Video Training / IT and Programming for PC Tutorials →Securing Agentic AI Attack & Defend Autonomous Agents

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