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Securing Agentic AI Attack & Defend Autonomous Agents

Name: Securing Agentic AI Attack &amp; Defend Autonomous Agents
Author: Baturi

Securing Agentic AI Attack & Defend Autonomous Agents

Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Securing Agentic AI Attack & Defend Autonomous Agents, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 7/2026
Created by Dr. Amar Massoud
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 45 Lectures ( 3h 34m ) | Size: 795.8 MB
Hands-on agent security: prompt injection, tool abuse, least privilege, sandboxing & red-teaming with LangGraph/MCP

What you'll learn

⚡ Map the attack surface of any agentic workflow: tools, memory, plan loop, multi-agent edges
⚡ Exploit prompt injection (direct & indirect), tool/command/SSRF injection, memory poisoning
⚡ Hijack agent plans and trigger denial-of-wallet; defend with execution guards
⚡ Enforce least privilege with permission brokers, MCP scopes, and approval gates
⚡ Secure multi-agent systems (CrewAI/AutoGen) against agent-to-agent injection & confused-deputy
⚡ Sandbox code execution with containers + egress control; defend the tool/MCP supply chain
⚡ Instrument tracing, build runtime guardrails, and run automated red-teaming (Garak, PyRIT)
⚡ Write a governance-grade red-team report mapped to OWASP, ATLAS, NIST, EU AI Act, ISO 42001

Requirements

❗ Python fluency; familiarity with OWASP-style security frameworks
❗ Sandbox code execution with containers + egress control; defend the tool/MCP supply chain

Description

This course contains the use of artificial intelligence.
Autonomous AI agents introduce entirely new attack vectors. The moment an LLM can call a shell, read a database, fetch a URL, send email, and run code across multiple steps, a single manipulation stops being unsafe text and becomes an executed action — at scale. This is action amplification, and it's why securing agents is its own discipline.
This is a practical, attack-then-defend course. You'll build a deliberately vulnerable agent — TaskPilot — on LangGraph and a local Llama model, then attack and harden it across eight hands-on labs. You'll exploit direct and indirect prompt injection, tool and command injection, SSRF, memory poisoning, plan hijacking, denial-of-wallet, agent-to-agent injection, and sandbox escape — then build the defenses: spotlighting and output handling, secure tool design, least-privilege permission brokers, MCP scoping, execution guards, container sandboxing with egress control, human-in-the-loop gates, tracing, runtime guardrails, and automated red-teaming with Garak and PyRIT.
Everything runs locally on a 16 GB laptop with Ollama — no paid API keys, no cloud accounts. Every lecture and lab is anchored to the frameworks that matter: the OWASP Top 10 for LLM Applications and the OWASP Agentic Security Initiative, MITRE ATLAS, the NIST AI Risk Management Framework, the EU AI Act, and ISO/IEC 42001. The capstone is a publishable red-team and remediation report you can adapt for client engagements.

Who this course is for

⭐ Security engineers, red-teamers, AppSec leads, and AI/platform engineers building or defending agents
⭐ Instrument tracing, build runtime guardrails, and run automated red-teaming (Garak, PyRIT)

Homepage

https://www.udemy.com/course/securing-agentic-ai-attack-defend-autonomous-agents

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