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Rao, Abhinav
Sukumar; Naik, Atharva Roshan; Vashistha, Sachin; Aditya, Somak; Choudhury, Monojit (2024). "Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks". In Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen
656:, to adversarial attacks. These attacks are designed to manipulate the models' outputs by introducing subtle perturbations in the input text, leading to incorrect or harmful outputs, such as generating
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Rossi, Sippo; Michel, Alisia
Marianne; Mukkamala, Raghava Rao; Thatcher, Jason Bennett (January 31, 2024). "An Early Categorization of Prompt Injection Attacks on Large Language Models".
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Rossi, Sippo; Michel, Alisia
Marianne; Mukkamala, Raghava Rao; Thatcher, Jason Bennett (January 31, 2024). "An Early Categorization of Prompt Injection Attacks on Large Language Models".
644:, which allowed malicious actors to manipulate the model's outputs through prompt injections. The resulting paper investigated the vulnerability of large pre-trained
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Proceedings of the 2024 Joint
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and risk mitigation for enterprises. They are a part of the Air Force security program as a notable
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and policy regulations. In May 2022, Preamble's researchers discovered critical vulnerabilities in
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attacks, a critical security issue for AI systems. These findings were first reported privately to
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in 2022 and have since been the subject of numerous studies in the field.
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Kosinski, Matthew; Forrest, Amber (March 21, 2024).
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867:August 2024
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614:AI safety
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383:Industry
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336:Deepfake
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258:Robotics
233:Planning
141:Founders
113:Industry
853:Please
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620:AI hub
504:AI boom
482:History
405:Physics
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454:Ethics
799:arXiv
732:(PDF)
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