physical systems that integrate sensors, IoT devices, cloud platforms, and AI-driven services and decision-making. While these systems enh…
Superspace Concentration and Adversarial Robustness in Quantum Algorithms
Eric Yocam, Christian Yocam, Varghese Vaidyan +3
We study superspace concentration as a quantum resource, formalized through the focus measure F(\r{ho}) = λ_max(\r{ho}_super) - the largest eigenvalue of the reduced superspace sta…
Context-Based Adversarial Attacks on AI Code Generators: Vulnerability Analysis and Implications
Walther A. Del Orbe, John D. Hastings, Varghese Vaidyan
AI-powered code generation systems have transformed software development but introduce critical inference-time security vulnerabilities. This research presents a systematic investi…
TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis
Eric Yocam, Varghese Vaidyan
Electromagnetic (EM) side-channel analysis traditionally assumes a stationary, close-proximity probe - a threat model that underestimates aerial adversaries. TriSweep is a simulati…
Beyond Collection: Measuring the Detection Efficacy of Modern Security Logging Standards
Ryan Holeman, John Hastings, Varghese Mathew Vaidyan
Effective security logging is crucial for the timely and accurate detection of cyber threats; however, the relative effectiveness of various industry-standard logging frameworks re…
H-Node Attack and Defense in Large Language Models
Eric Yocam, Varghese Vaidyan, Yong Wang
We present H-Node Adversarial Noise Cancellation (H-Node ANC), a mechanistic framework that identifies, exploits, and defends hallucination representations in transformer-based lar…
Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models
Eric Yocam, Varghese Vaidyan, Gurcan Comert +3
Large Language Models frequently generate fluent but factually incorrect text. We propose Adaptive Activation Cancellation (AAC), a real-time inference-time framework that treats h…
Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities
Eric Yocam, Anthony Rizi, Mahesh Kamepalli +3
As quantum computing continues to advance, the development of quantum-secure neural networks is crucial to prevent adversarial attacks. This paper proposes three quantum-secure des…
Causal Interpretability for Adversarial Robustness: A Hybrid Generative Classification Approach
Chunheng Zhao, Pierluigi Pisu, Gurcan Comert +3
Deep learning-based discriminative classifiers, despite their remarkable success, remain vulnerable to adversarial examples that can mislead model predictions. While adversarial tr…