Titlebook: Generative AI Security; Theories and Practic Ken Huang,Yang Wang,Jyoti Ponnapalli Book 2024 The Editor(s) (if applicable) and The Author(s)

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書目名稱Generative AI Security影響因子(影響力)




書目名稱Generative AI Security影響因子(影響力)學(xué)科排名




書目名稱Generative AI Security網(wǎng)絡(luò)公開度




書目名稱Generative AI Security網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Generative AI Security被引頻次




書目名稱Generative AI Security被引頻次學(xué)科排名




書目名稱Generative AI Security年度引用




書目名稱Generative AI Security年度引用學(xué)科排名




書目名稱Generative AI Security讀者反饋




書目名稱Generative AI Security讀者反饋學(xué)科排名

多山

Generative AI Security978-3-031-54252-7Series ISSN 2662-2467 Series E-ISSN 2662-2475

HACK

智力高

無脊椎

幼兒

https://doi.org/10.1007/978-1-4039-9019-8key policy elements like goals, risk management, compliance, consequences, and priority areas focused on model integrity, data privacy, resilience to attacks, and regulatory adherence. The chapter also covers specialized processes for GenAI across risk management, development cycles, and access gove

幼兒

背信

Basic principles and considerations,w of the security landscape for generative models. It begins by elucidating common vulnerabilities and attack vectors, including adversarial attacks, model inversion, backdoors, data extraction, and algorithmic bias. The practical implications of these threats are discussed, spanning domains like fi

預(yù)知

Helping the person to feel better,nalysis of the OWASP Top 10 for LLM applications gives the initial context of security concerns of GenAI Applications. Leading application design paradigms including RAG, ReAct, and agent-based systems are explored, along with their security implications. Major cloud-based AI services and associated

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