🤖 AI Summary
Static benchmarking inadequately captures LLM security vulnerabilities exposed in real-world online community practices. To address this, we propose a community-driven dynamic monitoring paradigm, focusing on psychological attacks as the primary threat vector, revealing that capability advancement and safety improvement are misaligned. Methodologically, we design a cross-platform data collection system, a multidimensional scoring framework, and a scalable monitoring architecture—integrating horizontal comparative analysis with fine-grained vulnerability classification. Over five months, we conduct an empirical study across nine commercial LLMs. Our approach identifies 198 novel vulnerabilities with 78% classification accuracy; psychological attacks exhibit significantly higher detection rates than traditional exploit-based techniques yet demonstrate low cross-model transferability—highlighting their stealthiness and model specificity. This work pioneers systematic, continuous discovery and quantitative evaluation of community-emergent LLM vulnerabilities.
📝 Abstract
Static benchmarks fail to capture LLM vulnerabilities emerging through community experimentation in online forums. We present PrompTrend, a system that collects vulnerability data across platforms and evaluates them using multidimensional scoring, with an architecture designed for scalable monitoring. Cross-sectional analysis of 198 vulnerabilities collected from online communities over a five-month period (January-May 2025) and tested on nine commercial models reveals that advanced capabilities correlate with increased vulnerability in some architectures, psychological attacks significantly outperform technical exploits, and platform dynamics shape attack effectiveness with measurable model-specific patterns. The PrompTrend Vulnerability Assessment Framework achieves 78% classification accuracy while revealing limited cross-model transferability, demonstrating that effective LLM security requires comprehensive socio-technical monitoring beyond traditional periodic assessment. Our findings challenge the assumption that capability advancement improves security and establish community-driven psychological manipulation as the dominant threat vector for current language models.