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Whitepapers/Guides

Whitepapers

Resources, Top 10 for LLM

die OWASP Top 10 für LLM & Generative KI (2025)

/ / Resources, Top 10 for LLM

Dieses Update bietet eine aktualisierte und umfassende Ressource, die sich mit den größten Risiken, Schwachstellen und Gegenmaßnahmen für die Absicherung von Anwendungen für generative KI und LLM über ihren gesamten Entwicklungs-, Bereitstellungs- und Verwaltungslebenszyklus hinweg befasst. Ganz gleich, ob Sie mit RAG-basierten Anwendungen, Agentic-Architekturen oder komplexen LLM-Integrationen arbeiten, diese Liste ist ein Muss für Entwickler, […]

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Resources, Top 10 for LLM

Top 10 2025 de riesgos y mitigaciones para LLMs y aplicaciones de IA Generativa

/ / Resources, Top 10 for LLM

El OWASP Top 10 para Aplicaciones de Modelos de Lenguaje Grandes comenzó en 2023 como un esfuerzo impulsado por la comunidad para resaltar y abordar problemas de seguridad específicos para aplicaciones de IA. Desde ese momento, la tecnología ha seguido extendiéndose a través de industrias y aplicaciones, al igual que los riesgos asociados. A medida

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Resources

OWASP LLM Exploit Generation v1.0

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This paper examines the practical implications of large language models (LLMs) in offensive cybersecurity, moving beyond theoretical possibilities to assess their real-world effectiveness. The research, conducted by the CTI Layer Team at OWASP Top Ten For LLMs, explores the ability of LLMs such as GPT-4o, Claude, and DeepSeek r-1 to exploit vulnerabilities in the OWASP

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Resources, Initiatives

Agentic AI – Threats and Mitigations

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Agentic AI represents an advancement in autonomous systems, increasingly enabled by large language models (LLMs) and generative AI. While agentic AI predates modern LLMs, their integration with generative AI has significantly expanded their scale, capabilities, and associated risks. This document is the first in a series of guides from the OWASP Agentic Security Initiative (ASI)

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Resources, Initiatives

LLM and Gen AI Data Security Best Practices

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The rapid proliferation of Large Language Models (LLMs) across various industries has highlighted the critical need for advanced data security practices. As these AI systems become more sophisticated, they bring with them unprecedented risks, including potential breaches of sensitive information and challenges in meeting stringent data protection regulations. This white paper outlines a comprehensive set

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Resources, Initiatives

GenAI Red Teaming Guide

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This guide outlines the critical components of GenAI Red Teaming, with actionable insights for cybersecurity professionals, AI/ML engineers, Red Team practitioners, risk managers, adversarial attack researchers, CISOs, architecture teams, and business leaders. The guide emphasizes a holistic approach to Red Teaming in four areas: model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis.

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Resources, Initiatives

LLM and Generative AI Security Solutions Landscape – Q1,2025

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Updated for Q1, 2025 – The LLM and Generative AI Security Solutions Landscape is tailored for a diverse audience comprising developers, AppSec professionals, DevSecOps and MLSecOps teams, data engineers, data scientists, CISOs, and security leaders who are focused on developing strategies to secure Large Language Models (LLMs) and Generative AI applications. It provides a reference

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Resources, Initiatives

OWASP Top 10 for LLM Applications 2025

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The OWASP Top 10 for Large Language Model Applications started in 2023 as a community-driven effort to highlight and address security issues specific to AI applications. Since then, the technology has continued to spread across industries and applications, and so have the associated risks. As LLMs are embedded more deeply in everything from customer interactions to internal operations, developers and security professionals are discovering new vulnerabilities—and ways to counter them.

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Resources, Initiatives

LLM and Generative AI Security Solutions Landscape

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The LLM and Generative AI Security Solutions Landscape is tailored for a diverse audience comprising developers, AppSec professionals, DevSecOps and MLSecOps teams, data engineers, data scientists, CISOs, and security leaders who are focused on developing strategies to secure Large Language Models (LLMs) and Generative AI applications. It provides a reference guide of the solutions available

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Resources, Initiatives

LLM and Generative AI Security Center of Excellence Guide

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As generative AI technologies evolve and integrate into various aspects of business and society, the need for robust governance, security, and policy management becomes paramount. Establishing a Center of Excellence (COE) for Generative AI Security aims to bring together diverse groups such as security, legal, data science, operations, and end-users to foster collaboration, develop best

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