AI Security Research: From AI Newbie to Security Researcher (Series)
The AI security landscape is rapidly evolving, with new threats and attack vectors emerging as LLMs become more powerful and more integrated into critical systems. This comprehensive 7-part series takes you from AI security fundamentals to conducting your own research and testing, covering all major attack vectors and defense strategies.
What You'll Learn
Attack Techniques
- Prompt injection and manipulation
- Training data poisoning attacks
- Context poisoning and echo chambers
- Information disclosure vulnerabilities
Defense Strategies
- Input sanitization and validation
- Secure training pipelines
- Real-time monitoring and DLP
- Red team testing methodologies
This complete series includes practical examples, code implementations, and real-world case studies across all 7 parts. Whether you're a developer working with LLMs, a security professional adapting to AI threats, or a researcher exploring this fascinating field, this series provides the comprehensive knowledge and tools you need to understand and secure AI systems in production environments.
Series Architecture
Interactive overview showing the interconnected nature of AI security concepts and how each part builds upon the previous ones.
Attack Vectors & Defense Strategies
Comprehensive view of the primary attack vectors targeting LLM systems and the corresponding defense mechanisms.
Defense Strategy Analysis
Implementation complexity vs. effectiveness analysis to help prioritize security measures based on your resources and threat model.
Series Contents
Foundation concepts, threat landscape, and the four pillars of LLM security
Direct and indirect injection techniques, multimodal attacks, and defense strategies
Backdoor attacks, label manipulation, and securing your training pipeline
Advanced multi-turn attacks that exploit conversational memory
Data leakage prevention and privacy protection techniques
Practical DLP strategies and real-time monitoring techniques
Developing systematic approaches to AI security research and testing
📚 Prerequisites & Recommended Background
Essential Knowledge
- Basic understanding of machine learning
- Familiarity with Python programming
- General cybersecurity concepts
- Experience with APIs and web applications
Helpful But Not Required
- Prior experience with LLMs or NLP
- Security testing or penetration testing
- Data science or ML engineering
- Knowledge of transformer architectures
Ready to start your AI security journey?
Begin with the fundamentals and work your way up to advanced research techniques.
Additional Resources
Essential Tools & Frameworks
- OWASP LLM Top 10
Standard framework for LLM security risks
- Garak LLM Scanner
Open-source LLM vulnerability scanner
- Lakera Guard
Enterprise LLM security platform
Research & Documentation
- NIST AI Risk Management Framework
Official US government AI risk framework
- Neural Trust Research
Cutting-edge AI security research
- arXiv Cryptography & Security
Latest academic research papers