BUILD TOMORROW.
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SECURE TOMORROW.
Certified Offensive AI Security Professional (C|OASP)
EC-Council / C|OASPBuild the skills for your next mission.
The Certified Offensive AI Security Professional (C|OASP) certification, offered by EC-Council, is an advanced-level program meticulously designed to equip cybersecurity professionals with the specialized knowledge and practical skills required to identify, exploit, and mitigate vulnerabilities within Artificial Intelligence (AI) and Machine Learning (ML) systems.
Course objectives
Build practical skills across the key domains covered in Certified Offensive AI Security Professional (C|OASP) (C|OASP).
Fundamentals of AI and Machine Learning for Offensive SecurityExplain core AI/ML concepts, including supervised, unsupervised, and reinforcement learning.
- Explain core AI/ML concepts, including supervised, unsupervised, and reinforcement learning.
- Identify common AI/ML model types (e.g., neural networks, decision trees, SVMs) and their applications.
- Describe the typical AI/ML lifecycle, from data collection to deployment and monitoring.
- Analyze the components of an AI/ML pipeline and identify potential attack surfaces.
- Differentiate between various AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
- Understand the ethical implications and societal impact of AI, particularly in security contexts.
AI/ML Model Evasion and Adversarial AttacksExplain the concept of adversarial examples and their generation methods (e.g., FGSM, PGD, C&W).
- Explain the concept of adversarial examples and their generation methods (e.g., FGSM, PGD, C&W).
- Perform adversarial attacks against image classification models.
- Execute adversarial attacks against natural language processing (NLP) models.
- Identify and exploit vulnerabilities in machine learning models that lead to evasion.
- Assess the transferability of adversarial examples across different models.
- Analyze the impact of data poisoning and data manipulation on model integrity.
AI/ML Model Inference and Data Extraction AttacksDescribe model inversion attacks and their potential for data reconstruction.
- Describe model inversion attacks and their potential for data reconstruction.
- Perform membership inference attacks to determine if specific data points were used in training.
- Explain the principles of model stealing and intellectual property theft.
- Execute model extraction attacks to replicate or approximate target models.
- Identify side-channel attacks applicable to AI/ML systems.
AI/ML System Vulnerabilities and ExploitationIdentify common vulnerabilities in AI/ML data pipelines and infrastructure.
- Identify common vulnerabilities in AI/ML data pipelines and infrastructure.
- Exploit misconfigurations in AI/ML development and deployment environments.
- Analyze the security of AI/ML APIs and interfaces.
- Perform penetration testing on AI-powered applications and services.
- Understand the impact of supply chain attacks on AI/ML components and datasets.
- Evaluate the security of federated learning and distributed AI systems.
Defensive AI/ML Security and CountermeasuresImplement adversarial training and other robust defense mechanisms against adversarial examples.
- Implement adversarial training and other robust defense mechanisms against adversarial examples.
- Apply data sanitization and anomaly detection techniques to mitigate data poisoning.
- Utilize differential privacy and homomorphic encryption for data and model protection.
- Design secure AI/ML development and deployment workflows.
- Evaluate and recommend security best practices for AI/ML systems.
- Develop incident response plans for AI/ML security breaches.
Find your DoD 8140 pathway
Explore how this training may align to DoD 8140 work roles and proficiency levels.
About this course
The Certified Offensive AI Security Professional (C|OASP) certification, offered by EC-Council, is an advanced-level program meticulously designed to equip cybersecurity professionals with the specialized knowledge and practical skills required to identify, exploit, and mitigate vulnerabilities within Artificial Intelligence (AI) and Machine Learning (ML) systems. This comprehensive course delves into the intricate attack surfaces of AI/ML models, data pipelines, and infrastructure, providing a deep understanding of adversarial AI techniques, data poisoning, model evasion, and the ethical implications of offensive AI operations. Participants will gain hands-on experience with cutting-edge tools and methodologies to conduct robust offensive security assessments against AI-driven applications, ensuring the integrity and resilience of critical AI deployments.
Training alone does not establish DoD 8140 qualification. Confirm position-specific requirements with your organization.