ISBN: 9798337371337 - securing large language models against emerging threats (12 resultados)

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Hardcover. Condición: new. Hardcover. As large language models (LLMs) become integrated into critical applications, their security emerges as a pressing concern. While these models offer capabilities in understanding and generating language, they also present new vulnerabilities that malicious actors are learning to exploit. Emerging threats challenge the integrity, confidentiality, and availability of LLM-based systems. Securing these models requires a comprehensive approach that anticipates emerging risks, blending technical safeguards with responsible deployment practices. As the adoption of LLMs increases, strengthening them against new threats becomes critical. Securing Large Language Models Against Emerging Threats explores the field of LLM security, focusing on the challenges, threats, and solutions surrounding the deployment and use of generative AI systems. It examines defense mechanisms, auditing techniques, red teaming practices, regulatory implications, and best practices for securing LLMs in real-world environments. This book covers topics such as cybercrime, smart technology, and fraud detection, and is a useful resource for security professionals, computer engineers, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condición: Neu. Securing Large Language Models Against Emerging Threats | Hewa Majeed Zangana (u. a.) | Buch | Englisch | 2025 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337371337 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As large language models (LLMs) become integrated into critical applications, their security emerges as a pressing concern. While these models offer capabilities in understanding and generating language, they also present new vulnerabilities that malicious actors are learning to exploit. Emerging threats challenge the integrity, confidentiality, and availability of LLM-based systems. Securing these models requires a comprehensive approach that anticipates emerging risks, blending technical safeguards with responsible deployment practices. As the adoption of LLMs increases, strengthening them against new threats becomes critical. Securing Large Language Models Against Emerging Threats explores the field of LLM security, focusing on the challenges, threats, and solutions surrounding the deployment and use of generative AI systems. It examines defense mechanisms, auditing techniques, red teaming practices, regulatory implications, and best practices for securing LLMs in real-world environments. This book covers topics such as cybercrime, smart technology, and fraud detection, and is a useful resource for security professionals, computer engineers, academicians, researchers, and scientists.…