Certified Offensive AI Security Professional (C|OASP)

Master Offensive AI Security & Red Teaming

Hack and secure LLMs, AI agents, and pipelines
Perform prompt injection, jailbreaking, and adversarial attacks
Identify vulnerabilities in AI models and workflows
Apply OWASP LLM Top 10 & MITRE ATLAS frameworks
Build real-world AI defense and detection strategies

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Certified Offensive AI Security Professional (C|OASP) is a hands-on, practitioner-level certification designed for professionals who want to specialize in offensive AI security. It focuses on identifying, exploiting, and defending vulnerabilities in AI systems, including LLMs, agents, data pipelines, and integrations. Unlike traditional pentesting, this program introduces a structured methodology for AI red teaming—equipping learners to think like attackers, validate defenses, and secure AI systems against real-world threats.

What This Certification Covers

This course covers the complete lifecycle of offensive AI security—from reconnaissance and attack surface mapping to exploitation and defense. Learners gain expertise in prompt injection, adversarial machine learning, AI agent attacks, and supply chain vulnerabilities. It also emphasizes frameworks like OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF to ensure industry-aligned security practices.

Key Skills You Will Gain

Prompt injection and jailbreaking techniques
AI red teaming and adversarial attack simulation
AI vulnerability scanning and fuzzing
Data poisoning and model extraction attacks
AI agent and workflow exploitation
AI supply chain risk assessment

Certification Name	C\|OASP (MCQ + Practical)
Exam Code	312-52
Exam Format	MCQ
Duration	6 Hours
Questions	70
Passing Score	70% – 80%

Security professionals, pentesters, red teamers, AI engineers, and SOC analysts with cybersecurity experience can enroll.

Yes, you will receive the official C|OASP certification after passing the exam.

Yes, every module includes practical labs such as prompt injection, adversarial attacks, and red/blue team scenarios.

Yes, available in instructor-led, live online, and self-paced formats.

You will work with AI security tools, red teaming frameworks, and adversarial testing techniques.

No, it requires prior cybersecurity experience (recommended 3+ years).

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Master The Syllabus

Module 01: Offensive AI and AI System Hacking Methodology

Offensive AI Fundamentals
AI Threat Landscape & Attack Taxonomies
MITRE ATLAS & OWASP LLM Top 10 Mapping
AI System Hacking Methodologies
AI Attack Surface Identification
Offensive AI Scoping

Module 02: AI Reconnaissance and Attack Surface Mapping

AI OSINT Techniques
AI Asset Discovery & Profiling
AI Attack Surface Mapping
Data Pipeline Intelligence Gathering
API & Endpoint Enumeration
Vector Stores & Model Analysis
Exposure Analysis & Hardening

Module 03: AI Vulnerability Scanning and Fuzzing

Module 04: Prompt Injection and LLM Application Attacks

LLM Architecture & Trust Boundaries
Prompt Injection & Jailbreaking
Output Manipulation Attacks
Sensitive Data Exposure Risks
System Prompt Leakage
Secure LLM Design & Defenses

Module 05: Adversarial Machine Learning and Model Privacy Attacks

Adversarial ML Attack Types
Input Manipulation Attacks
Model Extraction Techniques
Privacy Attacks & Inference Risks
Model Robustness Evaluation
Defensive Strategies for ML Models

Module 06: Data and Training Pipeline Attacks

Module 07: Agentic AI and Model-to-Model Attacks

Agentic AI Architecture
Cross-Model Attack Vectors
Excessive Agency Exploitation
Orchestration & Workflow Attacks
Resource Abuse (Denial-of-Wallet)
Securing AI Agents

Module 08: AI Infrastructure and Supply Chain Attacks

Module 09: AI Security Testing, Evaluation, and Hardening

Module 10: AI Incident Response and Forensics

Tools Covered

Prompt Injection Tools

Exploitation

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Adversarial ML Tools

Attacks

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AI Red Team Tools

Testing

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LLM Security Tools

Defense

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AI Monitoring Tools

Detection

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VirtualBox

Virtualization

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Training Certification and Exam Details

Ability to perform AI red teaming and adversarial testing
Strong understanding of AI threat models and vulnerabilities
Hands-on experience with LLM and AI attack techniques
Hands-on experience with LLM and AI attack techniques
Knowledge of AI governance and compliance frameworks

Master In Courses.