Articoli correlati a LLM Security Engineering: Red Team Tactics for Prompt...
LLM Security Engineering: Red Team Tactics for Prompt Injection, Data Leakage, and OWASP GenAI Compliance - Brossura
Sinossi
The rush to deploy Generative AI is leaving enterprise applications dangerously exposed. Are your LLMs secure?
Traditional AppSec models, WAFs, and standard penetration testing fail when it comes to Large Language Models. In a landscape where natural language is the new attack vector, firewalls cannot stop a cleverly crafted prompt.
LLM Security Engineering is the definitive, hands-on guide for security professionals, AI developers, and DevSecOps teams tasked with deploying secure Generative AI in production. Written by an expert AI security engineer, this book bridges the gap between offensive Red Team tactics and defensive engineering, showing you exactly how attackers manipulate LLMs and how to stop them.
Instead of theoretical risks, you will dive into practical, repeatable exploit frameworks and robust mitigation strategies. You will learn how to build automated attack surface inventories, harden Retrieval-Augmented Generation (RAG) pipelines, and architect multi-layered output filters that actually work.
Inside, you will master:
Advanced Prompt Injection: Execute and defend against direct overrides, delimiter breaking, and complex multi-turn conversation poisoning.
Indirect Attack Vectors: Secure your system against malicious payloads hidden in retrieved documents (RAG), web content, and tool outputs.
Guardrail Bypass & Jailbreaking: Understand token smuggling, persona hijacking, and how to patch vulnerabilities without breaking legitimate user experience.
Data Leakage Prevention: Stop training data extraction, system prompt leakage, and PII regurgitation cold.
Agent & Tool Security: Sandbox function-calling, scope permissions, and prevent devastating privilege escalation in autonomous AI agents.
OWASP GenAI Top 10 Compliance: Map your defenses to industry standards with audit-ready checklists, continuous CI/CD security testing, and automated fuzzing.
Whether you are auditing a complex AI agent architecture or building the security review gate for a new enterprise chatbot, this book provides the exact rubrics, test harnesses, and containment procedures you need.
Don't wait for a costly data breach or a PR disaster to test your AI's defenses.
Scroll up and click "Buy Now" to secure your LLM applications before attackers do.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Risultati della ricerca per LLM Security Engineering: Red Team Tactics for Prompt...
Foto dell'editore
LLM Security Engineering: Red Team Tactics for Prompt Injection, Data Leakage, and OWASP GenAI Compliance
Print on DemandDa: California Books, Miami, FL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Print on Demand. Codice articolo I-9798193047025
Compra nuovo
EUR 32,18
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
LLM Security Engineering
Da: PBShop.store US, Wood Dale, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9798193047025
Compra nuovo
EUR 33,89
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
LLM Security Engineering (Paperback)
Print on DemandDa: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. The rush to deploy Generative AI is leaving enterprise applications dangerously exposed. Are your LLMs secure?Traditional AppSec models, WAFs, and standard penetration testing fail when it comes to Large Language Models. In a landscape where natural language is the new attack vector, firewalls cannot stop a cleverly crafted prompt.LLM Security Engineering is the definitive, hands-on guide for security professionals, AI developers, and DevSecOps teams tasked with deploying secure Generative AI in production. Written by an expert AI security engineer, this book bridges the gap between offensive Red Team tactics and defensive engineering, showing you exactly how attackers manipulate LLMs and how to stop them.Instead of theoretical risks, you will dive into practical, repeatable exploit frameworks and robust mitigation strategies. You will learn how to build automated attack surface inventories, harden Retrieval-Augmented Generation (RAG) pipelines, and architect multi-layered output filters that actually work.Inside, you will master: Advanced Prompt Injection: Execute and defend against direct overrides, delimiter breaking, and complex multi-turn conversation poisoning.Indirect Attack Vectors: Secure your system against malicious payloads hidden in retrieved documents (RAG), web content, and tool outputs.Guardrail Bypass & Jailbreaking: Understand token smuggling, persona hijacking, and how to patch vulnerabilities without breaking legitimate user experience.Data Leakage Prevention: Stop training data extraction, system prompt leakage, and PII regurgitation cold.Agent & Tool Security: Sandbox function-calling, scope permissions, and prevent devastating privilege escalation in autonomous AI agents.OWASP GenAI Top 10 Compliance: Map your defenses to industry standards with audit-ready checklists, continuous CI/CD security testing, and automated fuzzing.Whether you are auditing a complex AI agent architecture or building the security review gate for a new enterprise chatbot, this book provides the exact rubrics, test harnesses, and containment procedures you need.Don't wait for a costly data breach or a PR disaster to test your AI's defenses.Scroll up and click "Buy Now" to secure your LLM applications before attackers do. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798193047025
Compra nuovo
EUR 34,67
Spedizione EUR 43,72
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 1 disponibili
Foto dell'editore
LLM Security Engineering : Red Team Tactics for Prompt Injection, Data Leakage, and OWASP GenAI Compliance
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. Neuware - The rush to deploy Generative AI is leaving enterprise applications dangerously exposed. Are your LLMs secure Traditional AppSec models, WAFs, and standard penetration testing fail when it comes to Large Language Models. In a landscape where natural language is the new attack vector, firewalls cannot stop a cleverly crafted prompt.LLM Security Engineering is the definitive, hands-on guide for security professionals, AI developers, and DevSecOps teams tasked with deploying secure Generative AI in production. Written by an expert AI security engineer, this book bridges the gap between offensive Red Team tactics and defensive engineering, showing you exactly how attackers manipulate LLMs and how to stop them.Instead of theoretical risks, you will dive into practical, repeatable exploit frameworks and robust mitigation strategies. You will learn how to build automated attack surface inventories, harden Retrieval-Augmented Generation (RAG) pipelines, and architect multi-layered output filters that actually work.Inside, you will master: Advanced Prompt Injection: Execute and defend against direct overrides, delimiter breaking, and complex multi-turn conversation poisoning.Indirect Attack Vectors: Secure your system against malicious payloads hidden in retrieved documents (RAG), web content, and tool outputs.Guardrail Bypass & Jailbreaking: Understand token smuggling, persona hijacking, and how to patch vulnerabilities without breaking legitimate user experience.Data Leakage Prevention: Stop training data extraction, system prompt leakage, and PII regurgitation cold.Agent & Tool Security: Sandbox function-calling, scope permissions, and prevent devastating privilege escalation in autonomous AI agents.OWASP GenAI Top 10 Compliance: Map your defenses to industry standards with audit-ready checklists, continuous CI/CD security testing, and automated fuzzing.Whether you are auditing a complex AI agent architecture or building the security review gate for a new enterprise chatbot, this book provides the exact rubrics, test harnesses, and containment procedures you need.Don't wait for a costly data breach or a PR disaster to test your AI's defenses.Scroll up and click 'Buy Now' to secure your LLM applications before attackers do. Codice articolo 9798193047025
Compra nuovo
EUR 48,59
Spedizione EUR 35,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 2 disponibili