5 papers
Agents at Risk: How Users Unwittingly Undermine LLM Safety
Fengchao Chen, Tingmin Wu, Van Nguyen +2
Large language model (LLM)-based agents are increasingly deployed in applications, such as trip-planning agents and web-use agents, to perform complex planning and execution tasks.…
SoK: Exposing the Generation and Detection Gaps in LLM-Generated Phishing
Fengchao Chen, Tingmin Wu, Van Nguyen +1
Phishing campaigns involve adversaries masquerading as trusted vendors trying to trigger user behavior that enables them to exfiltrate private data. While URLs are an important par…
Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecasting
Hua Ma, Ruoxi Sun, Minhui Xue +4
Accurate time-series forecasting is increasingly critical for planning and operations in low-carbon power systems. Emerging time-series large language models (TS-LLMs) now deliver…
MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection
Van Nguyen, Surya Nepal, Xingliang Yuan +3
Software vulnerabilities (SVs) pose a critical threat to safety-critical systems, driving the adoption of AI-based approaches such as machine learning and deep learning for softwar…
PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models
Fengchao Chen, Tingmin Wu, Van Nguyen +3
Phishing remains a pervasive cyber threat, as attackers craft deceptive emails to lure victims into revealing sensitive information. While Artificial Intelligence (AI), in particul…