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312-52 Exam Topics and Domains
312-52 is organized into 10 weighted domains. Expect to work with Hugging Face, LangChain, OWASP, PyTorch, and more.
Offensive AI and AI System Hacking Methodology
AI and Machine Learning Fundamentals from an Offensive Security Perspective
Understand AI and machine learning fundamentals from an offensive security perspective
AI Attack Surfaces, Threat Landscapes, and Adversary Techniques
Identify AI attack surfaces, threat landscapes, and adversary techniques aligned to MITRE ATLAS
AI System Hacking Methodologies, Frameworks, and Risk Implications
Apply AI system hacking methodologies, frameworks, and risk implications
AI Attack Taxonomies and Classification
Classify AI attack taxonomies and models
OWASP LLM and ML Top 10 Mapping
Provide an overview and mapping of OWASP LLM & ML Top 10 (2025) to AI threats and governance considerations
AI Reconnaissance and Attack Surface Mapping
AI-Focused OSINT Tools and Techniques
Apply OSINT tools and techniques to identify and profile AI assets
Intelligence Gathering from AI Data Sources and Training Pipelines
Gather intelligence from AI data sources and training pipelines
AI Attack Surface Discovery and Mapping
Discover and map AI attack surfaces using publicly available intelligence
AI Endpoint and Service Enumeration
Enumerate AI endpoints, services, APIs, and exposed parameters
AI Model and Vector Store Analysis
Identify and analyze AI models and vector stores from an attacker's perspective
OSINT Exposure Evaluation and Hardening
Evaluate OSINT exposure and apply hardening controls to reduce risk
AI Threat Intelligence for Continuous Monitoring
Use AI threat intelligence to support continuous monitoring and defensive readiness
AI Vulnerability Scanning and Fuzzing
AI Vulnerability Assessment Principles
Understand core principles of AI vulnerability assessment and threat discovery
AI Model and Pipeline Vulnerability Scanning
Use tools and techniques for scanning vulnerabilities in AI models, pipelines, and deployments
AI Fuzzing Techniques
Apply practical fuzzing methods tailored for AI systems and model interfaces
Integrating Scanning and Fuzzing into AI Security Workflows
Integrate scanning and fuzzing into AI security workflows for proactive risk mitigation
Prompt Injection and LLM Application Attacks
LLM Architecture, Trust Boundaries, and Attack Vectors
Understand LLM architecture, trust boundaries, and associated attack vectors
Prompt Injection and Jailbreaking Techniques
Execute prompt injection and jailbreaking techniques in real-world LLM applications
Sensitive Information Disclosure and System Prompt Leakage
Identify sensitive information disclosure and system prompt leakage risks
Improper Output Handling and Misinformation
Evaluate improper output handling vulnerabilities and misinformation threats
Advanced Prompt-Based Attack Techniques
Apply advanced prompt-based attack techniques and exploitation strategies
Secure LLM Application Design and Defensive Controls
Implement secure LLM application design principles and defensive controls
Adversarial Machine Learning and Model Privacy Attacks
Core Adversarial Machine Learning Attack Classes
Identify core adversarial machine learning attack classes
Practical Adversarial Input Attacks
Execute practical adversarial input attacks across data modalities
Privacy, Inference, and Model Extraction Attacks
Apply privacy, inference, and model extraction attack techniques
Robustness, Trustworthiness, and Risk Evaluation
Evaluate robustness, trustworthiness, and risk evaluation methods
Defensive Strategies for Model Privacy and Resilience
Implement defensive strategies for model privacy and resilience
Data and Training Pipeline Attacks
AI Data and Training Pipeline Architecture and Threat Surfaces
Understand AI data and training pipeline architecture and threat surfaces
Data Poisoning Techniques and Attack Scenarios
Execute practical data poisoning techniques and attack scenarios
Backdoor and Trojan Insertion During Model Training
Apply backdoor and trojan insertion during model training
Safeguarding Data and Training Pipelines
Implement security measures to safeguard data and training pipelines
Agentic AI and Model-to-Model Attacks
Agentic AI Architecture and Attack Surface
Understand agentic AI architecture and attack surface
Excessive Agency and Autonomy Exploitation
Apply excessive agency and autonomy exploitation techniques
Cross-LLM and Model-to-Model Attack Vectors
Identify cross-LLM and model-to-model attack vectors
Denial-of-Wallet and Unbounded Resource Consumption
Assess denial-of-wallet risks and unbounded resource consumption
Attacks Targeting AI Workflows and Orchestration Layers
Execute attacks targeting AI workflows and orchestration layers
Defensive Strategies for Agentic AI Applications
Implement defensive strategies for securing agentic AI applications
AI Infrastructure and Supply Chain Attacks
AI Infrastructure Components and Integration Architectures
Understand AI infrastructure components and system integration architectures
AI System and Framework Vulnerabilities
Identify vulnerabilities in AI systems, frameworks, and deployment pipelines
Abuse of Tools, Plugins, and APIs in AI Applications
Analyze abuse of tools, plugins, and APIs in AI-enabled applications
AI Supply Chain Threats and Dependency Risks
Assess AI supply chain threats and dependency risks (deep dive)
Hardening AI Infrastructure and Supply Chains
Implement hardening strategies for AI infrastructure and supply chains
AI Security Testing, Evaluation, and Hardening
AI Security Testing Methodologies and Evaluation Techniques
Understand AI security testing methodologies and evaluation techniques
Red Team Frameworks for Offensive AI Assessment
Apply red team frameworks for offensive AI assessment
AI Vulnerability Identification, Validation, and Reporting
Identify, validate, and report AI vulnerabilities and risk
Security Hardening and Mitigation Best Practices
Implement security hardening and mitigation best practices for AI systems
AI Incident Response and Forensics
AI-Specific Security Incident Detection and Response
Detect and respond to AI-specific security incidents
AI Log Collection, Telemetry, and Digital Evidence Analysis
Collect and analyze AI logs, telemetry, and digital evidence
Root Cause Analysis in AI Incidents
Analyze root causes in post-incident analysis
How do I earn this certification?
Passing 312-52 earns the Certified Offensive AI Security Professional (C|OASP) certification. It sits in the AI Security - Defend Pillar (ADG Framework) track.
- Practical - Licensed Penetration Tester (LPT) Master Expert-level penetration testing credential for advanced offensive professionals
- 712-50 - Certified Chief Information Security Officer (CCISO) v4Executive security leadership with AI-driven risk management (updated v4 includes AI governance)
- 312-40 - Certified Network Defender (CND)Defensive counterpart for network security specialization
- 312-49 - Certified Penetration Testing Professional (CPENT) Traditional penetration testing credential complementing AI-specific offensive skills
- 312-85 - Certified Threat Intelligence Analyst (CTIA)AI threat intelligence and adversary analysis skills
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for 312-52 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
Who should take this exam?
- Experience with penetration testing or ethical hacking
- Familiarity with AI/ML concepts and frameworks
- Understanding of OWASP Top 10 and MITRE ATT&CK
- Knowledge of LLM architectures and prompt engineering
- Experience with Python and security tooling