Certified Offensive AI Security Professional (C|OASP) Training

Who Should Attend This Course?

C|OASP is built for experienced security professionals and AI engineers who want to add offensive AI skills to their toolkit. The program targets over 20 job roles across six domains:

Offensive security

•    Penetration testers and ethical hackers
•    Red team operators and red team leads
•    Offensive security engineers
•    Adversary emulation and purple team specialists

Defensive security

•    SOC analysts (Tier 2 and Tier 3) and detection engineers
•    Blue team engineers and threat detection engineers
•    Incident responders and DFIR analysts
•    Security operations managers and SOC leads

Threat intelligence

•    Malware analysts and threat researchers
•    Cyber threat intelligence analysts with an AI focus
•    Fraud and abuse detection analysts dealing with AI-enabled threats

AI and ML engineering

•    ML engineers and applied AI engineers
•    GenAI engineers working with RAG and agents
•    AI and LLM application developers
•    MLOps and AI platform engineers

Security engineering

•    DevSecOps and secure DevOps specialists
•    Application security engineers working with LLM apps and APIs
•    Product security engineers with an AI security focus

AI security architecture

•    Secure AI engineers and AI security architects
•    LLM systems engineers

What You Will Learn

The C|OASP program covers offensive AI security from start to finish. Here is a breakdown of the skills and knowledge areas you will walk away with:

AI security foundations

•    How AI systems work from an offensive security perspective
•    AI attack surfaces, threat landscapes, and adversary techniques aligned with MITRE ATLAS
•    AI hacking methodologies, risk frameworks, and attack taxonomies
•    OWASP LLM and ML Top 10 (2025) mapped to real threat scenarios

Reconnaissance and attack surface mapping

•    OSINT tools and techniques to find and profile AI assets
•    How to gather intelligence from training pipelines and data sources
•    Enumerating AI endpoints, APIs, services, and exposed parameters
•    Identifying AI models and vector stores from an attacker's point of view
•    Reducing exposure through hardening and continuous monitoring

Vulnerability scanning and fuzzing

•    AI-specific vulnerability assessment and threat discovery methods
•    Tools for scanning vulnerabilities in AI models, pipelines, and deployments
•    Fuzzing techniques tailored for AI systems and model interfaces
•    Integrating scanning and fuzzing into your AI security workflow

Prompt injection and LLM application attacks

•    Prompt injection, jailbreaking, and prompt chaining techniques on real LLM applications
•    Extracting sensitive information and leaking system prompts
•    Exploiting improper output handling and misinformation vulnerabilities
•    Designing LLM applications with security in mind

Adversarial machine learning and privacy attacks

•    Core adversarial ML attack classes across different data types
•    Privacy, inference, and model extraction attack techniques
•    Evaluating AI system robustness, trustworthiness, and risk
•    Defensive strategies for model privacy and resilience

Data and training pipeline attacks

•    How AI data and training pipeline architectures create threat surfaces
•    Practical data poisoning techniques and attack scenarios
•    Inserting backdoors and trojans during model training
•    Safeguarding data and training pipelines against compromise

Agentic AI and model-to-model attacks

•    Agentic AI architecture and where it is vulnerable
•    Exploiting excessive agency and autonomy in AI agents
•    Cross-LLM and model-to-model attack vectors
•    Denial-of-wallet risks and unbounded resource consumption
•    Attacking AI workflows and orchestration layers
•    Securing agentic AI applications

AI infrastructure and supply chain attacks

•    AI infrastructure components and system integration architectures
•    Vulnerabilities in AI frameworks and deployment pipelines
•    Abusing tools, plugins, and APIs in AI-enabled applications
•    AI supply chain threats, dependency risks, and hardening strategies

AI security testing, evaluation, and hardening

•    Structured AI security testing and evaluation methodologies
•    Red team frameworks for offensive AI assessment
•    AI vulnerability identification, validation, and risk reporting
•    Hardening and mitigation best practices for enterprise AI systems

AI incident response and forensics

•    Detecting and responding to AI-specific security incidents
•    Collecting and analyzing AI logs, telemetry, and digital evidence
•    Root cause analysis in post-incident investigations

Prerequisites

This is not a beginner-level course. You need 3 Years of Cybersecurity Experience. You need a working knowledge of cybersecurity fundamentals before enrolling. The program assumes you already understand:

•    Core cybersecurity concepts (networking, operating systems, common attack types)
•    Basic penetration testing or ethical hacking principles
•    Familiarity with web application security (APIs, authentication, common vulnerabilities)
•    General awareness of how AI and machine learning systems work (you do not need to be an ML engineer, but you should know the basics)

If you hold certifications like C|EH, C|PENT, OSCP, or have hands-on experience with penetration testing or security operations, you are well positioned for this program.

