Ai Agent Security: App Security For Vibe-Coded Agents
Published 5/2026
Created by Eden Marco
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 29 Lectures ( 1h 32m ) | Size: 1.71 GB
Secure AI-generated apps and web-based AI agents against injection, auth flaws, secrets exposure, and insecure defaults.
What you'll 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 applicationsRequirements
Basic familiarity with software development or web applications is helpful, but deep security expertise is not required Python, APIs, or backend development will make the hands-on demos easier to follow Security professionals can take the course without being full-time developers, as concepts are explained from both engineering and security perspectives An interest in AI agents, AI-assisted development, application security, or secure system design is recommendedDescription
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 thefundamentals of application security for vibe coded apps through a practical, modern example: aweb-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 bothtraditional app security andAI agent security together.
In this hands-on course, you'll learn
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 developmentA major focus of the course is showing how aclassic 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 AIwithout 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 PermissionsWho this course is for
Software engineers and developers building AI-powered apps, AI agents, or vibe-coded products Security engineers, application security engineers, and cloud security engineers who need to assess AI application risk SOC engineers and security analysts who want to understand how AI agent attacks work in practice CISOs, security leaders, and technical decision-makers who need a practical view of AI agent risk and defense Solutions architects, platform engineers, and engineering managers responsible for secure AI adoption Anyone who wants to understand how traditional AppSec and modern AI agent security connect in real systemsHomepage
https://rapidgator.net/file/1d8700c...Security_for_Vibe-Coded_Agents.part2.rar.html
https://rapidgator.net/file/61a06d0...Security_for_Vibe-Coded_Agents.part1.rar.html
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