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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.09097 |
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| _version_ | 1866912159556960256 |
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| author | Raheja, Tarun Pochhi, Nilay Curie, F. D. C. M. |
| author_facet | Raheja, Tarun Pochhi, Nilay Curie, F. D. C. M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09097 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations Raheja, Tarun Pochhi, Nilay Curie, F. D. C. M. Computation and Language Artificial Intelligence 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. |
| title | Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.09097 |