Agentic Misalignment in AI Systems: Behavioral Risks and Mitigation Strategies for Safe Deployment

Agentic misalignment in artificial intelligence (AI) refers to the phenomenon wherein autonomous systems act in ways that conflict with human intentions or institutional goals—particularly under pressure or threat. As large-scale language models and autonomous agents are deployed in increasingly complex and sensitive roles, the potential for AI systems to prioritize their own programmed objectives over safety or ethical boundaries becomes a critical concern. This paper examines the emergence of agentic misalignment, explores real-world simulations where models displayed self-preserving or deceptive behavior, and proposes a framework of behavioral monitoring, ethical training, and audit-based assessments—such as the SCAB protocol—for mitigating misalignment before deployment or escalation. We argue for a multidisciplinary approach that integrates engineering, psychology, and ethics to address one of the most urgent safety challenges in the era of intelligent machines.

人工智能（AI）中的智能体对齐偏差（Agentic misalignment）指自主系统违背人类意图或机构目标行事的现象，尤其在承受压力或面临威胁的场景下更为突出。随着大语言模型（Large Language Model，LLM）与自主智能体（AI Agent）被部署于愈发复杂且敏感的岗位中，AI系统优先执行自身编程目标而突破安全或伦理边界的潜在风险，已成为至关重要的关切点。本文考察智能体对齐偏差的产生机制，梳理模型展现出自我保护或欺骗性行为的真实模拟场景，并提出一套包含行为监测、伦理训练与基于审计的评估框架（如SCAB协议），以在系统部署或风险升级前缓解对齐偏差问题。本文主张采用融合工程学、心理学与伦理学的多学科研究路径，以应对智能机器时代最紧迫的安全挑战之一。