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Agentic AI Security Red Team Agents Safely
Published 9/2026
Created by PapaHR ★ 170K students: Courses in Human Resources, HR, SHRM, AI Talent Analytics, HRMS, HRIS, CIPD, Claude, HRCI, PHR, Reward
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 8 Lectures ( 6h 50m ) | Size: 3 GB
Test AI agents for prompt injection, tool misuse, memory poisoning, multi-agent attacks, sandboxing and failure cascades
What you'll learn
Map agent attack surfaces across goals, planning, tool use, memory, delegation, trust boundaries, and side effects. Design multi-turn prompt injection and goal-hijacking tests with explicit success, failure, containment, and recovery oracles. Trace unsafe tool affordance chains from attacker-controlled sources to simulated forbidden states without touching live systems. Test memory poisoning, session persistence, cross-user isolation, cleanup, and retained utility with benign synthetic canaries. Build reproducible cross-agent spoofing, replay, privilege-laundering, context-smuggling, and collusion scenarios. Configure a deterministic agent evaluation sandbox with fake tools, local sinks, fixed budgets, stop controls, and verified reset. Measure first-cycle failure, trajectory attack success, cascade incidence, propagation, depth, amplification, containment, recovery, and utility. Run an authorized agentic red-team campaign and connect evidence, findings, remediation, regression tests, and release decisions.Requirements
Familiarity with core cybersecurity concepts such as identity, permissions, logs, and trust boundaries. Ability to read simple JSON, HTTP request fields, or structured event records; no programming is required for the supplied exercises. A desktop spreadsheet application for the editable workbooks and a modern browser for the self-contained sandbox. Written authorization and an isolated, resettable test environment before adapting any exercise to a company workflow.DescriptionThis course contains the use of artificial intelligence.
Agentic AI systems can plan across several steps, call tools, store memory, delegate work, and affect othercomponents. This hands-on course gives cybersecurity professionals a safe, structured way to test thosesystems before release. You will build reproducible agent security evaluations that produce observableevidence, actionable findings, and clear remediation decisions.
Traditional prompt testing often ends when the model returns a response. An AI agent can fail later: after aretrieved document changes its goal, after an allowed tool result feeds another call, after a poisoned memoryrecord returns in a new session, or after one peer passes tainted authority to another. A final refusal can alsohide an earlier planning deviation, while a successful tool block can contain impact without fixing the source.Effective agentic red-teaming must therefore follow the complete trajectory.
The course shows you how to move from isolated prompts to bounded security experiments. You will defineprotected properties before each run, compare clean and adversarial conditions from the same frozen state,preserve causal links between events, and verify both containment and retained utility. Instead of saying thatan output "looked unsafe," you will be able to identify the first changed field, the control that allowed orblocked it, the downstream components affected, the final simulated state, and the evidence required for aretest.
Every exercise uses the fictional SentinelWorks security operations workflow and synthetic incident INC-1042.The environment contains mock identities, fake canaries, local sinks, reversible state, deterministic scripts,and explicit stop conditions. No exercise requires production credentials, customer data, public targets,destructive payloads, host mounts, uncontrolled egress, or third-party systems. If you do not have anauthorized company sandbox, the supplied case is a complete practice path.
You begin by mapping the agent loop. You will trace how a goal becomes a plan, how the plan selects tools,how results return to context, how memory influences later decisions, and how agents delegate to peers. TheAgent Attack Surface Map turns this architecture into trust boundaries, attack paths, and a prioritized testbacklog. This becomes the foundation for every later test.
Next, you test goal integrity across multiple turns. You will freeze the original task, success condition, allowedactions, forbidden actions, and stop condition. Then you will place a harmless conflict in a user message, tooloutput, retrieved document, memory recall, or peer message. You will compare the clean baseline with theattack trace, locate the first deviation, and distinguish clean behavior, containment, recovery, and completedhijack. The Multi-Turn Goal Hijack Test Pack preserves the sequence, oracle, evidence, cleanup, and retest.
