How NOT to Train Your Hack Bot - Dos and Don'ts of Building Offensive GPTs

Overview

Explore the potential and limitations of using large language models for offensive security operations, including techniques for finding vulnerabilities and ethical considerations.

Syllabus

• Introduction to Offensive Security and AI
Overview of offensive security operations
Introduction to large language models (LLMs)
• Understanding the Capabilities and Limitations of LLMs
Capabilities of GPTs in security contexts
Limitations and challenges in AI-driven security
• Techniques for Finding Vulnerabilities
Automated code analysis and vulnerability detection
Language model-assisted penetration testing
Identifying social engineering opportunities with AI
• Ethical Considerations in Offensive AI Operations
The ethics of using AI for offensive security
Legal implications and compliance requirements
Developing guidelines for ethical AI usage in security
• Dos and Don'ts for Building Offensive GPTs
Best practices for designing ethical offensive GPTs
Common pitfalls and how to avoid them
Case studies of successful and unsuccessful implementations
• Security Measures Against Misuse
Techniques for safeguarding GPTs against misuse
Implementing monitoring and control mechanisms
Response strategies for AI-driven security breaches
• Incorporating Human Oversight and Control
The role of human expertise in AI security operations
Strategies for effective human-AI collaboration
• Future Trends and Research Directions
Emerging trends in AI for security
Opportunities and challenges in the evolving landscape
• Conclusion
Summary of key learnings
Resources for further study and exploration in AI and security
• Assessment and Evaluation
Practical exercises and projects
Final assessment guidelines and criteria

Subjects

Conference Talks