Syed Bahauddin Alam

This paper demonstrates that neural operators used in digital twins for nuclear systems are highly vulnerable to undetectable, sparse adversarial perturbations, necessitating new robustness guarantees.

The paper introduces Semantic Intent Fragmentation (SIF), an attack class demonstrating that multi-agent AI orchestrators can violate security policies through a composition of individually benign subtasks, even when subtask-level safety checks pass.

The paper demonstrates that fine-tuning safety guard models on benign data can catastrophically collapse their safety alignment, proposing Fisher-Weighted Safety Subspace Regularization (FW-SSR) to actively maintain safety geometry.

This paper addresses the lack of systematic infrastructure for evaluating jailbreak attacks by introducing a large-scale dataset, an automated generation method, and a continuous evaluation metric that surpasses traditional binary scoring.

The paper identifies the Misattribution Gap, showing that memory-layer attacks (Semantic Norm Drift) can mimic model failure in multi-agent AI systems, and proposes novel detection and mitigation techniques.