Research repository focused on prompt injection attacks, AI jailbreaking methods, and mitigation strategies for large language model (LLM) systems.
This repository documents practical research into AI security risks associated with prompt injection and LLM-connected applications.
The project explores:
- Prompt injection techniques
- Reflection model bypass concepts
- Encoded prompt attacks
- AI jailbreaking methodologies
- OWASP LLM risk mapping
- Defensive mitigation strategies
All material was recreated from personal learning, public educational labs, and independent security research.
- AI security research
- Prompt injection analysis
- Threat modeling
- OWASP AI risk analysis
- Security documentation
- LLM attack methodology
- Defensive security recommendations
Investigation of how crafted prompts can manipulate model behavior and bypass restrictions.
Research into multi-stage AI filtering systems and methods attackers use to evade secondary validation models.
Study of techniques involving:
- Base64 encoding
- Binary encoding
- Indirect prompting
- Multi-stage conversational manipulation
Analysis of risks associated with:
- Sensitive information disclosure
- Excessive AI permissions
- Unsafe plugin integrations
- Prompt manipulation
- Unauthorized actions
Recommended mitigation approaches include:
- Input sanitization
- Output filtering
- Policy-based validation
- Least-privilege AI integrations
- Human approval workflows
- Monitoring and logging
This repository is intended strictly for defensive security education and AI security awareness.
No malicious payloads, weaponized prompts, or harmful automation tools are included.
research/ -> AI security concepts and attack methodology
test-cases/ -> Educational prompt injection examples
mitigations/ -> Defensive recommendations
owasp-mapping/ -> OWASP AI risk analysis- Large Language Models (LLMs)
- Prompt injection
- OWASP Top 10 for LLMs
- AI threat modeling
- AI security controls
- AI policy enforcement