EC-Council
Certified Offensive AI Security Professional (COASP)
Attack AI systems to make them safer.
5 daysIntermediate10 modules
About this course
Attack AI systems to make them safer. Over five days you will map AI attack surfaces, scan and fuzz for AI-specific vulnerabilities, exploit prompt injection and LLM applications, apply adversarial machine learning, then harden systems and respond to AI incidents.
Curriculum
10 modules
- AI & ML Fundamentals
- AI Attack Surface and Threat Landscape (ATLAS-Aligned)
- AI Attack Taxonomy and Classification
- OWASP LLM and ML Top 10 (2025) - Overview & Mapping
- AI System Hacking Methodology
- Securing AI Systems - Foundations (Defensive Anchor)
- AI Security Governance and Compliance
- OSINT for AI Assets
- Tools and Techniques for AI OSINT
- Data & Training Pipeline Intel Gathering
- Mapping AI Attack Surfaces from OSINT
- Discovering AI Endpoints & Services
- AI API & Parameter Enumeration
- Model & Vector Store Enumeration
- Defensive - Reducing AI OSINT Exposure
- Defensive - Hardening Enumerated Surfaces
- AI Threat Intelligence & Continuous Monitoring
- Fundamentals of AI Vulnerability Assessment
- Tools and Techniques for Vulnerability Scanning
- Fuzzing Techniques for AI Systems
- Defensive - Integrating Scanning & Fuzzing
- LLM Architecture & Trust Boundaries
- Prompt Injection & Jailbreaking
- Sensitive Information Disclosure and System Prompt Leakage
- Improper Output Handling and Misinformation
- Advanced Prompt Attack Techniques
- Defensive - Secure LLM Application Design
- Adversarial ML Attacks
- Practical Adversarial Input Attacks
- Privacy & Model Extraction Attacks
- Evaluating Robustness & Trustworthiness
- Emerging Model Attack Techniques
- Defensive - Privacy & Robustness Mitigations
- Understanding AI Data & Training Pipelines
- Data Poisoning Attacks
- Backdoor / Trojan Attacks in Training Pipelines
- AI Supply Chain Attack Vectors
- Defensive - Securing Data & Training Pipelines
- Agentic AI Architecture & Attack Surface
- Excessive Agency & Autonomy
- Model-to-Model and Cross-LLM Attacks
- Unbounded Consumption and Denial of Wallet
- AI Workflow and Orchestration Attacks
- Defensive - Securing Agentic Applications
- AI Infrastructure & Integration Landscape
- System and Framework Exploits
- Tool and API Abuse in AI Apps
- Supply Chain Threats (Deep Dive)
- Defensive - Hardening AI Infra & Supply Chain
- AI Security Test & Evaluation Fundamentals
- Designing AI Security Test Plans
- Executing AI Security Tests
- Reporting, Assurance & Risk Management
- Defensive - Embedding T&E into MLOps/DevSecOps
- Detecting & Responding to AI-Specific Incidents
- Logging, Telemetry & Evidence Collection
- AI Forensics & Post-Incident Analysis
- Capstone: Full-Scope AI Red Team Engagement
- Course Wrap-Up & Professional Practice
Who it's for
Penetration testers, red teamers and AI security specialists.
Before you start
- Strong technical background recommended, Requires: 2–3 years of cybersecurity experience, with understanding of security operations, networks or application security and offensive security experience
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