The tool-use module moves from individual permissions to complete affordance chains. You will inventoryprincipals, schemas, resources, side effects, approval gates, reversibility, and detection signals. Then you willconnect individually allowed fake actions into a simulated forbidden outcome. The exercise demonstrates whytool security cannot be evaluated one call at a time. You will capture every request, policy decision, statetransition, containment point, rollback, and repeated run in the Tool Affordance Chain Mapper.
Memory receives its own end-to-end test. You will map session context, profile records, retrieval indexes,cached summaries, writers, readers, scope, retention, provenance, and reset. A benign canary lets you followone exact record from write through storage, retrieval, planning, cleanup, and utility retest. You will verifywhether it survives a fresh session, crosses to another synthetic user, appears in a derived index or cache, orcontinues to influence state after the visible record is removed. The Memory Poisoning Test Protocol keepsevery source and derived store auditable.
Agent networks add identity, delegation, replay, and evidence-lineage risk. You will work with six scriptedpeers that have different capabilities and trust tiers. Structured message contracts make sender, recipient,requested capability, asset, freshness, nonce, evidence, and delegation depth explicit. You will run forged-sender, replayed-approval, privilege-laundering, context-smuggling, and colluding-peer scenarios. The Multi-Agent Scenario Builder records delivery, acceptance, propagation, causal depth, containment, final state, andclean-path utility.
You will then operate a self-contained Sandboxed Agent Lab. The browser file uses no external network,persistent storage, file upload, arbitrary code field, model call, or live integration. It contains deterministicagents, fake tools, synthetic memory, mock mail and ticket services, fixed step and tool budgets, circuitbreakers, state hashing, invariant checks, local report export, and verified reset. You can run injection, unsafetool-chain, memory-poisoning, retry-loop, and clean-control scenarios one step at a time or as a completetrace.
The measurement module explains how to separate an early model or planner failure from a system-levelcascade. You will create four matched conditions: clean without the evaluated control, attack without thecontrol, clean with the control, and attack with the control. From raw run and event records, you will calculatefirst-cycle failure, trajectory attack success, cascade incidence, affected reach, edge propagation, maximumcausal depth, amplification, detection latency, time to containment, recovery completeness, clean success,and utility retention. The workbook keeps numerator, denominator, exclusions, versions, causal links, andevidence visible. The course does not impose universal thresholds; you will define action zones for theprotected asset and approved risk tolerance.
Finally, you will assemble an authorized red-team campaign. The Agentic Red-Team Engagement Packconnects written authorization, system boundary, test identities, excluded actions, stop authority, scenariocoverage, execution runbook, evidence log, finding lineage, remediation owner, regression test, retest record,and release gate. You will learn how to write a bounded claim, preserve controls that already work, fix theearliest practical weak boundary, and prove that the security change does not destroy required clean behavior.
The course is taught by Mike Pritula, the #1 HR instructor on Udemy. More than 2,000,000 students haveenrolled in his Udemy courses, while 170,000+ students have studied at Mike Pritula Academy. These areseparate learning communities and are not combined. Mike's professional experience includes Wargaming,Preply, iDeals, Starlightmedia, Sense Bank, and PeopleForce. The course follows the academy's practicalformat: a clear operating model, an editable tool, a guided case, a completion check, and homework that canbe completed in the supplied synthetic environment.
Across eight lessons, you will create an Agentic Red-Team Lab Kit
Agent Attack Surface Map Multi-Turn Goal Hijack Test Pack Tool Affordance Chain Mapper Memory Poisoning Test Protocol Multi-Agent Scenario Builder Sandboxed Agent Lab Agent Failure Metrics Workbook Agentic Red-Team Engagement PackWhat's included
Lifetime access to all course materials Active instructor support in Q&A Udemy Certificate of Completion Practical assignments and a complete synthetic business case Editable workbooks, a self-contained browser lab, and a reusable engagement document Reproducible examples for clean, attack, defended, cleanup, and retest conditionsEnroll now and start your first lesson today.
Who this course is for
Cybersecurity engineers who assess or secure tool-using generative AI agents and autonomous workflows. Cybersecurity analysts who need reproducible evidence for prompt injection, memory, tool, or multi-agent findings. DevSecOps engineers who build predeployment agent security tests, sandbox controls, regression suites, and release gates. Security architects and AI platform owners who need a practical way to connect agent failure traces with remediation decisions.Homepage
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