most citedLLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

1 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2026

DeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling Dialogue

Qi Zhang, Heajun An, Prakriti Dumaru +4

Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed progression required to conduct a…

cs.HC20261 cited

From Vulnerable to Resilient: Examining Parent and Teen Perceptions on How to Respond to Unwanted Cybergrooming Advances

Xinyi Zhang, Mamtaj Akter, Heajun An +6

Cybergrooming is a form of online abuse that threatens teens' mental health and physical safety. Yet, most prior work has focused on detecting perpetrators' behaviors, leaving a li…

cs.LG2026

StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming

Heajun An, Qi Zhang, Minqian Liu +5

Cybergrooming is an evolving threat to youth, requiring proactive educational interventions. We address this by modeling dialogue progression as a structured planning problem over…

cs.CL20251 cited

LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Minqian Liu, Zhiyang Xu, Xinyi Zhang +8

Recent advancements in Large Language Models (LLMs) have enabled them to approach human-level persuasion capabilities. However, such potential also raises concerns about the safety…

cs.CY2025

Toward Integrated Solutions: A Systematic Interdisciplinary Review of Cybergrooming Research

Heajun An, Marcos Silva, Qi Zhang +8

Cybergrooming exploits minors through online trust-building, yet research remains fragmented, limiting holistic prevention. Social sciences focus on behavioral insights, while comp…