Learn how to identify, exploit and mitigate the OWASP Top 10 risks in AI & LLM systems for practical defense
What You Will 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
- Show more
A Pragmatic Deep Dive into LLM Security: OWASP Top 10 for LLM Applications Review
As someone who’s navigated the ever-shifting sands of cybersecurity for a good chunk of my career, I’m always on the lookout for training that offers genuine, actionable insights rather than just theoretical fluff. The ‘OWASP Top 10 for LLM Applications: AI Security Explained’ course promised just that – a practical approach to the burgeoning world of AI and LLM security. After going through it, I can confidently say it largely delivers on that promise, offering a much-needed roadmap for securing these complex systems.
The course isn’t just a rehash of existing OWASP Top 10 lists; it’s a forward-thinking exploration tailored specifically to the unique attack vectors LLMs introduce. It dives headfirst into understanding how established vulnerabilities manifest in AI contexts, and crucially, introduces entirely new threat landscapes that we’re only beginning to grapple with. The emphasis on *practical defense* is palpable throughout, moving beyond simply identifying risks to providing the “how-to” for mitigating them. This isn’t a certification prep course in the traditional sense, but the depth of knowledge gained certainly lays a solid foundation for those aiming for advanced AI security roles.
Prerequisites
While the course is designed to be accessible, having a foundational understanding of cybersecurity principles and some familiarity with AI/ML concepts will significantly enhance your learning experience. You don’t need to be an LLM architect, but understanding basic programming logic and common web application vulnerabilities will help you connect the dots more effectively. Think of it as needing to know what a firewall is before learning how to configure one for an AI model.
Skills & Tools
This course is a masterclass in developing job-ready skills for the current AI security landscape. You’ll gain hands-on experience in:
- Identifying and explaining the OWASP LLM Top 10 vulnerabilities, including novel threats like System Prompt Leakage and Vector & Embedding Weaknesses.
- Executing prompt injection attacks across various forms (direct, indirect, multimodal, agentic) with real-world attack scenarios that feel remarkably plausible.
- Implementing robust defenses against sensitive data disclosure, training data extraction, and RAG-sourced data leakage.
- Threat modeling LLM applications using established frameworks like STRIDE and MITRE ATLAS, applied to common architectures such as chatbots and RAG systems.
- Securing RAG pipelines through proper vector database access controls and integrity checks.
- Applying crucial design patterns like least-privilege and human-in-the-loop to manage AI agent agency.
The course doesn’t shy away from practical application, so be prepared to engage with exercises that mirror real-world projects.
Career Benefits & Job Roles
For anyone looking to bolster their career growth in cybersecurity, this course is an absolute game-changer. The demand for professionals skilled in AI security is skyrocketing. Completing this training equips you for roles such as:
- AI Security Engineer
- Machine Learning Security Analyst
- LLM Application Security Specialist
- Cloud Security Engineer with AI focus
- Cybersecurity Consultant (AI/ML)
It provides that specialized knowledge that differentiates candidates and makes them invaluable to organizations embracing AI.
Pros
- Deeply Practical Focus: This isn’t theoretical. The course excels at demonstrating attacks and defenses in a way that sticks, making the learning highly applicable.
- Forward-Thinking Content: Addressing the OWASP LLM Top 10 is crucial, and the inclusion of new vulnerabilities is timely and vital for staying ahead.
- Comprehensive Scope: From prompt injection to RAG security and threat modeling, it covers a wide and critical array of LLM security concerns.
- Real-World Relevance: The use of realistic attack scenarios and industry-standard tools (even if implied) makes the knowledge immediately transferable to professional environments.
Cons
My only honest critique, and it’s a minor one given the course’s strengths, is that some of the more complex agentic attack scenarios could benefit from a bit more scaffolding for those not deeply steeped in agent architectures. While the explanation is clear, the sheer complexity of some multi-agent interactions might leave a slight learning curve for individuals new to that specific domain. However, this is a minor point in an otherwise stellar learning experience.