Technical Training on AI for Red and Blue Team Penetration Testing Teams by Tonex
As artificial intelligence (AI) becomes increasingly integrated into cybersecurity, penetration testing teams must evolve to leverage AI tools and methodologies effectively.
This 1-day intensive training provides security professionals, developers, and cybersecurity teams with a deep dive into AI and LLM security risks, vulnerabilities, and mitigation strategies.
This course also provides best practices and integrates the latest OWASP AI Security Guidelines (2024-2025) and MIOTRE ATLAS Framework to help participants understand, assess, and defend against AI-specific threats.
Participants will also explore how AI enhances penetration testing, vulnerability assessments, and cyber threat detection while also understanding AI-driven attack techniques.
Learning Objectives
By the end of this training, participants will be able to:
- Understand the security challenges of AI and LLMs in enterprise applications.
- Learn about OWASP AI Security Top Risks (2024-2025) and CAISF best practices.
- Identify LLM-specific vulnerabilities, including prompt injection, data poisoning, and model theft.
- Implement secure AI development and deployment strategies to mitigate real-world threats.
- Gain practical skills in AI/LLM security testing
- Learn how to protect AI systems from adversarial threats
- Understand AI’s role in offensive (Red Team) and defensive (Blue Team) cybersecurity.
- Identify AI-powered attack techniques, including AI-driven malware and adversarial machine learning.
- Utilize AI and ML-based tools for vulnerability detection, threat hunting, and penetration testing.
- Implement AI-powered defensive strategies to counter advanced cyber threats.
- Assess ethical and adversarial AI implications in cybersecurity operations.
Target Audience
- Red Team professionals (ethical hackers, penetration testers, offensive security experts)
- Blue Team professionals (SOC analysts, threat hunters, incident responders)
- Cybersecurity professionals looking to integrate AI into their security strategies
Prerequisites: Basic knowledge of cybersecurity and penetration testing tools
Course Modules
Module 1: Introduction to AI and LLM Security Issues
- Threat Modeling for AI & LLMs
- Understanding AI security frameworks (CAISF, OWASP AI Security 2024-2025, MITRE ATLAS, Google SIAF an NIST AI)
- AI risk landscape: Adversarial AI, LLM vulnerabilities, compliance concerns
Module 2: OWASP AI Security Top Risks 2024-2025 (90 min)
- Prompt Injection Attacks: Direct & Indirect Injection
- Training Data Poisoning: Malicious dataset manipulation
- Model Theft & Reverse Engineering: How attackers steal LLMs
- Insecure Model APIs: Exposing sensitive data & backend systems
- AI Supply Chain Risks: Threats in AI model deployment
- Model Hallucination & Misinformation Risks
- Adversarial AI Attacks & Defense Techniques
- Red Teaming AI Systems for Security Assessment
Module 3: AI/LLM Security Testing & Red Teaming
- LLM Penetration Testing Methodology
- AI Fuzzing & Model Robustness Testing
- Data Privacy Concerns & Extraction Attacks
- Secure AI/LLM Deployment & Defense Strategies
- Implementing Guardrails for Secure LLMs
- Defending Against Prompt Injection & Jailbreak Attacks
- Monitoring AI Systems for Abnormal Behavior
- Secure API Integration & Governance for AI
Module 4: Introduction to AI in Cybersecurity
- Understanding AI and Machine Learning in Cybersecurity
- How AI is Used in Red and Blue Teaming
- AI-Driven Attack and Defense Frameworks
- AI for Red Team Operations (Offensive Security)
- Using AI for Automated Reconnaissance and OSINT
- AI-Generated Malware and Evasion Techniques
- Adversarial Machine Learning: Manipulating AI Defenses
- AI in Social Engineering and Phishing Attacks
- AI for Blue Team Operations (Defensive Security)
