Certified Offensive AI Security Professional (COASP)
5 Based on 213 reviews- 141,927 Learners
- 287 Courses
Upcoming Class Schedule
$2,995.00
Sep 28 - Oct 02, 2026
9:00 AM - 5:00 PM Eastern- VILT
Who should attend Certified Offensive AI Security Professional (COASP) Course
- Penetration Testers / Ethical Hackers
- Red Team / Offensive Security Specialists
- Security Engineers / DevSecOps Engineers
- SOC Analysts / Incident Responders
- AI/ML Engineers focused on security
Prerequisites for Certified Offensive AI Security Professional (COASP) Course
Certified Offensive AI Security Professional (COASP) Course Outline
- 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
Resources
FAQs on Certified Offensive AI Security Professional (COASP)
COASP is a specialized training program focused on offensive security testing of AI and ML systems. It covers AI attack methodologies, reconnaissance, vulnerability scanning, fuzzing, and exploitation techniques aligned with frameworks like MITRE ATLAS and OWASP LLM/ML Top 10, while also introducing foundational defensive concepts to secure AI systems.
This course is designed for penetration testers, ethical hackers, red team and offensive security specialists, security engineers, DevSecOps engineers, SOC analysts, incident responders, and AI/ML engineers with a security focus who want to understand and test AI system vulnerabilities.
Participants should have a strong technical background with 2-3 years of cybersecurity experience, including familiarity with security operations, network or application security, and prior offensive security experience.
The course covers AI system hacking methodology, AI reconnaissance and attack surface mapping, OSINT for AI assets, AI-specific vulnerability scanning, fuzzing techniques, model and vector store enumeration, and foundational AI security governance and defensive hardening practices.
The course is aligned with the MITRE ATLAS framework for AI threat landscapes and incorporates the OWASP LLM and ML Top 10 (2025) to provide a structured, industry-recognized approach to identifying and classifying AI-specific security risks.