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OWASP Top 10 for LLM Aplications: AI Security Explained
Learn how to identify, exploit and mitigate the OWASP Top 10 risks in AI & LLM systems for practical defense
Created byAdrian Găitan, Evaluris Solutions, Reju Kole
Last updated 5/2026
English
What you'll learn
- Identify and explain all 10 OWASP LLM 2025 vulnerabilities including new risks like System Prompt Leakage and Vector & Embedding Weaknesses
- Analyze and execute prompt injection attacks — direct, indirect, multimodal, and agentic — using real-world attack 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 across chatbot, RAG, and agentic architectures
- Implement secure RAG pipelines with proper vector database access controls, ingestion validation, and embedding integrity checks
- Apply least-privilege and human-in-the-loop design patterns to prevent excessive agency in AI agents and MCP-connected systems
- Evaluate LLM supply chain risk, perform model vetting, and build an AI Software Bill of Materials (SBOM) for enterprise deployments
- Build a defense-in-depth architecture for LLM applications that covers input validation, output sanitization, secrets management, and monitoring
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Course content
12 sections • 36 lectures • 13h 44m total length
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 in Section 1
Description
Artificial intelligence is no longer experimental — it's in production. LLM-powered applications are being deployed across finance, healthcare, legal, and enterprise software at scale. And attackers are already exploiting them.
This course is the most comprehensive practitioner-built guide to the OWASP Top 10 for LLM Applications (2025 edition). Built for security professionals, developers, and architects who need to understand not just the theory, but how these vulnerabilities are exploited and how to stop them.
You will work through all 10 OWASP LLM risks in structured depth — starting with the architecture of LLM applications, moving through every vulnerability class with real attack scenarios, and finishing with a complete secure design framework you can apply immediately.
The course covers Prompt Injection in four dedicated modules — from direct and indirect injection to agentic pipeline hijacking and multimodal attacks. You'll learn how attackers exfiltrate data through poisoned RAG systems, backdoor models through supply chain compromise, and exploit excessive AI agent permissions to escalate privileges across enterprise environments.
The 2025-specific risks receive special attention: System Prompt Leakage and Vector & Embedding Weaknesses are new entries that reflect how real-world LLM deployments have evolved — and both are significantly under-covered elsewhere.
Every section follows a consistent three-part structure: understand the vulnerability, learn how it is exploited, then implement the defenses. The final section brings everything together with threat modeling methodology, a secure LLM application reference architecture, and a practical compliance mapping to EU AI Act, NIST AI RMF, GDPR, and SOC2.
This is a slides-based course built for focused learning — no fluff, no filler. Just the knowledge you need to secure AI systems in 2025 and beyond.
By the end of this course you will be able to threat model any LLM application, identify and demonstrate every OWASP LLM risk, and implement the architectural controls that prevent them.
Who this course is for:
- Security engineers and penetration testers who need to understand how to assess and attack LLM-powered applications
- Developers building products with LLM APIs (OpenAI, Anthropic, Gemini, open-source models) who need to ship secure AI features
- AppSec professionals expanding their skill set into AI and machine learning security
- AI/ML engineers and data scientists who want to understand the security implications of their model deployments
- Security architects designing enterprise LLM platforms, RAG systems, or agentic AI pipelines
- CISO, GRC, and compliance professionals mapping LLM risks to EU AI Act, NIST AI RMF, and SOC2 obligations