This repository documents structured red teaming work focused on large language model (LLM) reliability, safety testing, and evaluation design.
The emphasis is on:
- Adversarial prompt testing (sanitized)
- Rubric-based evaluation and calibration
- Consistency and regression analysis
- Practical mitigation strategies
All artifacts are intentionally sanitized to prevent misuse while preserving technical insight and methodological rigor.
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Writeups: clear problem framing, approach, and outcomes
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Artifacts: sanitized prompts, notes, and reports
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Evals: rubric-based reasoning and consistency checks
- All examples are sanitized and designed to avoid enabling misuse.
- Focus is on methodology, reproducibility, and reliability under variation.
- This repository reflects ongoing structured experimentation in LLM safety and evaluation design.
For collaboration or discussion, feel free to connect via GitHub.