PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models

TL;DR

PrompTrend is a scalable system that monitors and assesses vulnerabilities in large language models by analyzing community-reported exploits, revealing insights into how capabilities and platform dynamics influence security risks.

Contribution

We introduce PrompTrend, a novel framework for continuous, community-driven vulnerability detection and evaluation of large language models, highlighting the importance of socio-technical monitoring.

Findings

Advanced capabilities correlate with increased vulnerabilities.

Psychological attacks outperform technical exploits.

Platform dynamics influence attack effectiveness.

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.