PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models
This addresses the need for socio-technical monitoring in LLM security, revealing that capability advancement does not necessarily improve security, though it is incremental in applying existing methods to new community-driven data.
The paper tackled the problem of LLM vulnerabilities emerging from community experimentation by introducing PrompTrend, a system for continuous monitoring and assessment, which collected 198 vulnerabilities over five months and found that psychological attacks outperform technical exploits with 78% classification accuracy.
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.