Offensive AI Red Team Operator
A 42-module offensive AI security course covering LLM applications, MCP-style tool exposure, agentic workflows, RAG systems, model infrastructure, cloud AI attack surface, detection, remediation, and professional reporting.
One operator track for modern AI attack surfaces.
Offensive AI Red Team Operator is built for practitioners who need to assess AI-enabled products as real security targets. The course moves from foundations into exploitation, validation, evidence handling, defensive mapping, and reporting.
The public page shows the syllabus and outcomes. The detailed lesson content, walkthrough videos, exercises, and knowledge checks stay inside the academy learning flow after enrollment.
AI security, MCP exposure, and agentic AI in one course.
The course covers the areas often sold separately: AI/LLM security fundamentals, MCP-style tool and connector risk, agentic workflows, RAG abuse, model/pipeline exposure, and practical defensive validation.
What the course is built to cover.
The curriculum is organized around the attack surfaces security teams are now expected to understand, test, explain, and defend.
AI and LLM Security
Prompt injection, jailbreaks, insecure output handling, model behavior abuse, and AI application assessment workflow.
MCP and Tool Exposure
Tool invocation risk, connector abuse, excessive permissions, tool poisoning patterns, and validation of AI-controlled actions.
Agentic AI Security
Agent workflows, memory abuse, planning loops, multi-step misuse, automation risk, and control design for autonomous systems.
RAG and Data Leakage
Retrieval abuse, indirect injection, context leakage, hidden content, embedding exposure, and sensitive information paths.
Model and Pipeline Risk
Model artifacts, AI supply chain, MLSecOps, CI/CD exposure, dependency risk, signing, provenance, and deployment validation.
Defense and Reporting
Detection logic, canaries, telemetry, remediation, re-testing, severity scoring, executive summaries, and evidence-backed reports.
Curriculum overview.
The course is grouped into ten parts so learners can understand the progression without exposing the full lesson material publicly.
Security and DevSecOps Baseline
Threat modeling, CI/CD, infrastructure, supply chain, containers, Kubernetes, and the security context needed before AI-specific testing.
How AI Products Are Built
AI application architecture, MLSecOps, model workflows, AI threat modeling, and the vulnerable application model used through the course.
Prompt Injection and LLM Abuse
Direct and indirect prompt injection, jailbreak strategy, RAG abuse, agent misuse, model extraction, and offensive tooling workflow.
Evidence, Scoring, and Communication
Finding validation, reproduction notes, AIVSS scoring, AISVS mapping, executive summaries, and technical reporting discipline.
MCP, Agents, and Model Attacks
MCP-style exposure, adversarial ML, output handling, multi-agent abuse, tool execution risk, and full attack-chain development.
Leakage and Side Channels
Token timing, embedding leakage, cache behavior, hidden document content, covert output channels, and practical impact validation.
Cloud and AI Platform Attack Surface
Endpoint discovery, model routing, multimodal injection, supply-chain exposure, cloud AI services, and production deployment review.
Coding Agents and Autonomous Testing
Coding assistants, automation platforms, autonomous red-team workflows, skill/protocol abuse, and agentic security validation.
Detection and Continuous Validation
Privacy validation, telemetry, detection rules, canaries, release gates, and repeatable AI red-team regression testing.
Final Operator Validation
Assessment planning, exploitation, remediation guidance, re-test evidence, and final synthesis across the offensive AI workflow.
Designed for focused progression.
Each module is structured so learners can move through theory, walkthrough, exercise, and knowledge validation in a predictable flow.
Theory Lessons
Concise written material explains the security reasoning before demonstrations.
Walkthrough Videos
Guided video pages support practical demonstration and learning pace.
Exercises
Practice tasks reinforce the lesson flow and prepare learners for applied validation.
Knowledge Checks
Module-level checks help confirm understanding before moving forward.
What learners should be able to do.
Assess AI systems as security targets
Plan and execute AI-focused assessments across applications, retrieval layers, agents, tools, model workflows, and cloud-connected services.
Prove impact with evidence
Validate findings with reproduction steps, captured evidence, severity context, and mappings to practical control frameworks.
Communicate risk professionally
Produce clear reports, executive summaries, remediation guidance, re-test notes, and defensive recommendations.
For practitioners moving into offensive AI security.
Designed for penetration testers, red teamers, application security engineers, cloud and platform security engineers, DevSecOps practitioners, AI/ML security teams, security researchers, and consultants.
