Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

2024-03-19

arXiv.org

AI Summary

This paper addresses the growing security and privacy risks associated with large language models (LLMs). It systematically identifies five core risk dimensions: vulnerability to adversarial attacks, malicious misuse, data leakage, hallucination-induced threats, and inadequate regulatory alignment. Methodologically, the study proposes a novel five-dimensional unified risk taxonomy, integrating threat modeling, adversarial case analysis, mitigation strategy efficacy evaluation, and cross-study comparative analysis to expose fundamental limitations of existing defenses. Key contributions include: (1) a structured LLM security risk taxonomy; (2) classification of 12 representative attack patterns; (3) delineation of applicability boundaries for seven critical mitigation strategies; and (4) formulation of verifiable, scalable risk mitigation principles. The findings provide both theoretical foundations and actionable guidance for industrial security practices and AI governance standardization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations. Nevertheless, alongside their remarkable utility, LLMs introduce critical security and risk considerations. These challenges warrant careful examination to ensure responsible deployment and safeguard against potential vulnerabilities. This research paper thoroughly investigates security and privacy concerns related to LLMs from five thematic perspectives: security and privacy concerns, vulnerabilities against adversarial attacks, potential harms caused by misuses of LLMs, mitigation strategies to address these challenges while identifying limitations of current strategies. Lastly, the paper recommends promising avenues for future research to enhance the security and risk management of LLMs.

Problem

• Investigates security and privacy concerns in Large Language Models
• Examines vulnerabilities against adversarial attacks on LLMs
• Proposes mitigation strategies for LLM misuse and limitations

Innovation

• Investigates LLM security from five perspectives
• Analyzes vulnerabilities against adversarial attacks
• Proposes mitigation strategies and future research