Certified AI Penetration Tester – Red Team Tutorial

Module 1 — Course Overview and Learning Path

Learning objectives:

• Understand the CAIPT-RT certification scope, expected competencies, exam format, and learning milestones.
• Map course modules to real-world red team activities against AI systems.
• Build a study plan and identify required software, hardware, and datasets.
  Lessons:
• What a Certified AI Penetration Tester — Red Team does.
• Exam blueprint, skill levels, and hands-on vs. theoretical balance.
• Lab environment setup: VMs, GPU options, containerization, virtual networks, and safe isolated testing practices.
  Practical lab:
• Create an isolated test environment (VM or container), install Python, Docker, an ML framework (TensorFlow or PyTorch), and a local model-serving tool. Configure network isolation and snapshot the environment.
  Assessment:
• Short quiz on exam structure and environment checklist.
  Deliverable:
• Environment build log and screenshot(s) showing components installed.

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Certified AI Penetration Tester – Red Team (CAIPT-RT) Certification Course by Tonex

• Public Training with Exam: November 20-21, 2025

REGISTER

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Module 2 — AI and ML Fundamentals for Red Teamers

Learning objectives:

• Grasp core ML concepts and the ML lifecycle from data collection to deployment.
• Distinguish model types (classification, regression, generative, sequence models) and common architectures (CNN, RNN, Transformer).
  Lessons:
• Supervised, unsupervised, and reinforcement learning fundamentals.
• Feature engineering, training/validation/test splits, overfitting, and evaluation metrics.
• Model deployment patterns: batch, real-time, microservice, edge.
  Practical lab:
• Train a small text classifier and a simple Transformer-based generation model on toy datasets. Evaluate metrics and produce a saved artifact for later attack labs.
  Assessment:
• Short problem set calculating metrics (precision, recall, AUC) and diagnosing overfitting.
  Deliverable:
• Trained model artifacts and a short report describing training decisions.

Module 3 — ML Security Threat Model and Attack Surface

Learning objectives:

• Define threat models specific to AI systems and identify attack surfaces.
• Prioritize targets and enumerate realistic attacker capabilities and goals.
  Lessons:
• Attack surface taxonomy: data, model, inference API, pipeline, supply chain, and human interfaces.
• Assets and adversary goals: confidentiality, integrity, availability, privacy, and safety.
• Risk assessment and scoring for AI systems.
  Practical lab:
• Build a threat model for a sample AI service (e.g., image classification API) using a template: assets, actors, entry points, and mitigations.
  Assessment:
• Create a prioritized attack plan with rationales and expected impact.
  Deliverable:
• Threat model document and prioritized attack matrix.

Module 4 — Data Attacks: Poisoning, Backdoors, and Privacy Leakage

Learning objectives:

• Understand and execute data poisoning and backdoor attacks in controlled environments.
• Identify privacy leakage vectors and test models for membership inference and data extraction risk.
  Lessons:
• Training-time threats: targeted poisoning, indiscriminate poisoning, and backdoor insertion.
• Privacy attacks: membership inference, attribute inference, model inversion, and differential privacy basics.
• Defenses: data validation, anomaly detection, differential privacy, robust training.
  Practical lab:
• Implement a label-flipping poisoning attack and a simple backdoor insertion against a small image classifier, then measure attack success.
• Run membership inference tests on a trained model and report confidence distributions.
  Assessment:
• Lab report documenting attack steps, success metrics, and suggested mitigations.
  Deliverable:
• Attack artifacts (code, poisoned dataset) and analysis report.

Module 5 — Model Extraction, Reconstruction, and Intellectual Property Attacks

Learning objectives:

• Learn techniques for model extraction and model stealing from queryable APIs.
• Assess economic and confidentiality impact of model theft.
  Lessons:
• Query-based extraction strategies, transfer learning and surrogate training, caching and output truncation attacks.
• Measuring extraction success: accuracy vs. query budget, fidelity metrics, and feature-space comparisons.
• Mitigations: API rate limiting, query shaping, watermarking, and output randomization.
  Practical lab:
• Simulate a model extraction attack against a deployed classifier API with a limited query budget and train a surrogate model; evaluate fidelity.
  Assessment:
• Provide an extraction report with queries used, surrogate performance, cost estimate, and recommended mitigations.
  Deliverable:
• Scripts used for extraction, surrogate model, and evaluation charts.

