Certified AI Red Teaming Professional (CAIRTP) Certification Program by Tonex
Certified AI Red Teaming professional Certification is a 2-day course where participants learn AI-specific attack vectors and threat models as well as learn to apply OWASP LLM Top 10 principles in AI red teaming.
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Ensuring safety, fairness, and robustness is more crucial than ever as AI systems continue to integrate into critical infrastructures and everyday services.
This is where Certified AI Red Teaming Professionals (CAIRTPs) come in. These experts are trained to simulate adversarial attacks and stress-test AI models. To perform these tasks effectively, CAIRTPs rely on a suite of cutting-edge technologies.
At the heart of AI red teaming lies adversarial machine learning. CAIRTPs use open-source AML frameworks like:
- CleverHans: Developed by Google Brain, this Python library helps test the vulnerability of neural networks to adversarial examples.
- Foolbox: A library built on PyTorch, TensorFlow, and JAX that allows red teamers to conduct attacks such as FGSM, DeepFool, and Carlini & Wagner.
- ART (Adversarial Robustness Toolbox) by IBM: A comprehensive framework that supports multiple attacks and defenses, widely used for both research and practical testing.
These tools allow professionals to craft perturbations that deceive models while appearing benign to humans.
Understanding how and why a model makes certain predictions is equally vital. CAIRTPs use tools such as:
- LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations): These help red teamers explain model decisions, identify biases, and detect spurious correlations.
- Captum (for PyTorch): Offers integrated gradients and other methods to visualize model sensitivity and interpret behavior.
These interpretability frameworks are essential when probing AI for fairness, transparency, and ethical compliance.
Professionals often rely on platforms designed for AI auditing, such as:
- Microsoft’s Counterfit: A robust automation framework for testing AI model security against adversarial threats.
- SecML: An open-source Python library for security evaluation of machine learning models with a strong focus on structured pipelines.
These tools support automated testing pipelines and compliance reporting.
Traditional red teaming intersects with AI through tools like:
- Metasploit with AI-focused plugins
- Burp Suite extensions for detecting LLM prompt injection vulnerabilities
- Custom scripts to exploit AI APIs and prompt logic flaws
These are particularly useful for testing large language models (LLMs) and AI-integrated web applications.
To simulate realistic attack environments or rare data scenarios, CAIRTPs use:
- Gretel.ai or Synthia: For generating synthetic yet statistically similar datasets
- Unity ML-Agents: To simulate reinforcement learning environments for AI system stress-testing
These platforms allow CAIRTPs to create robust test conditions, ensuring AI models are evaluated under diverse scenarios.
Bottom Line: The role of a Certified AI Red Teaming Professional combines cybersecurity acumen with AI expertise. By leveraging AML toolkits, interpretability tools, governance platforms, and simulation environments, CAIRTPs play a critical role in ensuring the trustworthiness of modern AI systems.
As AI adoption grows, so does the need for these professionals and the technologies they wield.
Certified AI Red Teaming Professional (CAIRTP) Certification Program by Tonex
The Certified AI Red Teaming Professional (CAIRTP) certification program by Tonex is designed to equip cybersecurity professionals, red teamers, and AI engineers with the advanced knowledge and skills needed to test and harden GenAI systems, LLMs, and AI pipelines.
Participants explore emerging threats in AI, including jailbreaking, prompt injection, and model manipulation. The curriculum dives deep into OWASP Top 10 for LLMs and offers hands-on exposure to adversarial techniques, such as retrieval-augmented generation (RAG) attacks and vector store poisoning. Tools like Garak, Promptbench, AttackGen, and LLM-attacks are introduced for offensive security testing of AI systems.
This program is highly relevant in today’s evolving AI threat landscape. With AI systems becoming integrated into critical infrastructure and decision-making, cybersecurity risks have escalated. CAIRTP provides the strategies to preemptively identify vulnerabilities and secure AI applications, making it vital for AI-integrated cybersecurity resilience.
Audience:
- Cybersecurity Professionals
- Red Team Practitioners
- AI Security Analysts
- Machine Learning Engineers
- Penetration Testers
- AI/ML Architects
Learning Objectives:
- Understand AI-specific attack vectors and threat models
- Apply OWASP LLM Top 10 principles in AI red teaming
- Execute prompt injection and jailbreak scenarios
- Conduct RAG-based adversarial testing
- Use red teaming tools tailored for GenAI and LLMs
- Analyze and mitigate AI-specific vulnerabilities
Program Modules:
Module 1: Foundations of AI Red Teaming
- Overview of AI red teaming methodology
- Understanding GenAI security surface
- Threat modeling for LLM-based systems
- Risk analysis for AI pipeline components
- Compliance and regulatory awareness
- Ethics in offensive AI testing
Module 2: OWASP Top 10 for LLMs Deep Dive
- Overview of OWASP LLM Top 10
- Prompt injection vulnerabilities
- Insecure output handling
- Excessive agency and authorization flaws
- Data leakage risks
- Mitigation techniques
Module 3: Prompt Injection & Jailbreaking Tactics
- Anatomy of prompt injection attacks
- Jailbreak prompt design and impact
- Role of temperature and randomness
- Encoding bypass and context pollution
- Prevention strategies
- Prompt filtering best practices
Module 4: Attacks on Vector Databases & RAG
- What is RAG and why it’s vulnerable
- Vector store poisoning explained
- Inference manipulation via knowledge injection
- Index tampering and search disruption
- Tool-assisted RAG attack demonstrations
- Countermeasures and monitoring
Module 5: Testing Autonomous Agents
- Autonomous agent architecture
- Simulated adversarial behavior injection
- Prompt chaining vulnerabilities
- Looping attacks and misaligned goals
- Manipulating tool usage
- Securing agent workflows
Module 6: Red Teaming Tools & Strategy
- Overview of Garak and its capabilities
- Using Promptbench for benchmark analysis
- Automating attacks with AttackGen
- Exploring LLM-attacks toolkit
- Choosing the right tool for the objective
- Building red teaming workflows for AI systems
Exam Domains:
- AI Threat Modeling and Attack Surface Analysis
- Prompt-Based Vulnerability Identification
- Secure RAG and Vector Store Operations
- AI Red Teaming Strategy and Governance
- Adversarial Testing of LLM Applications
- Tools and Techniques for GenAI Offense
Course Delivery:
The course is delivered through lectures, expert-led interactive sessions, and guided exercises using real-world tools. Participants will access curated resources, toolkits, and case examples to build hands-on red teaming capabilities specifically for AI systems.
Assessment and Certification:
Participants are assessed via quizzes, structured assignments, and a final project. Upon successful completion, participants receive the Certified AI Red Teaming Professional (CAIRTP) certification by Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Red Teaming Professional (CAIRTP) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to test and secure the next generation of AI systems? Join the CAIRTP program and become a certified expert in red teaming for LLMs and AI pipelines. Defend the future with knowledge. Enroll today!
