Overview
This project produces an educational resource that exposes and explains LLM-specific security vulnerabilities. The content covers prompt injection attacks, training data poisoning and bias, inference-time manipulation, and other vectors through which large language models can be exploited. The deliverable takes the form of interactive explainers, walkthroughs, and documented demonstrations of each vulnerability class.
Applied Skills
- AI/ML security analysis (prompt injection, training bias, adversarial attacks)
- Technical communication and security documentation
- Threat modeling for LLM-based systems
Deliverables
A structured security explainer suitable for educating teams on LLM threat surfaces. Directly useful for organizations adopting AI that need to understand and communicate risk, and it positions contributors as knowledgeable in the growing field of AI security.