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20242026
most citedExposing LLM Vulnerabilities: Adversarial Scam Detection and Performance

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

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

Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance

Anuridhi Gupta, Samara Mansoor, Hemant Purohit

Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and function…

cs.CR2025

Scam Shield: Multi-Model Voting and Fine-Tuned LLMs Against Adversarial Attacks

Chen-Wei Chang, Shailik Sarkar, Hossein Salemi +7

Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical Scam Detection System (HSDS) that…

cs.CR2025

SCVI: Bridging Social and Cyber Dimensions for Comprehensive Vulnerability Assessment

Shutonu Mitra, Tomas Neguyen, Qi Zhang +8

The rise of cyber threats on social media platforms necessitates advanced metrics to assess and mitigate social cyber vulnerabilities. This paper presents the Social Cyber Vulnerab…

cs.CR2024★ 2 cited

Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance

Chen-Wei Chang, Shailik Sarkar, Shutonu Mitra +7

Can we trust Large Language Models (LLMs) to accurately predict scam? This paper investigates the vulnerabilities of LLMs when facing adversarial scam messages for the task of scam…