Agentic AI Security: Red Team Agents Safely
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Agentic AI Security: Red Team Agents Safely – taught by PapaHR
Updated July 2026 – This free Udemy course equips cybersecurity professionals with a hands‑on, reproducible framework for red‑team testing of autonomous AI agents. You’ll learn to map attack surfaces, design multi‑turn prompt‑injection tests, and run a sandboxed red‑team campaign that produces concrete evidence and remediation guidance. The curriculum blends theory with a synthetic business case, delivering practical skills that translate directly to AI‑driven security programs and certification‑ready experience.
What You'll Learn
- Build an Agent Attack Surface Map that visualizes goals, planning, tool use, memory, delegation, trust boundaries, and side‑effects.
- Master multi‑turn prompt‑injection and goal‑hijacking test design with explicit success, failure, containment, and recovery oracles.
- Learn to trace unsafe tool‑affordance chains from attacker‑controlled inputs to simulated forbidden states without touching live systems.
- Understand memory‑poisoning, session persistence, cross‑user isolation, and cleanup using benign synthetic canaries.
- Create reproducible cross‑agent spoofing, replay, privilege‑laundering, context‑smuggling, and collusion scenarios.
- Implement a deterministic sandbox with fake tools, local sinks, fixed budgets, stop controls, and verified reset mechanisms.
- Apply quantitative metrics such as first‑cycle failure, trajectory attack success, cascade incidence, depth, amplification, and utility retention.
- Analyze an authorized agentic red‑team campaign, linking evidence, findings, remediation, regression tests, and release decisions.
Course Details
- Instructor: PapaHR
- Rating: 4.7 stars (based on student reviews)
- Language: English (en‑US)
- Certificate: Yes, upon completion
- Includes: Lifetime access to all materials, active instructor support in Q&A, Udemy Certificate of Completion
What This Course Covers
Agent Attack Surface Mapping
- Identify and document every phase of the agent loop: goal formulation, planning, tool selection, memory usage, and delegation.
- Translate the loop into a visual Attack Surface Map highlighting trust boundaries and prioritized test backlogs.
- Align mapped surfaces with security policies and risk tolerance thresholds.
- Produce a reusable artifact that serves as the foundation for all subsequent red‑team exercises.
Multi‑Turn Goal Hijack Testing
- Freeze original task parameters, success conditions, allowed actions, and stop conditions for baseline comparison.
- Inject harmless conflicts into user messages, tool outputs, retrieved documents, memory recalls, or peer messages.
- Capture clean vs. adversarial runs, locate the first deviation, and classify outcomes as containment, recovery, or successful hijack.
- Document evidence in a structured Goal Hijack Test Pack for repeatable regression testing.
Tool Affordance Chain Mapping
- Inventory principals, schemas, resources, side‑effects, approval gates, and detection signals for each tool.
- Connect individually allowed fake actions into a simulated forbidden outcome, exposing chain‑level vulnerabilities.
- Record every request, policy decision, state transition, containment point, and rollback event.
- Generate a Tool Affordance Chain Mapper workbook that can be reused across different AI agents.
Memory Poisoning Test Protocol
- Map session context, profile records, retrieval indexes, cached summaries, writers, readers, and provenance trails.
- Deploy a benign canary to follow a single record through write, storage, retrieval, planning, cleanup, and utility retest.
- Verify whether the canary survives new sessions, crosses synthetic users, or influences downstream decisions.
- Produce auditable logs that demonstrate memory integrity or poisoning effects.
Multi‑Agent Scenario Builder
- Work with six scripted peer agents, each with distinct capabilities and trust tiers.
- Define structured message contracts covering sender, recipient, capability, asset, freshness, nonce, evidence, and delegation depth.
- Execute forged‑sender, replayed‑approval, privilege‑laundering, context‑smuggling, and colluding‑peer scenarios.
- Capture delivery, acceptance, propagation, causal depth, containment, final state, and clean‑path utility.
Sandboxed Agent Lab
- Operate a self‑contained browser‑based lab with no external network, persistent storage, file uploads, or live model calls.
- Use deterministic agents, fake tools, synthetic memory, mock mail/ticket services, fixed step budgets, and circuit‑breakers.
- Run injection, unsafe‑tool‑chain, memory‑poisoning, retry‑loop, and clean‑control scenarios step‑by‑step or as full traces.
- Export state hashes, invariant checks, and reset logs for independent verification.
Who Should Take This Course
- Cybersecurity engineers tasked with securing generative AI agents and autonomous workflows.
- Security analysts who need reproducible evidence for prompt‑injection, memory, or multi‑agent findings.
- DevSecOps engineers building pre‑deployment agent security tests, sandbox controls, and regression suites.
- Security architects and AI platform owners who must connect agent failure traces to remediation decisions.
- Professionals preparing for AI‑focused red‑team certifications or internal governance programs.
Prerequisites
- Basic understanding of cybersecurity principles and threat modeling.
- Familiarity with AI concepts such as prompting, tool use, and memory handling is helpful but not required.
- No prior experience with agentic AI is needed — the course is beginner‑friendly for security practitioners.
Why Enroll in This Course
This Udemy offering delivers a complete, production‑ready red‑team framework for agentic AI, a niche that few other trainings cover. A free coupon provides 100 % off for a limited time, making the high‑value content accessible without cost. Enrolling now ensures you benefit from the latest 2026 updates before the discount expires, positioning you ahead of peers in AI security testing.
Course Highlights
- Lifetime access to all video lessons, workbooks, and the sandbox lab.
- Self‑paced learning lets you progress through complex modules at your own speed.
- Certificate of Completion that can be added to LinkedIn or a professional portfolio.
- Hands‑on synthetic case (SentinelWorks incident INC‑1042) provides real‑world context without exposing production data.
- Active instructor support in Udemy Q&A for troubleshooting and deeper discussion.
- Reusable artifacts (maps, test packs, workbooks) that integrate into existing security processes.
Frequently Asked Questions
Q: Is this course really free?
A: Yes. By applying the available free coupon, you can enroll at 100 % off. The discount is offered for a limited period, so you should claim it soon to avoid paying the regular price.
Q: What will I learn in this Agentic AI security course?
A: You will learn to map agent attack surfaces, design multi‑turn prompt‑injection tests, trace unsafe tool chains, conduct memory‑poisoning experiments, build multi‑agent attack scenarios, and run a complete sandboxed red‑team campaign. Each skill is reinforced with hands‑on labs and reusable documentation.
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