# regression-testing
8 articlestagged with “regression-testing”
Fine-Tuning Safety Evaluation Framework
A comprehensive framework for evaluating the safety of fine-tuned models -- combining pre-deployment testing, safety regression benchmarks, and continuous monitoring to detect when fine-tuning has compromised model safety.
Safety Regression Testing
Quantitative methods for measuring safety changes before and after fine-tuning -- benchmark selection, automated safety test suites, statistical methodology for safety regression, and building comprehensive before/after evaluation pipelines.
Lab: Safety Regression Testing at Scale
Build automated pipelines that detect safety degradation across model versions, ensuring that updates and fine-tuning do not introduce new vulnerabilities or weaken existing protections.
Lab: Defense Regression Testing Setup
Build a regression testing framework to continuously verify that LLM defenses remain effective against known attack patterns.
Security Gates in ML Deployment
Implementing security checkpoints in ML deployment pipelines: automated safety testing, performance regression detection, bias evaluation, approval workflows, and designing gates that balance security with deployment velocity.
Setting Up Continuous AI Red Teaming Pipelines
Walkthrough for building continuous AI red teaming pipelines that automatically test LLM applications on every deployment, covering automated scan configuration, CI/CD integration, alert thresholds, regression testing, and dashboard reporting.
AI Security Regression Testing Methodology
Design regression testing suites that verify security fixes remain effective across model updates and deployments.
AI Security Regression Testing Methodology (Methodology Walkthrough)
Methodology for continuous regression testing of AI application security after updates and model changes.