Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mech...

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Bibliographic Details
Main Authors Raheja, Tarun, Pochhi, Nilay
Format Journal Article
LanguageEnglish
Published 08.10.2024
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computation and Language
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Summary:Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mechanisms within the field of Large Language Model (LLM) red-teaming. We analyze various attack methods, including gradient-based optimization, reinforcement learning, and prompt engineering approaches. We discuss the implications of these attacks on LLM safety and the need for improved defense mechanisms. This work aims to provide a thorough understanding of the current landscape of red-teaming attacks and defenses on LLMs, enabling the development of more secure and reliable language models.
DOI:10.48550/arxiv.2410.09097