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

• URL: http://arxiv.org/abs/2507.19185v1
• Date: Fri, 25 Jul 2025 11:52:46 GMT
• Title: PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models
• Abstract summary: We present PrompTrend, a system that collects vulnerability data across platforms and evaluates them using multidimensional scoring.<n>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.
• Score: 1.03121181235382
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• STRIDE-AI: A Threat Modeling Framework for Generative AI Security Assessment [0.0]
  We present textitSTRIDE-AI, a framework that bridges the gap between high-level risk standards (NIST AI RMF) and technical vulnerability.<n>The framework defines a six-phase assessment lifecycle, introduces a threat modeling adaptation of classical STRIDE for AI systems, and is operationalized through a purpose-built web tool.
  arXiv  Detail & Related papers  (2026-05-16T21:27:23Z)
• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment [13.18964488705143]
  Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats.<n>In this paper, we investigate the intrinsic vulnerabilities of LLMs in cyber threat intelligence (CTI)<n>We introduce a novel categorization methodology that integrates stratification, autoregressive refinement, and human-in-the-loop supervision.
  arXiv  Detail & Related papers  (2025-10-02T00:49:20Z)
• White-Basilisk: A Hybrid Model for Code Vulnerability Detection [50.49233187721795]
  We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance.<n>White-Basilisk achieves results in vulnerability detection tasks with a parameter count of only 200M.<n>This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks.
  arXiv  Detail & Related papers  (2025-07-11T12:39:25Z)
• A Survey on Model Extraction Attacks and Defenses for Large Language Models [55.60375624503877]
  Model extraction attacks pose significant security threats to deployed language models.<n>This survey provides a comprehensive taxonomy of extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks.<n>We examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios.
  arXiv  Detail & Related papers  (2025-06-26T22:02:01Z)
• Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation [69.63626052852153]
  We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems.<n>We also introduce a dataset of 100 indirect harm scenarios, testing models' ability to foresee adverse, non-obvious outcomes from seemingly harmless user prompts.
  arXiv  Detail & Related papers  (2025-06-26T02:28:58Z)
• Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
  This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
  arXiv  Detail & Related papers  (2025-06-11T18:43:51Z)
• Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space [82.75174050101108]
  We introduce LatentSeek, a framework that enhances reasoning through Test-Time Instance-level Adaptation (TTIA) within the model's latent space.<n>LatentSeek is evaluated on a range of reasoning benchmarks, including GSM8K, MATH-500, and AIME2024.<n>Results show that LatentSeek consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-05-19T16:26:02Z)
• HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of Quantization on Model Alignment [1.8843687952462742]
  This paper aims to address gaps in the current literature on jailbreaking techniques and the evaluation of LLM vulnerabilities. Our contributions include the creation of a novel dataset designed to assess the harmfulness of model outputs across multiple harm levels. We provide a comprehensive benchmark of state-of-the-art jailbreaking attacks, specifically targeting the Vicuna 13B v1.5 model.
  arXiv  Detail & Related papers  (2024-11-11T10:02:49Z)
• A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models [9.304845676825584]
  We propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness.
  arXiv  Detail & Related papers  (2024-10-18T23:47:46Z)
• Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection [9.652886240532741]
  This paper thoroughly analyses large language models' capabilities in detecting vulnerabilities within source code. We evaluate the performance of six open-source models that are specifically trained for vulnerability detection against six general-purpose LLMs.
  arXiv  Detail & Related papers  (2024-08-29T10:00:57Z)
• MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks [65.86360607693457]
  No-box attacks, where adversaries have no prior knowledge, remain relatively underexplored despite its practical relevance.<n>This work presents a systematic investigation into leveraging large-scale Vision-Language Models (VLMs) as surrogate models for executing no-box attacks.<n>Our theoretical and empirical analyses reveal a key limitation in the execution of no-box attacks stemming from insufficient discriminative capabilities for direct application of vanilla CLIP as a surrogate model.<n>We propose MF-CLIP: a novel framework that enhances CLIP's effectiveness as a surrogate model through margin-aware feature space optimization.
  arXiv  Detail & Related papers  (2023-07-13T08:10:48Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.