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20242026
most citedExposing LLM Vulnerabilities: Adversarial Scam Detection and Performance

2 citations · 4 across the 12 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.SI2026

Assessing Socio-Cyber Vulnerability Using Survey and Social Media Data

Shutonu Mitra, Qi Zhang, Tomas Neguyen +6

The rapid growth of social media participation has increased exposure to socially engineered cyber threats (e.g., phishing, romance fraud, and tech-support scams), yet prevailing a…

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.CL2026

CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety

Heajun An, Qi Zhang, Vedanth Achanta +1

Large language models (LLMs) are increasingly embedded in adolescent digital environments, mediating information seeking, advice, and emotionally sensitive interactions. Yet existi…

cs.HC2026★ 1 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.CR2026

Grounded but Misleading: Evaluating Semantic Alignment in AI-Generated Security Explanations

Heajun An, Connor Ng, Sandesh Sharma Dulal +2

Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky. However,…