- This product will Coming soon.
AI Red Team Analyst [AI-RTA]
- Build a security foundation in AI/ML, GenAI, and LLMs.
- Understand AI attack surfaces and offensive techniques.
- Gain hands-on experience through practical labs.
- Explore industry frameworks like MITRE ATLAS, OWASP Top 10 for LLMs, and notable AI security case studies.
- Develop practical AI red teaming skills to identify, assess, and validate security risks in AI applications.
![AI Red Team Analyst [AI-RTA]](https://cyberwarfare.live/wp-content/uploads/2026/08/AIRTA.png)
- Execute direct and indirect prompt injection attacks to bypass guardrails and manipulate LLM behaviour.
- Exploit malicious MCP servers, indirect prompt injection, description typosquatting, command injection, excessive permissions, and multivector attacks.
- Build a RAG pipeline and perform RAG poisoning, tool calling abuse, and multi-agent trust exploitation.
- Perform data poisoning, backdoor attacks, and model evasion.
- Analyze and reproduce practical AI attack scenarios to understand AI security threats and offensive techniques.
AI Red Teaming Fundamentals
- AI Red Teaming Methodology and Objectives
- AI vs Traditional Red Teaming
- AI Attack Surfaces
- AI Threat Landscape
- MITRE ATLAS Framework
- OWASP Top 10 for LLM Applications
- AI Security Tools and Assessment Methodology
AI & LLM Foundations
- Artificial Intelligence, Machine Learning, and Deep Learning
- Generative AI and Foundation Models
- LLM Architecture
- Tokens, Embeddings, Context Windows
- Transformer Architecture and Self-Attention
- Hyperparameters
- AI Application Frameworks
LLM & Agent Security
- Prompt Engineering and Prompt Security
- Prompt Injection and Jailbreak Techniques
- Model Context Protocol (MCP) Architecture
- MCP Trust Model and Authentication
- MCP Security Risks and Tool Abuse
- Malicious MCP Servers and Tool Description Typosquatting
- Command Injection and Multi-Vector Agent Attacks
RAG & Model Security
- Retrieval Augmented Generation (RAG) Architecture
- RAG Components and Retrieval Workflow
- RAG Poisoning and Knowledge Base Manipulation
- Data Poisoning and Model Inference Attacks
- Adversarial Perturbations and Model Extraction
- Backdoor Attacks and Model Evasion
Hands-on Labs & Case Studies
- Prompt Injection and Jailbreak Labs
- MCP Attack Simulation Labs
- RAG Poisoning and Tool Abuse Labs
- Data Poisoning and Adversarial Attack Labs
- Backdoor and Model Evasion Demonstrations
- Real-World AI Security Case Studies
- Offensive AI Red Teaming Exercises
Pre-requisites
The following are the requirements:
- 16 GB+ RAM system capable of running a hypervisor (VMware Workstation preferred)
- 50 GB or more of available storage
- Basic understanding of cybersecurity concepts
- Basics of Python, web technologies, and APIs
- Curiosity to explore AI security concepts and offensive testing techniques
- No GPU or prior AI/ML experience required
Target Audience:
Designed specifically for practitioners operating at the intersection of AI and offensive security:
- Security Professionals who want to expand into AI and LLM security testing.
- AI/ML Engineers & Developers who want to understand AI security risks.
- Application Security & Cloud Professionals building AI applications.
- Students & IT Professionals who want to build practical AI red teaming skills.
To earn the CWL Offensive Cyber Operations with AI (OCO-AI) Certificate, participants must:
Premium Version
Certified AI Red Team Analyst [AI-RTA]Â
$99 $9
Top features:
- 150+ Pages Premium Course Material
- 11+ Hands-on offensive labs across 7 Modules
- 10+ Practical Exercises with Walkthroughs
- CWL Verified AI-RTA Certificate
- Access to CWL Red Team Community (Discord)
F.A.Q
The release date is set to 6th Aug 2026. The exam scenario and certification will be announced on our website and social media channels.Â
Study materials and labs are available via the CWL Labs Portal (CCSP)
https://labs.cyberwarfare.live
Once enrolled, you can access the video lectures, PDF materials, and lab environments using your registered email.
Yes. You will have lifetime access to the course materials and recorded content. Lab environments may receive updates periodically.
The estimated completion time is 30 Days, depending on your experience level and learning pace.
After completing the course, learners may pursue roles such as:
- AI Red Team Operator
- AI Security Engineer
- Penetration Tester
- Application Security Engineer
- AI/ML Security Engineer
- Security Researcher
CWL follows a strict no-refund policy once course access has been granted although we can exchange the same value courses.Â
If your query isn’t listed here, feel free to contact [email protected].
