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This study identifies the potential vulnerabilities of Large Language Models (LLMs) to 'jailbreak' attacks, specifically focusing on the Arabic language and its various forms.
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Automatic transliteration of romanized dialectal arabic
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Trojbits: A hardware aware inference-time attack on transformer-based language models
Mansour Al Ghanim, Muhammad Santriaji, Qian Lou, and Yan Solihin. 2023 · 2023
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Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing. 2023 · 2023
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Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson. 2023 · 2023
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Aratrust: An evaluation of trustworthiness for llms in arabic
Emad A Alghamdi, Reem I Masoud, Deema Alnuhait, Afnan Y Alomairi, Ahmed Ashraf, and Mohamed Zaytoon. 2024 · 2024
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Comprehensive assessment of jailbreak attacks against llms
Junjie Chu, Yugeng Liu, Ziqing Yang, Xinyue Shen, Michael Backes, and Yang Zhang. 2024 · 2024
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Attackeval: How to evaluate the effectiveness of jailbreak attacking on large language models
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Jailbreaking chatgpt via prompt engineering: An empirical study
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Fine-tuning aligned language models compromises safety, even when users do not intend to!
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Llama 2: Open foundation and fine-tuned chat models
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Low-resource languages jailbreak gpt-4
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Don’t listen to me: Understanding and exploring jailbreak prompts of large language models
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