The Offensive AI Security Methodology

C|OASP follows a three-phase methodology that mirrors how real offensive engagements work:

Phase 1: Recon

Map AI system architectures, enumerate exposed endpoints, and build threat models. Profile training pipelines, data flows, and inference APIs to find where defenses are weakest.

Phase 2: Exploit

Execute prompt injection, jailbreaking, data poisoning, and model extraction attacks. Validate weaknesses in AI systems and document every exploitable gap.

Phase 3: Defend

Implement guardrails, detection mechanisms, and incident response procedures. Harden AI systems and make sure deployments can withstand adversarial pressure.

Why Traditional Security Fails Against AI

AI systems bring attack vectors that conventional security tools and methods cannot detect or prevent. Here are the four categories of AI-specific threats this program addresses:

Prompt injection

Attackers manipulate LLM inputs to get around safety guardrails. This can lead to sensitive data extraction or unauthorized actions being executed through the model.

Model extraction

Adversaries steal proprietary AI models by carefully querying them, replicating months or years of training investment with relatively little effort.

Data poisoning

Manipulating training data introduces backdoors that activate only under specific conditions. This compromises the integrity of the model without anyone noticing until it is too late.

Jailbreaking

Carefully crafted prompts can override safety mechanisms, making AI systems produce harmful, inaccurate, or policy-violating outputs.
 

Career Opportunities After C|OASP

The C|OASP certification opens doors to more than 17 career paths in offensive AI security, adversarial research, and AI risk management. Some of the roles this program prepares you for:

Offensive AI security roles

•    AI red team specialist or adversarial AI engineer
•    Offensive security engineer with an AI/LLM focus
•    Adversarial AI security analyst

Research and analysis roles

•    Adversarial machine learning researcher
•    AI threat hunter or AI security analyst
•    AI malware and exploit analyst

Testing and incident response roles

•    AI incident response engineer
•    AI test and evaluation specialist
•    CTI analyst with an AI focus

Engineering and operations roles

•    Secure AI engineer or AI security architect
•    MLOps / AIOps security specialist
•    LLM systems engineer

Risk, assurance, and leadership roles

•    AI model risk specialist
•    AI risk advisor or consultant
•    Security program manager (AI security)
•    AI product security manager

Salary Outlook For AI Security Professionals

Demand for professionals who can test and defend AI systems continues to outpace supply. Here are the 2026 salary benchmarks for roles that C|OASP prepares you for:

Sources: Glassdoor, Payscale, 6figr.com 

Industries That Need AI Security Professionals

AI security skills are in demand across virtually every sector. Here is where C|OASP-certified professionals are most needed:

•    Finance and banking: AI fraud model hardening, LLM chatbot security, regulatory compliance testing
•    Technology: Enterprise LLM security, RAG pipeline hardening, AI DevSecOps integration
•    Defense and aerospace: Military AI systems, supply chain protection, counter-adversarial ML
•    Healthcare: Medical AI red-teaming, HIPAA-compliant security testing, clinical AI validation
•    Government: DoD AI security frameworks, critical infrastructure protection, national security AI assurance

Frameworks Covered

The C|OASP curriculum is built on frameworks and standards recognized across industry and government:

•    MITRE ATLAS Framework
•    OWASP LLM Top 10 (2025)
•    OWASP ML Security Top 10 (2025)
•    OWASP Top 10 for Agentic Applications
•    DoD AI Test and Evaluation Specialist (672) Framework
•    NIST AI Risk Management Framework
 

Tools You Will Work With

The program gives you hands-on experience with more than 20 AI security testing tools. Some of the tools you will use in the labs:

•    Garak (LLM vulnerability scanner)
•    PyRIT (Microsoft's AI red team tool)
•    Burp Suite for AI APIs
•    OWASP ZAP for web-based AI services
•    Atheris (Python fuzzer for AI applications)

You will also practice techniques like multi-protocol reconnaissance, API fingerprinting, RAG poisoning, gradient-based adversarial attacks (FGSM and PGD) on image and audio models, cross-LLM attacks, and model extraction from exposed infrastructure.
 

Why Train With Microtek Learning

Microtek Learning is an authorized EC-Council training partner. When you enroll through us, you get:

•    Expert-led, instructor-driven training sessions with certified professionals
•    Full access to official EC-Council courseware and lab environments
•    Flexible training delivery options (online, classroom, and blended formats)
•    Exam preparation support and guidance from experienced mentors
•    Post-training support to help you apply what you have learned

We have trained thousands of cybersecurity professionals across India and globally. Our track record with EC-Council programs, including C|EH, C|PENT, C|HFI, and C|CISO, means we know how to prepare you for both the exam and real-world application.