AI Security

Master Adversarial Machine Learning, LLM Protection, AI Red Teaming, and Secure AI System Design

What you'll learn

Skills you'll gain

AI Threat IdentificationAdversarial Machine Learning DefenseData Poisoning DetectionModel Extraction PreventionLLM Security & Prompt HardeningAI API SecurityThreat Modeling for AI SystemsAI Risk AssessmentAI Governance & Compliance MappingAI Red Teaming & Security TestingSecure AI Architecture DesignEnd-to-End AI Lifecycle Security

Offensive AI and AI System Hacking Methodology

Understand AI and Machine Learning Fundamentals from an Offensive Security Perspective

Identify AI Attack Surfaces, Threat Landscapes, and Adversary Techniques Aligned to MITRE

Apply AI System Hacking Methodologies, Frameworks, and Risk Implications

Classify AI Attack Taxonomies and Models

Define Offensive AI Scoping Fundamentals and Foundations for Securing AI Systems

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AI Reconnaissance and Attack Surface Mapping

Apply OSINT Tools and Techniques to Identify and Profile AI Assets

Gather Intelligence from AI Data Sources and Training Pipelines

Discover and Map AI Attack Surfaces Using Publicly Available Intelligence

Enumerate AI Endpoints, Services, APIs, and Exposed Parameters

Identify and Analyze AI Models and Vector Stores from an Attacker’s Perspective

Evaluate OSINT Exposure and Apply Hardening Controls to Reduce Risk

Use AI Threat Intelligence to Support Continuous Monitoring and Defensive Readiness

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AI Vulnerability Scanning and Fuzzing

Understand Core Principles of AI Vulnerability Assessment and Threat Discovery

Use Tools and Techniques for Scanning Vulnerabilities in AI Models, Pipelines, and Deployments

Apply Practical Fuzzing Methods Tailored for AI Systems and Model Interfaces

Integrate Scanning and Fuzzing into AI Security Workflows for Proactive Risk Mitigation

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Prompt Injection and LLM Application Attacks

LLM Architecture, Trust Boundaries, and Associated Attack Vectors

Execute Prompt Injection and Jailbreaking Techniques in Real-World LLM Applications

Identify Sensitive Information Disclosure and System Prompt Leakage Risks

Evaluate Improper Output Handling Vulnerabilities and Misinformation Threats

Apply Advanced Prompt-Based Attack Techniques and Exploitation Strategies

Implement Secure LLM Application Design Principles and Defensive Controls

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Adversarial Machine Learning and Model Privacy Attacks

Identify Core Adversarial Machine Learning Attack Classes

Execute Practical Adversarial Input Attacks Across Data Modalities

Apply Privacy, Inference, and Model Extraction Attack Techniques

Evaluate Robustness, Trustworthiness, and Risk Evaluation Methods

Implement Defensive Strategies for Model Privacy and Resilience

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Data and Training Pipeline Attacks

Understand AI Data and Training Pipeline Architecture and Threat Surfaces

Execute Practical Data Poisoning Techniques and Attack Scenarios

Apply Backdoor and Trojan Insertion During Model Training

Implement Security Measures to Safeguard Data and Training Pipelines

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Agentic AI and Model-to-Model Attacks

Understand Agentic AI Architecture and Attack Surface

Apply Excessive Agency and Autonomy Exploitation Techniques

Identify Cross-LLM and Model-to-Model Attack Vectors

Assess Denial-of-Wallet Risks and Unbounded Resource Consumption

Execute Attacks Targeting AI Workflows and Orchestration Layers

Implement Defensive Strategies for Securing Agentic AI Applications

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AI Infrastructure and Supply Chain Attacks

Understand AI Infrastructure Components and System Integration Architectures

Identify Vulnerabilities in AI Systems, Frameworks, and Deployment Pipelines

Analyze Abuse of Tools, Plugins, and APIs in AI-Enabled Applications

Assess AI Supply Chain Threats and Dependency Risks (Deep Dive)

Implement Hardening Strategies for AI Infrastructure and Supply Chains

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AI Security Testing, Evaluation, and Hardening

Understand AI Security Testing Methodologies and Evaluation Techniques

Apply Red Team Frameworks for Offensive AI Assessment

Identify, Validate, and Report AI Vulnerabilities and Risk

Implement Security Hardening and Mitigation Best Practices for AI Systems

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AI Incident Response and Forensics

Detect and Respond to AI-Specific Security Incidents

Collect and Analyze AI Logs, Telemetry, and Digital Evidence

Analyze Root Causes in Post-Incident Investigations

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Capstone Project

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