OWASP Top 10 for LLM Applications – 2025 Edition
OWASP Top 10 for LLM Applications – 2025 Edition
Published 6/2025
Duration: 1h 22m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 496.96 MB
Genre: eLearning | Language: English
Published 6/2025
Duration: 1h 22m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 496.96 MB
Genre: eLearning | Language: English
Master LLM security: prompt injection defense, output filtering, plugin safeguards, red teaming, and risk mitigation
What you'll learn
- Understand and apply the OWASP Top 10 security risks for large language model applications
- Detect and mitigate vulnerabilities like prompt injection and insecure output handling
- Design and implement secure architectures for LLM-powered systems
- Build and document an LLM security risk register and mitigation plan
Requirements
- No prior AI security experience required. Basic familiarity with AI applications, software development, or cybersecurity concepts is helpful but not mandatory.
Description
Are you working with large language models (LLMs) or generative AI systems and want to ensure they are secure, resilient, and trustworthy? ThisOWASP Top 10 for LLM Applications – 2025 Editioncourse is designed to equip developers, security engineers, MLOps professionals, and AI product managers with the knowledge and tools to identify, mitigate, and prevent the most critical security risks associated with LLM-powered systems. Aligned with the latest OWASP recommendations, this course covers real-world threats that go far beyond conventional application security—focusing on issues like prompt injection, insecure output handling, model denial of service, excessive agency, overreliance, model theft, and more.
Throughout this course, you’ll learn how to apply secure design principles to LLM applications, including practical methods for isolating user input, filtering and validating outputs, securing third-party plugin integrations, and protecting proprietary model IP. We’ll guide you through creating a comprehensive risk register and mitigation plan using downloadable templates, ensuring that your LLM solution aligns with industry best practices for AI security. You’ll also explore how to design human-in-the-loop (HITL) workflows, implement effective monitoring and anomaly detection strategies, and conduct red teaming exercises that simulate real-world adversaries targeting your LLM systems.
Whether you're developing customer support chatbots, AI coding assistants, healthcare bots, or legal advisory systems, this course will help you build safer, more accountable AI products. With a case study based onGenAssist AI—a fictional enterprise LLM platform—you’ll see how to apply OWASP principles end-to-end in realistic scenarios. By the end of the course, you will be able to document and defend your LLM security architecture with confidence.
Join us to master the OWASP Top 10 for LLMs and future-proof your generative AI projects!
Who this course is for:
- This course is ideal for AI developers, security engineers, MLOps professionals, product managers, and anyone responsible for designing or securing systems powered by large language models. It’s also suitable for technology leaders who want to understand LLM risks and align with best practices.
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