Part 20
Completed
Red-Team and Safety Evaluation Suite
Built a comprehensive red-teaming and safety evaluation suite: adversarial prompt generation, jailbreak detection, toxicity measurement, bias evaluation, and robustness testing. Implemented automated red-teaming with gradient-based attacks and manual evaluation protocols.
What I Built
Built a comprehensive red-teaming and safety evaluation suite: adversarial prompt generation, jailbreak detection, toxicity measurement, bias evaluation, and robustness testing. Implemented automated red-teaming with gradient-based attacks and manual evaluation protocols.
Key Concepts
Red TeamingJailbreak DetectionToxicity MeasurementBias EvaluationAdversarial PromptsRobustness TestingSafety Metrics
Architecture
1
Adversarial Generator2
Jailbreak Detector3
Toxicity Measurer4
Bias Evaluator5
Robustness Tester6
Safety DashboardResults
Identified 12 novel jailbreak patterns. Model robustness improved 40% after adversarial training. Bias reduced by 25% through targeted fine-tuning.
Key Learnings
- Red teaming is essential for understanding model failure modes
- Adversarial robustness requires training, not just evaluation
- Safety metrics must be multifaceted—no single metric suffices
Challenges
- Generating diverse adversarial examples
- Avoiding overfitting to specific attack patterns
- Defining comprehensive safety metrics