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AI Red Teaming Roadmap¶

Roadmap: https://roadmap.sh/ai-red-teaming

1. Introduction¶

1.1 AI Security Fundamentals
1.2 Why Red Team AI Systems?
1.3 Ethical Considerations
1.4 Role of Red Teams

2. Foundational Knowledge¶

2.1 AI / ML Fundamentals¶

2.1.1 Supervised Learning
2.1.2 Unsupervised Learning
2.1.3 Reinforcement Learning
2.1.4 Neural Networks
2.1.5 Generative Models
2.1.6 Large Language Models
2.1.7 Prompt Engineering

2.2 Cybersecurity Principles¶

2.2.1 Confidentiality, Integrity, Availability
2.2.2 Threat Modeling
2.2.3 Risk Management
2.2.4 Vulnerability Assessment

3. Prompt Hacking¶

3.1 Jailbreak Techniques
3.2 Safety Filter Bypasses
3.3 Prompt Injection
3.3.1 Direct
3.3.2 Indirect
3.4 Countermeasures

4. Model Vulnerabilities¶

4.1 Model Weight Stealing
4.2 Unauthorized Access
4.3 Model Extraction
4.4 Data Poisoning
4.5 Adversarial Examples
4.6 Model Inversion
4.7 Model Manipulation
4.8 Adversarial Training
4.9 Robust Model Design
4.10 Continuous Monitoring
4.11 Defense Strategies

5. System Security¶

5.1 Code Injection¶

5.1.1 Insecure Deserialization
5.1.2 Remote Code Execution

5.2 Infrastructure Security¶

5.2.1 API Protection
5.2.2 Authentication

6. Testing Methodologies¶

6.1 Black Box Testing
6.2 White Box Testing
6.3 Grey Box Testing
6.4 Automated vs Manual
6.5 Continuous Testing

7. Tools and Frameworks¶

7.1 Testing Platforms
7.2 Monitoring Solutions
7.3 Benchmark Datasets
7.4 Custom Testing Scripts
7.5 Reporting Tools

8. Professional Development¶

8.1 Certifications¶

8.1.1 Specialized Courses
8.1.2 Industry Credentials

8.2 Practical Experience¶

8.2.1 Lab Environments
8.2.2 CTF Challenges
8.2.3 Red Team Simulations

8.3 Community Engagement¶

8.3.1 Conferences
8.3.2 Research Groups
8.3.3 Forums

9. Real-world Applications¶

9.1 LLM Security Testing
9.2 Agentic AI Security
9.3 Responsible Disclosure

10. Future Directions¶

10.1 Emerging Threats
10.2 Advanced Techniques
10.3 Research Opportunities
10.4 Industry Standards