Module 6 — Evasion and Adversarial Examples

Learning objectives:

• Create and test adversarial examples against image, text, and audio models.
• Understand transferability, robustness testing, and defenses.
  Lessons:
• Gradient-based attacks (FGSM, PGD), optimization-based attacks (CW), and decision-based/black-box attacks.
• Adversarial attacks for text: token-level, paraphrase-based, and embedding-space methods.
• Evaluation: success rates, perceptibility metrics, and robustness benchmarks.
  Practical lab:
• Generate white-box and black-box adversarial examples for an image classifier; apply adversarial training as a defense and measure results.
• Craft text-level adversarial prompts to manipulate a language model’s output in a controlled scenario.
  Assessment:
• Comparative analysis of attack methods and defense effectiveness.
  Deliverable:
• Attack scripts, adversarial samples, and robustness evaluation report.

Module 7 — Prompt, Instruction, and Policy Evasion for Large Language Models

Learning objectives:

• Identify and execute prompt-based red team techniques to elicit undesired behaviors.
• Understand instruction-following failures, jailbreak patterns, and safety policy bypass strategies.
  Lessons:
• Prompt engineering fundamentals, jailbreak taxonomy, few-shot/chain-of-thought manipulation, and context injection.
• Safety policy enforcement mechanisms and common weaknesses.
• Measuring risk: harm classification, toxic content generation, and confidentiality leakage.
  Practical lab:
• Design and run red team prompt sequences against an open-source LLM instance to induce specified undesired outputs; log prompts and model responses.
• Evaluate and categorize successful jailbreaks, quantify prompt lengths and context that led to failure.
  Assessment:
• Curated repository of prompt patterns and a mitigation plan with prompt filters and runtime checks.
  Deliverable:
• Prompt logbook, success rate metrics, and recommended countermeasures.

Module 8 — Supply Chain, CI/CD, and Model Deployment Attacks

Learning objectives:

• Assess risks in data and model pipelines, CI/CD for ML, and third-party model dependencies.
• Simulate tampering and lateral movement in the ML deployment lifecycle.
  Lessons:
• Attack paths in data ingestion, model training pipelines, artifact stores, and orchestration systems.
• Compromising dependencies: poisoned open-source models, malicious pre-trained checkpoints, and compromised packages.
• Hardening CI/CD: signing artifacts, reproducible builds, and integrity checks.
  Practical lab:
• Simulate a compromised model checkpoint introduced into a pipeline; demonstrate behavioral changes and propose detection heuristics.
  Assessment:
• Pipeline security checklist and a simulated incident report with containment and remediation steps.
  Deliverable:
• Incident report, detection rules, and patched pipeline recommendations.

Module 9 — Red Team Methodology, Automation, and Tooling

Learning objectives:

• Apply classical red team methodology to AI systems and build automated attack pipelines.
• Design repeatable tests and integrate them into continuous red teaming.
  Lessons:
• Reconnaissance for AI assets, crafting hypotheses, iterative testing, and evidence collection.
• Tooling: adversarial toolkits, model probing frameworks, API fuzzers, and orchestration with CI.
• Reporting: executive summaries, technical findings, reproduction steps, and remediation guidance.
  Practical lab:
• Create a reproducible attack automation pipeline that runs a set of tests (extraction, membership inference, prompt jailbreaks) against a test service and produces a consolidated report.
  Assessment:
• Submit the automation pipeline and a sample consolidated report from a run.
  Deliverable:
• Automation scripts, configuration files, and generated report.

