Securing Agentic AI Attack & Defend Autonomous Agents

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Securing Agentic AI Attack & Defend Autonomous Agents
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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