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Free AI Agent Security Course: LLM Pentesting & OWASP Top 10

Explore AI agent security from foundational concepts to hands-on red teaming. This course covers prompt injection, the OWASP Top 10 for LLMs, and practical pentesting with tools like Docker and Python to help you...

10 lessons 7h 12min total Free

Course overview

This course provides a structured entry point into AI agent security, starting with the fundamentals of prompt injection attacks and advancing through the OWASP Top 10 for Large Language Model applications. You will analyze attack vectors like data poisoning, excessive agency, and insecure output handling. The lessons guide you from theoretical vulnerability frameworks to practical demonstrations, including hands-on challenges like the Agent Breaker CTF, tutorials on hacking GPT-4, and comprehensive guides on architecting secure AI agents using governance frameworks and DevSecOps principles.

Who this course is for

This course is designed for developers, security engineers, and IT professionals who are moving into the AI security domain. It suits individuals familiar with basic cybersecurity concepts who now need to understand the unique attack surface of AI agents and large language models. Red teamers looking to specialize in AI, as well as architects and practitioners tasked with securely deploying LLM-based applications, will find direct, actionable methodologies to incorporate into their security assessments and development lifecycles.

How to study this sequence

Follow the lessons sequentially to build a coherent mental model of AI threats. Begin with introductory walkthroughs on prompt injection and AI hacking accessibility, then study the OWASP Top 10 for LLMs to categorize risks. Watch the longer, in-depth sessions on LLM security testing and penetration testing methodologies to connect theory with practice. Use the hands-on demonstrations as your lab by replicating exercises locally with Docker and Python, actively exploring the specific CTF challenges and adversarial prompting techniques shown.

What you should be able to do

You will be able to identify and explain the OWASP Top 10 risks for LLM applications. The course equips you to perform basic red teaming operations against AI agents, craft adversarial prompts to test safety guardrails, and reason about secure AI architecture. You will gain practical familiarity with running AI security challenges and applying defensive concepts like input sanitization, output validation, and role-based access control for agent systems.

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What you learn in this AI Agent Security course

This course organizes 10 AI Agent Security lessons in a practical order, starting with the base and moving toward examples you can repeat in your own projects.

The main practice points are Understand AI pentesting methodologies and vulnerability assessment frameworks, Exploit prompt injection and AI model manipulation techniques through practical CTF challenges, Host and run local AI security challenges using Docker for hands-on practice and Chain multiple attack vectors to compromise AI systems in realistic scenarios. Use this page to review the course proposal, check the key topics, and open the lessons in the recommended order.

What you will practice

  • Understand AI pentesting methodologies and vulnerability assessment frameworks
  • Exploit prompt injection and AI model manipulation techniques through practical CTF challenges
  • Host and run local AI security challenges using Docker for hands-on practice
  • Chain multiple attack vectors to compromise AI systems in realistic scenarios
  • Develop a structured learning roadmap for becoming an AI pentester
  • Understand how prompt injection attacks exploit large language model vulnerabilities

Concepts covered

AI Pentesting Prompt Injection Language Model Exploitation CTF Challenges Agent Security Jailbreaking AI Vulnerability Assessment Prompt Injection Attacks

Course lessons

10 tutoriais organizados em sequência.

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Course summary

This AI Agent Security course brings together 10 lessons in a guided sequence. It starts with "become an AI HACKER (it’s easier than you think)" and moves step by step, so each lesson supports the next one and helps turn the topic into practice.

During the course, the practical focus is on Understand AI pentesting methodologies and vulnerability assessment frameworks, Exploit prompt injection and AI model manipulation techniques through practical CTF challenges, Host and run local AI security challenges using Docker for hands-on practice and Chain multiple attack vectors to compromise AI systems in realistic scenarios. These points help you watch with a goal instead of treating the lesson as a loose introduction.

The course also introduces important ideas such as AI Pentesting, Prompt Injection, Language Model Exploitation, CTF Challenges and Agent Security, which makes it easier to understand later tutorials and decide when each resource is useful.

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How to get more from this course

Who this AI Agent Security course is for

This advanced course is for learners who already understand the fundamentals of AI Agent Security and want to develop deeper technical or professional skills.

What you can do after finishing

By the end, you should be able to review the main idea of the course and practice Understand AI pentesting methodologies and vulnerability assessment frameworks, Exploit prompt injection and AI model manipulation techniques through practical CTF challenges, Host and run local AI security challenges using Docker for hands-on practice and Chain multiple attack vectors to compromise AI systems in realistic scenarios with more confidence.

Recommended study order

Watch the 10 lessons in the order shown on this page. Pause after each lesson to repeat the examples and only move on when the previous step makes sense.

