AI Pentesting Course: Practical Training for Offensive Security Professionals

AI Pentesting Course: Practical Training for Offensive Security Professionals

A dedicated AI pentesting course equips security professionals with the skills to identify and exploit vulnerabilities in machine learning models, data pipelines, and AI-driven applications. It covers adversarial attacks, model inversion, prompt injection, and data poisoning, using open-source tools like ART and Counterfit. Designed for red teams, penetration testers, and security analysts, the course includes hands-on labs, attack simulations, and reporting frameworks aligned with industry standards.

The AI Pentesting Course delivers 12 modules of practical training, including threat modelling for AI systems, bypassing detection mechanisms, and assessing model robustness under real-world conditions. Participants gain access to a sandboxed environment with pre-configured models and datasets, allowing safe experimentation with evasion techniques.

Who Should Enrol?

This course is designed for experienced penetration testers and security engineers who already understand network and application security but lack specific knowledge of AI system weaknesses. It assumes familiarity with Python, common attack frameworks like MITRE ATT&CK, and basic machine learning concepts such as classification and regression.

Security teams responsible for auditing AI-powered chatbots, fraud detection engines, or autonomous decision systems will benefit most. The course does not teach introductory programming or data science; instead, it focuses on offensive techniques tailored to AI architectures.

Core Modules and Practical Skills

The curriculum is structured around real attack vectors and defensive blind spots. Each module combines theory with lab exercises, ensuring participants can apply what they learn immediately.

Module 1: Introduction to AI System Architecture Covers the components of typical AI deployments, data ingestion, preprocessing, model serving, and feedback loops. Emphasises entry points for attackers, such as unsecured APIs and poorly validated inputs.

Module 2: Threat Modelling with STRIDE Uses Microsoft’s STRIDE framework to classify threats specific to AI systems: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. Participants map these to AI components like training data stores and inference endpoints.

Module 3: Adversarial Example Generation Teaches how to craft inputs that deceive models without being detected. For image classifiers, this includes pixel-level perturbations; for language models, it involves subtle word substitutions that alter meaning.

Module 4: Model Inversion and Membership Inference Demonstrates how attackers can reconstruct training data or determine whether a specific record was used to train a model, critical for privacy compliance under regulations like GDPR.

Module 5: Data Poisoning Attacks Shows how malicious actors can manipulate training datasets to introduce backdoors or degrade model accuracy. Includes techniques for injecting poisoned samples during online learning phases.

Module 6: Prompt Injection and Jailbreaking Focuses on large language models (LLMs), teaching how to craft inputs that override safety filters, extract system prompts, or induce unintended behaviour. Covers both direct and indirect injection methods.

Module 7: API Exploitation Examines common flaws in model-serving APIs, such as missing authentication, rate limiting bypass, and exposure of model metadata. Uses tools like Postman and Burp Suite to test endpoints.

Module 8: Model Stealing and Extraction Guides participants through techniques to replicate proprietary models by querying them repeatedly and analysing responses, a risk for companies relying on AI as a competitive advantage.

Module 9: Physical World Attacks Explores how adversarial examples can be printed or displayed in real environments to fool computer vision systems, such as traffic sign recognition in autonomous vehicles.

Module 10: Defensive Countermeasures While the course is offensive in nature, it includes a module on detection and mitigation, helping testers understand what defences look like so they can assess their effectiveness.

Module 11: Reporting and Communication Teaches how to document AI-specific findings in a way that resonates with technical and non-technical stakeholders, using standard templates and risk scoring methods.

Module 12: Capstone Exercise Participants conduct a full assessment of a simulated AI-powered customer service platform, identifying vulnerabilities across data, model, and deployment layers, then present findings in a formal report.

Alignment with Industry Frameworks

The course integrates key principles from recognised standards to ensure relevance and credibility. These include:

• NIST AI Risk Management Framework (AI RMF): Participants learn to categorise risks using the framework’s four functions: Govern, Map, Measure, and Manage.
• MITRE ATLAS: The Adversarial Threat Landscape for AI Systems provides a taxonomy of known attacks, which the course uses to structure lab scenarios.
• OWASP Top 10 for Machine Learning: Each module maps to one or more of the top risks, such as model theft or data leakage.

These frameworks ensure that assessments are repeatable, comprehensive, and aligned with regulatory expectations.

Why This Course Stands Out

Unlike generic cybersecurity courses, this programme focuses exclusively on AI-specific attack surfaces. It avoids theoretical overviews in favour of practical, tool-based exercises. The lab environment includes:

• Pre-trained models vulnerable to known exploits
• Simulated enterprise networks with AI components
• Logging and monitoring tools to analyse attack impact

All exercises are designed to mimic real-world conditions, preparing testers for engagements in finance, healthcare, and critical infrastructure sectors.

Graduates of the course can confidently assess AI systems for security weaknesses, produce actionable reports, and advise on risk mitigation strategies, skills increasingly in demand as AI adoption grows.

For more information and immediate access to the training materials, visit the AI Pentesting Course.