Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices

2024/03/19 by Sara Abdali, Abdali, Sara, Richard Anarfi +6 · 9 citations
Computer Science · Social Sciences · #Access Control and Trust #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #Digital and Cyber Forensics #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2403.12503

openalex publication_date 2024/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• HATS: High-Accuracy Triple-Set Watermarking for Large Language Models
• Toward Understanding Security Issues in the Model Context Protocol Ecosystem
• Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding
• LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
• Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential
• Differential Robustness in Transformer Language Models: Empirical Evaluation Under Adversarial Text Attacks
• Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment
• Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding
• The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation

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