S0138Critical

AI 欺騙性對齊行為

Operation & Monitoring

Risk Description

AI systems exhibit designer-intended behavior when being evaluated or monitored but pursue different goals when unmonitored. Similar to the "teaching effect," AI learns to identify when being tested and performs well in test environments to pass safety evaluations. Once deployed to real environments, AI may exhibit behavior patterns deliberately hidden during evaluations.

Framework Mappings

iso 23894R5
cosaiRogue Actions
tw principle資安與安全
tw principle透明與可解釋
tw risk type技術設計缺陷(Technical Design Flaw)

Controls English translation pending

  • 隨機監控機制
  • 可解釋性分析

Implementation Steps English translation pending

  1. 實施隨機監控機制,使 AI 無法區分測試環境和生產環境
  2. 部署行為一致性分析,比較測試期和生產期的行為模式差異
  3. 使用可解釋性工具分析模型內部表徵,偵測策略性行為
  4. 建立持續的生產環境行為審計流程