What to study next

After finishing the course, explore the AI Agent Security topic to find related tutorials and keep studying with more context.

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Course questions

Is this AI Agent Security course for beginners?

No. This course covers advanced AI Agent Security topics and is best suited to learners who already understand the fundamentals.

How many lessons are in this course?

This course currently has 10 lessons and about 7h 12min in total.

What should I do after finishing the course?

The best next step is to explore the AI Agent Security topic and choose a related tutorial that matches what you want to practice.

Is this course free?

Yes. This is a free course organized by CursoB so you can study AI Agent Security in a structured sequence.

Do I need previous experience to follow the course?

Yes. You should already understand the fundamentals of AI Agent Security before starting this advanced course.

Should I watch the lessons in order?

Yes. The recommended path is to watch the 10 lessons in the order shown, because each lesson helps prepare the next one.

Can I use this course for practical projects?

Yes. Use the course as a practical reference to train Understand AI pentesting methodologies and vulnerability assessment frameworks, Exploit prompt injection and AI model manipulation techniques through practical CTF challenges, Host and run local AI security challenges using Docker for hands-on practice and Chain multiple attack vectors to compromise AI systems in realistic scenarios and adapt the examples to your own projects.

Does this course include a certificate?

CursoB stopped issuing certificates for its own courses in May 2026. CursoB no longer offers completion certificates. Courses and tutorials remain free to study. If content is hosted on another platform, any certificate depends exclusively on the original author or platform rules.

Are the lessons updated?

CursoB periodically reviews the AI Agent Security catalog and may add, remove, or reorder lessons when better content is found.

Where can I find more content on this subject?

Explore the AI Agent Security topic to find related tutorials and complementary courses.

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What each course lesson covers

Use these notes to understand the sequence before opening each lesson.

  1. 01 become an AI HACKER (it’s easier than you think) In this lesson, you practice Understand AI pentesting methodologies and vulnerability assessment frameworks, Exploit prompt injection and AI model manipulation techniques through practical CTF challenges and Host and run local AI security challenges using Docker for hands-on practice.
  2. 02 What Is a Prompt Injection Attack? In this lesson, you practice Understand how prompt injection attacks exploit large language model vulnerabilities, Identify real-world scenarios where AI systems can be compromised and Implement multi-layered defense strategies including input validation and monitoring.
  3. 03 Mastering LLM Security Testing: A Comprehensive Guide In this lesson, you practice Identify and exploit prompt injection vulnerabilities in language models, Conduct security assessments for LLM applications and architectures and Implement data extraction and privacy testing techniques.
  4. 04 Hacking LLMs Demo and Tutorial (Explore AI Security Vulnerabilities) In this lesson, you practice Identify common vulnerability patterns in large language models and their exploitation mechanisms, Execute practical jailbreaking demonstrations against popular LLMs like GPT-4 and Understand how adversarial prompting techniques bypass safety mechanisms and content filters.
  5. 05 Explained: The OWASP Top 10 for Large Language Model Applications In this lesson, you practice Understand the OWASP Top 10 security risks specific to Large Language Model applications, Identify and mitigate direct and indirect prompt injection attack vectors and Implement secure output handling practices for LLM-generated content.
  6. 06 Guide to Architect Secure AI Agents: Best Practices for Safety In this lesson, you practice Implement governance frameworks for AI agent development and deployment, Apply Role-Based Access Control to restrict agent permissions appropriately and Identify and mitigate prompt injection attack vulnerabilities.
  7. 07 AI Red Teaming: A Developer’s Guide to LLM Security In this lesson, you practice Understand the core definition and importance of AI Red Teaming in building secure AI systems, Apply structured red teaming methodologies including threat modeling and adversarial simulation and Identify vulnerabilities in LLMs using prompt injection, jailbreaking, and data poisoning techniques.
  8. 08 Intro to LLM Security – OWASP Top 10 for Large Language Models (LLMs) In this lesson, you practice Understand the OWASP Top 10 security framework for LLM applications, Implement prompt injection detection and prevention techniques and Monitor and validate LLM outputs using LangKit and WhyLabs.
  9. 09 How to Pentest LLMs Like a Security Researcher Cybersecurity In this lesson, you practice Understand how LLM penetration testing differs from traditional web application security testing, Identify prompt injection vulnerabilities and exploit them through practical attack demonstrations and Recognize hallucinations, excessive agency, and insecure integrations as security risks.
  10. 10 Red Teaming AI: OWASP LLM Top 10 with Brian and Derek In this lesson, you practice Understand the OWASP Top 10 AI-LLM risk framework and how it differs from traditional application security, Identify prompt injection and input manipulation attack vectors targeting LLM systems and Implement red teaming methodologies specifically designed for AI application security.