ISBN: 9798905407642 - offensive llm security: the adversarial guide to hacking and exploiting large language models (7 risultati)

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  • Lingua: Inglese

    Editore: Notion Press, 2026

    9798905407642

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    Da: California Books, Miami, FL, U.S.A.California Books

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    EUR 50,41

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  • Lingua: Inglese

    Editore: Notion Press, 2026

    9798905407642

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    Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US

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    HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Lingua: Inglese

    Editore: Notion Press, 2026

    9798905407642

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    Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail

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    Hardcover. Condizione: new. Hardcover. LLM-powered applications are not magic-they are software systems built from prompts, retrieval pipelines, tools, agents, APIs, and trust boundaries. Each layer creates new opportunities for attackers.Offensive LLM Security is a practical guide for pentesters, bug-bounty researchers, application-security engineers, and developers who want to understand how modern AI applications fail. It covers direct and indirect prompt injection, jailbreaking, system-prompt extraction, insecure output handling, RAG poisoning, cross-tenant data leakage, excessive agency, MCP attacks, AI supply-chain risks, and model-serving infrastructure.Through hands-on labs, attack methodologies, measurement techniques, and complete worked engagements, readers learn how to trace attacker-controlled input through an LLM system to a privileged sink and demonstrate meaningful impact.Rather than treating AI security as a collection of clever prompts, this book applies proven application-security thinking to the full LLM stack-helping readers identify, reproduce, measure, report, and remediate real vulnerabilities. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

  • Lingua: Inglese

    Editore: Notion Press, 2026

    9798905407642

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    Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK

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    EUR 44,05

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    HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

  • Lingua: Inglese

    Editore: Notion Press, 2026

    9798905407642

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    Da: CitiRetail, Stevenage, Regno UnitoCitiRetail

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    EUR 48,85

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    Hardcover. Condizione: new. Hardcover. LLM-powered applications are not magic-they are software systems built from prompts, retrieval pipelines, tools, agents, APIs, and trust boundaries. Each layer creates new opportunities for attackers.Offensive LLM Security is a practical guide for pentesters, bug-bounty researchers, application-security engineers, and developers who want to understand how modern AI applications fail. It covers direct and indirect prompt injection, jailbreaking, system-prompt extraction, insecure output handling, RAG poisoning, cross-tenant data leakage, excessive agency, MCP attacks, AI supply-chain risks, and model-serving infrastructure.Through hands-on labs, attack methodologies, measurement techniques, and complete worked engagements, readers learn how to trace attacker-controlled input through an LLM system to a privileged sink and demonstrate meaningful impact.Rather than treating AI security as a collection of clever prompts, this book applies proven application-security thinking to the full LLM stack-helping readers identify, reproduce, measure, report, and remediate real vulnerabilities. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

  • Lingua: Inglese

    Editore: Notion Press, 2026

    9798905407642

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    Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller

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    EUR 70,83

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    Hardcover. Condizione: new. Hardcover. LLM-powered applications are not magic-they are software systems built from prompts, retrieval pipelines, tools, agents, APIs, and trust boundaries. Each layer creates new opportunities for attackers.Offensive LLM Security is a practical guide for pentesters, bug-bounty researchers, application-security engineers, and developers who want to understand how modern AI applications fail. It covers direct and indirect prompt injection, jailbreaking, system-prompt extraction, insecure output handling, RAG poisoning, cross-tenant data leakage, excessive agency, MCP attacks, AI supply-chain risks, and model-serving infrastructure.Through hands-on labs, attack methodologies, measurement techniques, and complete worked engagements, readers learn how to trace attacker-controlled input through an LLM system to a privileged sink and demonstrate meaningful impact.Rather than treating AI security as a collection of clever prompts, this book applies proven application-security thinking to the full LLM stack-helping readers identify, reproduce, measure, report, and remediate real vulnerabilities. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

  • Lingua: Inglese

    Editore: Notion Press, 2026

    9798905407642

    • Rilegato
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    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Condizione: Nuovo

    EUR 67,38

    EUR 35,00 spedizione 
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    Quantità: 2 disponibili

    Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - LLM-powered applications are not magic-they are software systems built from prompts, retrieval pipelines, tools, agents, APIs, and trust boundaries. Each layer creates new opportunities for attackers.Offensive LLM Security is a practical guide for pentesters, bug-bounty researchers, application-security engineers, and developers who want to understand how modern AI applications fail. It covers direct and indirect prompt injection, jailbreaking, system-prompt extraction, insecure output handling, RAG poisoning, cross-tenant data leakage, excessive agency, MCP attacks, AI supply-chain risks, and model-serving infrastructure.Through hands-on labs, attack methodologies, measurement techniques, and complete worked engagements, readers learn how to trace attacker-controlled input through an LLM system to a privileged sink and demonstrate meaningful impact.Rather than treating AI security as a collection of clever prompts, this book applies proven application-security thinking to the full LLM stack-helping readers identify, reproduce, measure, report, and remediate real vulnerabilities.…