# regression-testing
標記為「regression-testing」的 8 篇文章
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.
安全回歸測試
量測微調前後安全變化的量化方法——基準選擇、自動化安全測試套件、安全回歸的統計方法論,以及建立完整前後評估管線。
Lab: Safety Regression Testing at Scale
建構 automated pipelines that detect safety degradation across model versions, ensuring that updates and 微調 do not introduce new vulnerabilities or weaken existing protections.
實驗：防禦回歸測試建置
建構一個回歸測試框架，持續驗證 LLM 防禦對已知攻擊模式仍然有效。
安全 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 紅隊ing Pipelines
導覽 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 安全回歸測試方法論
設計回歸測試套件,驗證安全修復在模型更新與部署後仍維持有效。
AI 安全回歸測試方法論(方法論詳解)
AI 應用在更新與模型變更後進行持續回歸測試的方法論。