Module 10 — Defensive Techniques and Mitigation Engineering

Learning objectives:

• Recommend and implement effective mitigations against red team techniques.
• Balance security controls with utility and performance.
  Lessons:
• Input sanitization, output filtering, rate limiting, differential privacy, robust training, and runtime monitoring.
• Detection strategies: anomaly detection on inputs/outputs, logging, and model performance drift monitoring.
• Incident response tabletop exercises tailored to AI incidents.
  Practical lab:
• Implement runtime filters and monitoring for a model API; simulate attacks and demonstrate detection or blockage.
  Assessment:
• Create a mitigation roadmap prioritizing low-effort/high-impact controls and a TTP-to-detection mapping.
  Deliverable:
• Mitigation roadmap and demonstration logs showing controls in action.

Module 11 — Legal, Ethical, and Compliance Considerations

Learning objectives:

• Understand legal boundaries, ethical constraints, and responsible disclosure practices for AI red teaming.
• Apply frameworks for consent, data handling, and cross-border considerations.
  Lessons:
• Legal considerations for penetration testing and data privacy (authorization, contracts, lawful scope).
• Ethical red teaming: avoiding harm, handling sensitive data, and escalation procedures.
• Responsible disclosure workflows and stakeholder communication templates.
  Practical lab:
• Draft a rules-of-engagement (ROE) document and a responsible disclosure template for an AI red team engagement.
  Assessment:
• Review of ROE against hypothetical scenarios and identification of potential legal/ethical gaps.
  Deliverable:
• Finalized ROE and disclosure templates.

Module 12 — Capstone Project and Certification Readiness

Learning objectives:

• Execute a full-scope red team engagement against a representative AI system and produce a professional final deliverable.
• Demonstrate practical mastery across attack categories, reporting, and mitigation design.
  Project brief:
• Given a simulated target (an image classification API + LLM assistant + model registry), perform reconnaissance, prioritize targets, perform at least three distinct attack types (e.g., extraction, poisoning/backdoor, prompt jailbreak), automate at least one test, and produce a complete engagement report with remediation steps.
  Requirements:
• Provide reproduction steps, scripts, proof artifacts (logs, metrics, sample payloads), impact assessment, and remediation plan.
  Evaluation criteria:
• Technical correctness, novelty of attack vectors, clarity of reporting, and practicality of mitigations.
  Certification readiness checklist:
• Hands-on labs completed, ROE understood, environment reproducible, incident response practiced, and study of knowledge objectives.
  Final assessment:
• Proctored or instructor-graded practical exam simulating a timed engagement plus a short written exam covering theory and risk assessment.
  Deliverable:
• Capstone report, artifact repository, and a post-engagement presentation.

Want to learn more? Tonex offers Certified AI Penetration Tester – Red Team (CAIPT-RT) Certification, a 2-day course where participants gain expertise in AI penetration testing methodologies and develop skills in identifying and exploiting AI vulnerabilities.

Attendees also learn advanced techniques for securing AI-based applications and systems, learn the intersection of AI and cybersecurity for effective threat detection, gain expertise in AI penetration testing methodologies, develop skills in identifying and exploiting AI vulnerabilities and learn advanced techniques for securing AI-based applications and systems.

Students as well as professionals can benefit from this course, which is ideal for cybersecurity professionals, ethical hackers, penetration testers, and IT professionals seeking specialized knowledge in AI security.

Additionally, the course is suitable for individuals responsible for securing AI-powered applications and systems.

Tonex is the leader in AI certifications, offering more than six dozen courses, including in the Certified GenAI and LLM Cybersecurity Professional area, such as:

Certified AI Data Strategy and Management Expert (CAIDS) Certification

Certified AI Compliance Officer (CAICO) certification 

Certified AI Electronic Warfare (EW) Analyst (CAIEWS)

Certified GenAI and LLM Cybersecurity Professional (CGLCP) for Professionals   

Certified GenAI and LLM Cybersecurity Professional for Data Scientists

Certified GenAl and LLM Cybersecurity Professional for Developers Certification

Certified GenAI and LLM Cybersecurity Professional for Security Professionals (CGLCP-SP) Certification

Additionally, Tonex offers even more specialized AI courses through its Neural Learning Lab (NLL.AI). Check out the certification list here.

For more information, questions, comments, contact us.