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EC-Council Certified Offensive AI Security Professional (C|OASP)

Attacks are becoming more sophisticated—and increasingly rely on AI. What is needed is a deep understanding of how offensive techniques are combined with artificial intelligence to test systems under realistic conditions and identify vulnerabilities early on.

Key Topics

  • Adversarial AI and attack models
  • Prompt injection and model exploits
  • Automated vulnerability analysis
  • Red teaming with AI support
  • Securing ML and LLM Systems

Prerequisites
Fundamentals of IT security and networking, and some familiarity with machine learning

Target Audience
Security professionals, penetration testers, red teams, IT architects

Relevant expertise for a security landscape where traditional methods are no longer sufficient and intelligent attack simulation is becoming the norm.

Course Content
  • Explore the core principles of AI and ML from an offensive security standpoint.
  • Map AI attack surfaces and threat scenarios in alignment with MITRE ATLAS.
  • Deploy attack methods and frameworks against AI systems in practice.
  • Apply classification models and taxonomies to categorize AI attacks.
  • Establish core concepts of offensive AI and corresponding defense strategies.
  • Map the OWASP LLM & ML Top 10 (2025) to AI threats and governance requirements.
  • Apply OSINT techniques to identify and profile AI assets.
  • Collect data from AI sources and training pipelines systematically.
  • Use public information to uncover and map AI attack surfaces.
  • Catalog exposed AI endpoints, services, and APIs.
  • Assess AI models and vector stores from an attacker's viewpoint.
  • Evaluate OSINT exposure and derive appropriate countermeasures.
  • Use AI threat intelligence to maintain continuous monitoring and defensive posture.
  • Grasp the principles of vulnerability analysis and threat detection in AI environments.
  • Apply scanning tools to detect vulnerabilities in AI models, pipelines, and deployments.
  • Use AI-specific fuzzing techniques on models and interfaces.
  • Embed scanning and fuzzing into AI security workflows to reduce risks proactively.
  • Understand LLM architecture, trust boundaries, and related attack vectors.
  • Execute prompt injection and jailbreaking in real-world LLM environments.
  • Recognize risks from data exposure and system prompt leakage.
  • Analyze vulnerabilities from flawed output handling and misinformation risks.
  • Master advanced prompt-based attack techniques and exploitation strategies.
  • Apply secure LLM design principles and implement defensive controls.
  • Recognize and classify the main categories of adversarial ML attacks.
  • Execute adversarial input attacks across different data types.
  • Use privacy, inference, and model extraction attack techniques.
  • Assess model robustness, trustworthiness, and risk levels.
  • Develop and apply protection strategies for data privacy and model resilience.
  • Analyze the structure and vulnerabilities of AI data and training pipelines.
  • Execute data poisoning and attack scenarios in practice.
  • Inject backdoors and trojans deliberately into model training processes.
  • Set up and apply protective measures for data and training pipelines.
  • Analyze the architecture and attack surface of agentic AI systems.
  • Exploit excessive agency and autonomy in AI systems.
  • Detect and analyze multi-LLM and model-to-model attack vectors.
  • Evaluate denial-of-wallet risks and uncontrolled resource consumption.
  • Carry out attacks against AI workflows and orchestration layers.
  • Develop and deploy defenses for agentic AI applications.
  • Analyze the components and integration architecture of AI infrastructure.
  • Uncover security gaps in AI systems, frameworks, and deployment pipelines.
  • Recognize misuse patterns in tools, plugins, and APIs within AI applications.
  • Conduct an in-depth assessment of AI supply chain threats and dependency risks.
  • Develop and apply strategies to protect AI infrastructure and supply chains.
  • Learn methodologies for security testing and evaluation in AI contexts.
  • Use red team frameworks for offensive AI assessments.
  • Detect, validate, and document AI-related vulnerabilities and risks.
  • Apply best practices for AI hardening and risk mitigation.
  • Identify AI-specific security incidents early and respond effectively.
  • Secure and analyze logs, telemetry, and digital evidence from AI systems.
  • Systematically investigate root causes in post-incident reviews.

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.

Frequently asked questions

  • C|OASP builds applicable knowledge for attacking and securing AI systems – skills that organizations urgently need today.
  • AI models introduce novel attack surfaces. Those who understand them early can detect critical vulnerabilities before attackers exploit them.
  • Compromised models, data loss, and overlooked vulnerabilities can cause significant financial and reputational harm.
  • C|OASP focuses on active attack techniques against AI systems rather than theory – a clear advantage over conventional certifications.
  • The course covers prompt injection, model exploitation, adversarial attacks, and AI system hardening – practical skills ready for immediate professional use.
  • The course content is built around real-world threat scenarios and can be applied immediately in projects and within the organization.
  • The AI security market is growing fast. Building expertise now provides a clear edge in career opportunities and compensation.

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