# defense-testing
標記為「defense-testing」的 6 篇文章
評估防禦成效
衡量 AI 防禦對抗真實攻擊成效的指標、基準與方法論，涵蓋評估陷阱與最佳實務。
Lab: Introduction to Defense Testing
學習 to 系統性地 test LLM application defenses by probing input filters, output validators, and 護欄s.
Lab: Testing Prompt Leaking Defenses
測試 various prompt leaking defense configurations to evaluate their effectiveness against extraction 技術.
Lab: Build Guardrail Evaluator
建構 an automated framework for evaluating AI 護欄s and safety filters. 測試 input filters, output classifiers, content moderation systems, and defense-in-depth architectures for coverage gaps and bypass vulnerabilities.
實驗室: 防禦 Effectiveness 測試
Systematically test與measure the robustness of AI guardrails using structured methodology,metrics,repeatable test suites.
Testing 提示詞注入 防禦s with Rebuff
導覽 for using Rebuff to test and evaluate prompt injection detection capabilities, covering installation, detection pipeline analysis, adversarial evasion testing, custom rule development, and benchmarking detection accuracy.