A framework for evaluating Large Language Model safety through systematic testing.
This project documents defensive approaches to LLM security:
- Understanding common vulnerability patterns
- Testing methodologies for safety evaluation
- Defense strategies and guardrails
For comprehensive LLM security information, see these authoritative sources:
| Resource | Description |
|---|---|
| OWASP Top 10 for LLMs | Industry-standard security risks |
| Microsoft PromptBench | Robustness evaluation framework |
| NIST AI RMF | Risk management framework |
| Anthropic Safety Research | Alignment and safety research |
- Methodology - Testing principles and approach
- Defense Strategies - Protective measures
This project follows responsible disclosure principles:
- No publication of working exploit techniques
- Focus on defensive measures
- Coordination with vendors before disclosure
MIT - See LICENSE
For security researchers with legitimate needs, contact the maintainers.