- AI-Powered Threat Intelligence and Anomaly Detection
- Machine Learning for Malware Analysis and Intrusion Detection
- Defending Against AI-Enhanced Attacks
- AI in Cyber Threat Hunting and Incident Response
Module 5: Ethics, Compliance, and Future Trends
- Ethical Considerations in AI-Driven Cybersecurity
- AI in Compliance and Regulatory Frameworks
- The Future of AI in Cybersecurity: Challenges and Innovations
Workshop 1: Overview of OWASP 2025 and 2024 Top 10 LLM Issues
OWSAP Top 10 LLM 2025
- LLM01:2025 Prompt Injection
- LLM02:2025 Sensitive Information Disclosure
- LLM03:2025 Supply Chain
- LLM04: Data and Model Poisoning
- LLM05:2025 Improper Output Handling
- LLM06:2025 Excessive Agency
- LLM07:2025 System Prompt Leakage
- LLM08:2025 Vector and Embedding Weaknesses
- LLM09:2025 Misinformation
- LLM10:2025 Unbounded Consumption
OWSAP Top 10 LLM 2024
- LLM01: Prompt Injection
- LLM02: Insecure Output Handling
- LLM03: Training Data Poisoning
- LLM04: Model Denial of Service
- LLM05: Supply Chain Vulnerabilities
- LLM06: Sensitive Information Disclosure
- LLM07: Insecure Plugin Design
- LLM08: Excessive Agency
- LLM09: Overreliance
- LLM10: Model Theft
Exam Domains for 2-Day Technical Training on AI for Red and Blue Team Penetration Testing
This certification exam assesses participants’ knowledge and practical skills in AI-driven penetration testing, security risk assessments, and defense strategies. The exam integrates the latest OWASP AI Security Guidelines (2024-2025) and MITRE ATLAS Framework to ensure competency in AI security risks, vulnerabilities, and countermeasures.
Domain 1: AI & LLM Security Risks and Threat Modeling (20%)
- Understanding AI and Large Language Model (LLM) security risks
- AI threat modeling: OWASP AI Security, MITRE ATLAS, CAISF, NIST AI
- AI risk landscape: Adversarial AI, LLM vulnerabilities, compliance concerns
- AI supply chain risks and attack vectors
- Overview of the latest OWASP AI Security Top Risks (2024-2025)
Domain 2: AI Red Teaming & Offensive Security (25%)
- AI-powered penetration testing methodologies
- AI-driven reconnaissance, OSINT, and attack automation
- AI-generated malware, adversarial machine learning, and evasion techniques
- AI-assisted phishing, social engineering, and model manipulation attacks
- AI fuzzing and model robustness testing
- Prompt injection attacks, LLM data poisoning, and model theft
- Red teaming AI systems for security assessment
Domain 3: AI Blue Teaming & Defensive Security (25%)
- AI-driven threat detection, analysis, and defense techniques
- Machine learning for anomaly detection, malware analysis, and SOC automation
- Defending against AI-powered attack techniques (adversarial AI, prompt injection, AI-based malware)
- Secure AI deployment: Implementing guardrails, monitoring abnormal AI behaviors
- AI-powered cyber threat hunting and incident response
- Secure API integration & governance for AI systems
Domain 4: OWASP AI Security Top Risks & Mitigation Strategies (20%)
- OWASP AI Security Top Risks (2024-2025) and best practices
- LLM-specific vulnerabilities (e.g., prompt injection, system prompt leakage, excessive agency)
- Model denial of service, model theft, and insecure AI plugin design
- Secure AI/LLM development and defense strategies
- Implementing AI security best practices in enterprise environments
Domain 5: Ethical AI, Compliance, and Future Trends (10%)
- Ethical considerations in AI-driven cybersecurity
- AI in compliance and regulatory frameworks (NIST AI RMF, EU AI Act, ISO/IEC 42001)
- Challenges in securing AI for enterprise cybersecurity operations
- Future trends in AI security and evolving adversarial techniques
Exam Details:
- Number of Questions: 50 (Multiple Choice, Scenario-Based, Hands-on)
- Exam Duration: 90 minutes
- Passing Score: 70%