collaborators

6 papers

cs.CL2026

Ben-Misandry: A Bengali Dataset and Late-Fusion Transformer Framework for Detecting Anti-Male Gender Hate

Md Mehedi Hasan Nipu, Chinmoy Mitra, Tarannum Ahmed Nowshin +1

Gender based hate speech research in Bengali has focused almost exclusively on misogyny, leaving anti male hate, or misandry, entirely unstudied for this language and creating a sy…

cs.GT2026

Trust-Gated Capability Control: Breaking the Trust-Vulnerability Paradox in Multi-Agent LLM Systems

Mehedi Hasan Nipu, Chinmoy Mitra, Tarannum Ahmed Nowshin +1

Layered trust models for multi-agent LLM systems remain largely conceptual: they name which dimensions of trust matter but not how layers combine, how their importance is set at ru…

cs.CL2026

Agentic Security: A Systematization of Tools, Failure Modes, and Design Laws for LLM-Driven Penetration Testing

Israt Moyeen Noumi, Tarannum Ahmed Nowshin, Md. Mehedi Hasan Nipu +3

Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools. As these systems move from demonstrations to deployed products, practitione…

cs.CR2026

ChannelGuard: From Borrowed Safety to Application-Controlled Defenses in Multi-Agent LLMs

Elias Hossain, Md Mehedi Hasan Nipu, Fatema Tuj Johora Faria +4

AI assistants increasingly work as teams, where one model makes a plan, others search the web, use tools, or read stored information, and another writes the final answer. This crea…

cs.AI2026

NEXUS: Structured Runtime Safety for Tool-Using LLM Agents

Elias Hossain, Md Mehedi Hasan Nipu, Tasfia Nuzhat Ornee +2

Tool-using LLM agents increasingly execute high-impact actions, making runtime safety monitoring essential. We present NEXUS (Neural EXecution Utility and Safety), a structured-pla…

cs.AI2025

MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support

Elias Hossain, Md Mehedi Hasan Nipu, Maleeha Sheikh +5

Clinical language models often assign high confidence to incorrect predictions, particularly in high-severity and out-of-distribution cases. We present MedBayes-Lite, a retraining-…