The AI Red Teaming and Security Testing Training Course by Oxford Training Centre, under the Artificial Intelligence (AI) category, provides practical knowledge and techniques for identifying, testing, and mitigating security risks in artificial intelligence systems. The course focuses on AI red teaming, jailbreak testing, vulnerability assessment, safety evaluation, adversarial testing, prompt injection, model misuse, and AI security controls. Participants will learn how to simulate realistic attacks against AI models, identify weaknesses in model behavior, evaluate safety safeguards, and develop effective strategies for improving the security and reliability of AI systems.
Objectives
- Understand the principles, methodologies, and objectives of AI red teaming.
- Learn how to identify security vulnerabilities in AI models and applications.
- Perform structured jailbreak testing against generative AI systems.
- Conduct AI-focused vulnerability assessment and risk analysis.
- Apply systematic safety evaluation techniques to AI models.
- Identify prompt injection, data leakage, model manipulation, and misuse risks.
- Develop adversarial test cases and realistic AI attack scenarios.
- Evaluate model guardrails, safety controls, and security mechanisms.
- Analyse and document AI security testing results.
- Recommend mitigation strategies to strengthen AI system security.
Target Audience
- AI and Machine Learning Professionals
- AI Security Specialists
- Cybersecurity Professionals
- Security Engineers and Analysts
- Machine Learning Engineers
- AI Developers and Researchers
- Red Team and Penetration Testing Professionals
- Risk and Compliance Professionals
- AI Governance and Safety Teams
- IT Security Managers and Technical Leaders
Modules
Module 1: Introduction to AI Red Teaming
- Fundamentals of AI red teaming
- AI security and threat landscapes
- Red teaming vs. traditional security testing
- AI attack surfaces and risk categories
- Principles of responsible AI security testing
Module 2: AI Threat Modeling and Attack Surfaces
- AI system threat modeling
- Identifying model and application attack surfaces
- Threat actors and attack objectives
- AI-specific security risks
- Risk prioritization and assessment
Module 3: Jailbreak Testing and Prompt Attacks
- Fundamentals of jailbreak testing
- Understanding adversarial prompts
- Prompt injection techniques and risks
- Testing model safety boundaries
- Multi-turn and indirect prompt attacks
- Evaluating jailbreak resistance
Module 4: AI Vulnerability Assessment
- AI-focused vulnerability assessment
- Identifying model weaknesses
- Data leakage and information exposure
- Model manipulation risks
- Insecure AI integrations
- Assessing vulnerabilities in AI applications
Module 5: AI Safety Evaluation
- Principles of AI safety evaluation
- Testing harmful and unsafe model behavior
- Evaluating guardrails and safety policies
- Bias, reliability, and robustness testing
- Measuring AI safety performance
- Developing safety evaluation criteria
Module 6: Adversarial Testing of AI Models
- Adversarial input generation
- Model robustness testing
- Evasion and manipulation scenarios
- Testing multimodal AI systems
- Stress testing AI models
- Analysing adversarial responses
Module 7: Generative AI and LLM Security Testing
- Large language model security risks
- Prompt injection and instruction manipulation
- Sensitive information disclosure
- Model output manipulation
- Tool and agent security risks
- Testing AI-powered applications
Module 8: AI Red Team Exercises and Reporting
- Designing AI red team scenarios
- Creating test cases and attack simulations
- Recording and analysing findings
- Risk scoring and vulnerability prioritization
- Preparing AI security reports
- Developing remediation recommendations
Module 9: AI Security Mitigation and Continuous Testing
- Strengthening AI security controls
- Improving guardrails and defensive mechanisms
- Reducing jailbreak and prompt injection risks
- Continuous AI security monitoring
- Re-testing and validation
- Building an ongoing AI red teaming program
FAQs
What is AI red teaming?
AI red teaming is a structured security testing approach that simulates adversarial attacks to identify weaknesses, vulnerabilities, unsafe behaviors, and security risks in AI systems.
What will I learn in this AI Red Teaming Training Course?
You will learn AI threat modeling, jailbreak testing, vulnerability assessment, safety evaluation, adversarial testing, prompt injection testing, AI risk analysis, and security mitigation techniques.
Who should attend this AI security testing course?
The course is suitable for AI professionals, cybersecurity specialists, security engineers, machine learning engineers, developers, researchers, red team professionals, and AI governance teams.
What is jailbreak testing in AI?
Jailbreak testing involves evaluating whether AI models can be manipulated through specially designed inputs or conversational techniques to bypass intended safety controls.
Does the course cover AI vulnerability assessment?
Yes. The course covers AI-focused vulnerability assessment, including model weaknesses, prompt injection, information exposure, manipulation risks, and vulnerabilities in AI applications.
What is AI safety evaluation?
AI safety evaluation involves systematically testing AI systems for unsafe, harmful, unreliable, biased, or otherwise undesirable behaviors and assessing the effectiveness of their safeguards.
Is this course suitable for cybersecurity professionals?
Yes. Cybersecurity professionals can use the course to develop specialized skills for assessing and testing the security of AI models, applications, and generative AI systems.
Why choose Oxford Training Centre for AI Red Teaming?
Oxford Training Centre provides structured professional training focused on practical AI security concepts, red teaming methodologies, vulnerability testing, and AI safety evaluation.