OWASP Top 10 for LLM Applications: AI Security Explained

What you'll learn

• Identify and explain all 10 OWASP LLM 2025 vulnerabilities including System Prompt Leakage and Vector & Embedding Weaknesses
• Analyze and execute prompt injection attacks, direct, indirect, multimodal, and agentic, using real-world scenarios
• Defend LLM applications against sensitive information disclosure, training data extraction, and RAG-sourced data leakage
• Threat model LLM applications using STRIDE, MITRE ATLAS, and the OWASP LLM risk framework
• Implement secure RAG pipelines with proper vector database access controls and embedding integrity checks
• Apply least-privilege and human-in-the-loop patterns to prevent excessive agency in AI agents and MCP-connected systems
• Evaluate LLM supply chain risk and build an AI Software Bill of Materials (SBOM) for enterprise deployments
• Build defense-in-depth architecture covering input validation, output sanitization, secrets management, and monitoring

About this course

Requirements

• •Basic understanding of how web applications work (HTTP, APIs, client-server model)
• •Familiarity with at least one programming language (Python preferred but not required)
• •General awareness of cybersecurity concepts such as authentication, injection attacks, and access control
• •No prior AI or machine learning experience needed, LLM architecture is explained from the ground up

Who this course is for

• Security engineers and penetration testers assessing LLM-powered applications
• Developers building products with LLM APIs who need to ship secure AI features
• AppSec professionals expanding into AI and machine learning security
• Security architects designing enterprise LLM platforms, RAG systems, or agentic AI pipelines
• CISO, GRC, and compliance professionals mapping LLM risks to EU AI Act and NIST AI RMF

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