Security Concerns for Large Language Models: A Survey
May 24, 2025 ยท The Cartographer ยท ๐ Journal of Information Security and Applications
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"Title-pattern auto-detect: Security Concerns for Large Language Models: A Survey"
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Authors
Miles Q. Li, Benjamin C. M. Fung
arXiv ID
2505.18889
Category
cs.CR: Cryptography & Security
Cross-listed
cs.AI
Citations
28
Venue
Journal of Information Security and Applications
Last Checked
2 days ago
Abstract
Large Language Models (LLMs) such as ChatGPT and its competitors have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. This survey provides a comprehensive overview of these emerging concerns, categorizing threats into several key areas: inference-time attacks via prompt manipulation; training-time attacks; misuse by malicious actors; and the inherent risks in autonomous LLM agents. Recently, a significant focus is increasingly being placed on the latter. We summarize recent academic and industrial studies from 2022 to 2025 that exemplify each threat, analyze existing defense mechanisms and their limitations, and identify open challenges in securing LLM-based applications. We conclude by emphasizing the importance of advancing robust, multi-layered security strategies to ensure LLMs are safe and beneficial.
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