Program Highlights
InfosecTrain’s AI Penetration Testing Training equips Cybersecurity Professionals, AI Researchers, and Penetration Testers to secure AI-driven systems. The course blends theory with hands-on labs, covering adversarial ML, OWASP ML/LLM Top 10, and real-world misconfigurations using tools like TensorFlow, PyTorch, Scikit-Learn, Hugging Face, LangChain, and Foolbox. Gain the offensive security skills essential for defending AI in today’s threat landscape.
24-Hour Instructor-led Training 
Learn from AI Security and Offensive Security Experts 
Hands-on Labs on AI/ML Attacks and Defenses 
Coverage of OWASP ML and LLM Top 10 Risks 
Real-world Case Studies on AI Security 
Adversarial ML and Prompt Injection Labs 
Interview Preparation for AI Security Roles 
Post-Training Mentorship and Career Guidance 
Access to Recorded Sessions
Training Schedule
- upcoming classes
- corporate training
- 1 on 1 training
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- Upskill your team on the latest tech
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- Get personalized attention
- Customized content
- Learn at your dedicated hour
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About Course
InfosecTrain’s AI Penetration Testing Training Course is a specialized 24-hour program that blends foundational AI/ML knowledge with cutting-edge adversarial testing techniques. Participants will learn the core concepts of ML and AI while gaining hands-on exposure to industry-standard tools and frameworks such as PyTorch, TensorFlow, Keras, and Scikit-Learn.
Participants will practice adversarial machine learning using Adversarial Robustness Toolbox, Foolbox, and Cleverhans, explore poisoning and model extraction attacks on CIFAR-10, and secure real-world deployments using Docker and Flask. For LLM security, the course covers Hugging Face, LangChain, OpenAI, Ollama, and LM Studio, enabling learners to test and secure modern AI applications.
The program also covers data science workflows with Pandas, NumPy, Matplotlib, Seaborn, and NLTK, ensuring learners can effectively manipulate data, visualize attacks, and analyze model vulnerabilities. By the end, participants will have a complete toolkit for testing and defending AI-powered systems against real-world adversarial threats.
Course Curriculum
- An overview of AI Security
- Basics of AI and ML
- What is AI?
- History and Evolution of AI
- Key Concepts in AI
- Types of AI
- Narrow AI vs. General AI
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Natural Language Processing (NLP)
- Computer Vision
- Core Components of AI Systems
- Algorithms and Models
- OWASP Top 10 Machine Learning
- ML01:2023 Input Manipulation Attack (Adversarial Attacks)
- ML02:2023 Data Poisoning Attack
- ML03:2023 Model Inversion Attack
- ML04:2023 Membership Inference Attack
- ML05:2023 Model Theft
- ML06:2023 AI Supply Chain Attack
- ML07:2023 Transfer Learning Attack
- ML08:2023 Model Skewing
- ML09:2023 Output Integrity Attack
- ML10:2023 Model Poisoning
- OWASP Top 10 LLM
- LLM01:2025 Prompt Injection
- LLM02:2025 Data Leakage
- LLM03:2025 Supply Chain Vulnerabilities
- LLM04:2025 Data and Model Poisoning
- LLM05:2025 Improper Output Handling
- LLM06:2025 Excessive Agency
- LLM07:2025 System Prompt Leakage
- LLM08:2025 Vector and Embedding Weaknesses
- LLM09:2025 Misinformation
- LLM10:2025 Unbounded Resource Consumption
- Data Poisoning
- Inject Malicious Samples Into Training Data
- Observe Model Drift and Misclassification
- Lab: Poisoned Spam Classifier Using Scikit-learn
- Adversarial Examples
- Generate Perturbations using FGSM, PGD
- Test Image Classifiers Against Adversarial Inputs
- Lab: Attack CIFAR-10 Model with Foolbox
- Model Extraction
- Query Black-box APIs to Reconstruct Model Logic
- Lab: Steal a Decision Tree via API Fuzzing
- Membership Inference
- Determine if a Sample was Part of Training Data
- Lab: Shadow Model Setup with TensorFlow
- Insecure Deployment
- Exploit Misconfigured ML APIs (e.g., No Auth, Verbose Errors)
- Lab: Dockerized Flask App with Exposed Model Endpoints
Target Audience
- Penetration Testers and Red Team Professionals
- Security Analysts and SOC Professionals
- AI/ML Engineers and Data Scientists
- Cybersecurity Consultants and Auditors
Pre-requisites
- Basic understanding of ML and AI concepts
- Familiarity with Python programming
- Knowledge of penetration testing fundamentals
- Understanding of common cybersecurity concepts
Course Objectives
After completing this training, participants will be able to:
- Gain a strong foundation in AI, ML, and core algorithms.
- Understand and test against OWASP ML Top 10 and LLM Top 10 vulnerabilities.
- Conduct data poisoning, adversarial examples, model extraction, and inference attacks.
- Secure AI/ML systems from API misconfigurations and supply chain threats.
- Simulate real-world AI threats in lab environments.
- Improve organizational resilience against AI-driven cyberattacks.
Vision
Goal
Skill-Building
Mentoring
Direction
Support
Success
It was a very good experience with the team. The class was clear and understandable, and it benefited me in learning all the concepts and gaining valuable knowledge.
I loved the overall training! Trainer is very knowledgeable, had clear understanding of all the topics covered. Loved the way he pays attention to details.
I had a great experience with the team. The training advisor was very supportive, and the trainer explained the concepts clearly and effectively. The program was well-structured and has definitely enhanced my skills in AI. Thank you for a wonderful learning experience.
The class was really good. The instructor gave us confidence and delivered the content in an impactful and easy-to-understand manner.
The program helped me understand several areas I was unfamiliar with. The instructor was exceptionally skilled and confident in delivering content.
The program was well-structured and easy to follow. The instructor’s use of real-life AI examples made it easier to connect with and understand the concepts.
Frequently Asked Questions
What is AI Penetration Testing Training, and why is it essential?
It is a specialized cybersecurity course that focuses on testing and securing AI/ML models. With the rapid rise in AI adoption, attackers are exploiting AI weaknesses, making this training essential for future-ready professionals.
Who should join the AI Penetration Testing Training Course?
Pen Testers, Red Teamers, AI/ML Engineers, Data Scientists, and Cybersecurity Professionals aiming to upskill in AI-driven defense.
What skills are covered in AI Penetration Testing Training?
Adversarial ML, data poisoning, model extraction, prompt injection, membership inference, and AI deployment security.
How does AI penetration testing differ from traditional testing methods?
Traditional penetration testing focuses on networks and apps, while AI penetration testing targets machine learning models, LLMs, and AI-driven APIs.
What are the career benefits of completing an AI Penetration Testing Course?
Completing this course opens doors to roles such as AI Security Engineer, AI Red Team Operator, and LLM Security Analyst, with salaries ranging from $120K to $140K/year.
Is the AI Penetration Testing Training available online?
Yes, this program is delivered online with hands-on labs and live instructor-led sessions.
What certifications complement AI Penetration Testing Training?
OSCP, OSEP, CEH, CRTO, and AI Security–focused certifications complement this training.
How long does it take to complete the AI Penetration Testing Course?
The course duration is 24 hours, delivered over flexible instructor-led sessions.
What job roles can I pursue after AI Penetration Testing Training?
AI Security Engineer, Red Team Operator (AI focus), Adversarial ML Tester, AI Security Analyst.
Why choose InfosecTrain for the AI Penetration Testing Training Course?
InfosecTrain provides expert-led training, hands-on labs, real-world AI attack simulations, post-training mentorship, and career support to help you succeed in this emerging field.
