AI agent security focuses on prompt injection, tool permissions, data exposure and the risks created when AI systems can take actions.
AI Agent Security courses
View all →AI Agent Security tutorials
All tutorials related to this topic, gathered in one place.
Criteria: same topic and editorial language. Tutorials are individual content and do not represent a required sequence.
16min
ENbecome an AI HACKER (it’s easier than you think)
10min
ENWhat Is a Prompt Injection Attack?
1h 53min
ENMastering LLM Security Testing: A Comprehensive Guide
26min
ENHacking LLMs Demo and Tutorial (Explore AI Security Vulnerabilities)
14min
ENExplained: The OWASP Top 10 for Large Language Model Applications
13min
ENGuide to Architect Secure AI Agents: Best Practices for Safety
8min
ENAI Red Teaming: A Developer’s Guide to LLM Security
57min
ENIntro to LLM Security – OWASP Top 10 for Large Language Models (LLMs)
1h 43min
ENHow to Pentest LLMs Like a Security Researcher Cybersecurity
1h 07min
ENRed Teaming AI: OWASP LLM Top 10 with Brian and Derek
Frequently asked questions about AI Agent Security
What is AI Agent Security?
AI Agent Security is a technology topic that learners can study through concepts, practical workflows and validation habits.
Why should I learn AI Agent Security now?
It is connected to current AI, software, security and infrastructure changes, so it helps learners understand where modern technology work is moving.
Is AI Agent Security beginner-friendly?
Yes, when studied in order. Start with conceptual lessons, then move into practical tutorials and deeper technical material.
Do I need to know programming?
Some lessons are useful without programming, but developer-focused material may require basic Python, APIs, command-line tools or web concepts.
What should I practice first?
Start with a small task that can be repeated and checked, then change one variable at a time to understand the workflow.
How does this connect with AI agents?
Many modern technology topics connect with agents through tools, retrieval, evaluation, security, automation or developer workflows.
How do I validate what I learn?
Check sources, run code when available, compare outputs, document assumptions and test whether the result solves the original task.
What comes after this topic?
The next step is usually a project: build a small workflow, test it, document the result and connect it with related Cursob topics.
How were these materials selected?
The selection prioritizes free, embeddable English videos with practical value, clear explanations and relevance to the topic.
How to study AI Agent Security without getting lost
The best way to learn AI Agent Security is to separate concepts, tools, practical examples and validation. This keeps the topic useful even when individual tools change.
Start from the problem
Before choosing a tool, identify the problem the technology is trying to solve. Write down the inputs, expected output, constraints and risks. This makes each tutorial easier to compare.
Practice with small workflows
Use a small example that can be repeated. Change one part of the workflow, inspect the result and keep notes about what improved or failed.
Validate before scaling
Modern AI and infrastructure workflows can look impressive before they are reliable. Review claims, test code, check permissions and document assumptions before applying the method to important work.