AI Agent Security: AI Security (Udemy.com)
Secure AI apps and web-based AI agents against injection, auth flaws, secrets exposure, and insecure defaults.
Created by: Eden Marco
Last updated September 2026
What you will learn
- Identify the top security risks in AI agents and AI-generated applications, including prompt injection, auth flaws, insecure defaults, and data exposure
- Exploit and fix real vulnerabilities in a web-based AI agent using hands-on attack, defense, and verification exercises
- Apply secure coding patterns for input validation, authentication, authorization, secrets handling, and least privilege
- Recognize security issues introduced by AI coding tools and review generated code with a stronger AppSec mindset
- Reduce agent blast radius with tool restrictions, identity-aware controls, memory protection, and guardrails
- Use practical security review habits, checklists, and testing approaches before shipping AI-assisted applications
How it compares
Against the other 523 rated Udemy courses in Hacking on CourseDuck:
- Rating4.3 out of 5higher than 21% of them
- Reviews183#401 of 524 by number of reviews
- Length2.2 hoursshorter than 78% of them
- Last updatedSeptember 2026more recently than 97% of them
Course Description
assisted development makes it faster than ever to build applications, but it also makes it easier to ship security mistakes at speed. This course teaches the fundamentals of application security for vibe coded apps through a practical, modern example: a web-based AI agent application with real tools, user data, authentication, and cloud access.
Instead of learning security only through theory, you’ll work through a classic real-world pattern many developers are now building: an AI-powered app that looks like a normal web product on the surface, but behind the scenes includes LLM workflows, tool calling, memory, and backend access. That makes it the perfect example for understanding both traditional app security and AI agent security together.
In this hands-on course, you’ll learn:
AI Security
core application security concepts every AI-assisted developer should know
OWASP-style risks including injection, auth flaws, insecure defaults, and over-permissioned systems
how AI code generation can introduce vulnerabilities into apps and agents
how to recognize insecure patterns in generated code and architecture
secure coding patterns for input validation, authentication, authorization, and sensitive data handling
secrets management, dependency hygiene, and common supply chain risks
how to reduce blast radius in agentic systems with layered defenses
how to use automated scanning and AI-powered review workflows before deployment
how to build a personal security checklist for rapid AI-assisted development
A major focus of the course is showing how a classic web-coded AI agent can become vulnerable to prompt injection, data exfiltration, broken authorization, memory attacks, and excessive privilege and then walking through how to fix those issues step by step.
By the end of the course, students will understand how to build faster with AI without skipping security fundamentals, and how to apply practical defenses to both conventional software and modern AI agent applications.
Short Attack List
Prompt Injection
Indirect Prompt Injection
Injection Attacks
Broken Authentication
Broken Authorization
Insecure Defaults
Secret Exposure
Data Exfiltration
Memory Poisoning
Tool Abuse
Jailbreaks
PII Leakage
Dependency Risks
Supply Chain Risks
Excessive Permissions
Instructor Details
- 4.3 Rating
183 Reviews
Eden Marco
I’m a passionate Software Engineer with years of experience in back-end development and cloud architecture. I was one of the first engineers at Orca Security, where I helped shape the company’s core technology, and today I work as a GenAI Architect at Google Cloud, helping organizations design and deploy advanced generative AI and cloud-native solutions.
I’m also proud to be a LangChain Ambassador, actively contributing to the open-source community and helping developers build powerful LLM applications using the LangChain ecosystem.
I hold a Bachelor’s degree in Computer Science from the Technion, and I’ve always had a deep passion for teaching and mentorship. I taught Functional Programming and Introduction to Computer Science at Reichman University, where I guided the next generation of software engineers.
My courses are built on real-world experience and designed to give you practical, production-ready skills
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Reviews
By Mircea Goga on 10/13/2026
I have already got new ideas about how to work with my agents and the course has just started.
By Andrew Horn on 9/28/2026
It seems to be at the moment
By Mohammad Nauman on 7/5/2026
Overall a good course, ending was bit abrupt as the last section on memory poisoning ended without any fix demo (format followed for previous sections) .
By Dziyana Zavadskaya on 6/29/2026
Clear english for not native speaker
By Varkey Medayil John on 6/8/2026
Great course. Good to see the LLM security aspects from a proper white box perspective.
By Gaillard Daniel on 10/15/2026
By Sourabh Maind on 10/12/2026
By shahid Riaz on 8/14/2026
By Ravindra Hugar on 7/4/2026
By Jaypee Cananca on 6/26/2026
Quality Score
No CourseDuck member has rated this course yet. Taken it? Give each part a thumbs up or down.
Overall Score : 86 / 100

![LlamaIndex Develop LLM Powered Apps [UPDATE - 2026]](/programming/it-and-software/images/5609174_9a5d_2.jpg?v=1789443310)










