Author

Harsh Kasyap

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Preprint Aug 2026

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors

A critical vulnerability is demonstrated by introducing a more potent white-box, gradient-regularized evasion attack framework that systematically crushes target feature attribution to near-zero, maintains>90% Attack Success Rates, and fundamentally bypasses Conditional Anomaly Detection.

Niraj Kumar, Harsh Kasyap · 0 citations

Preprint Aug 2026

Breaking the Assumptions: Auditing Input-Side Jailbreak Defenses Against Semantic Attacks

This paper does an audit of defense mechanisms under jailbreak attacks on locally deployed models by tracing each failure back to the specific assumption it relies on.

Aaditya Pratap, Harsh Kasyap, Somanath Tripathy · 0 citations

Preprint Jul 2026

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

This work proposes PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation, and improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.

Harsh Kasyap, Anil Kumar Pradhan, U. Atmaca et al. · 0 citations