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Honeypots for AI Agent Insider Threats

Jan Wehner, Jam Kraprayoon · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26664 · cited by 0 works (OpenAlex) · open-access PDF

Korean

조직 내에 배치된 AI 에이전트는 실수, 공격자에 의해 위협되거나 잘못된 목표를 추구하는 경우 내부 에이전트 배포의 보안을 개선하기위한 AI 제어 방법으로 꿀 냄비를 제안합니다. 단지 사전 배포 평가를 위해 꿀 냄비를 사용하는 대신, 방어자는 오작동을 감지하고 악성 에이전트를 비난하고 공격을 방해하기 위해 생산 환경 전체에 걸쳐 해로운 행동에 대한 기회를 제공해야합니다. 그러한 꿀 냄비는 실제 배포 조건에서 잘못된 행동을 유발할 수 있으며, 사전 배포 추적율이 떨어지더라도 악성 에이전트를 식별해야하며, 보안 팀에 해로운 행동에 대한 신뢰할 수있는 경고를 제공

Machine translation (Meta M2M100); the English original follows.

Abstract

AI agents deployed inside organizations can pose an insider threat if they make mistakes, are compromised by an attacker, or pursue misaligned objectives. We propose honeypots as an AI control method to improve the security of internal agent deployments. Rather than only using honeypots for pre-deployment evaluations, defenders should plant decoy opportunities for harmful actions throughout the production environment to detect misbehavior, incriminate malicious agents, and deter attacks. Such honeypots can elicit misbehavior under real deployment conditions, identify malicious agents even if chain-of-thought monitorability degrades, provide reliable warnings about agent misbehavior to security teams, and scale cheaply to many agent instances. The presence of honeypots also introduces uncertainty about which opportunities are real, thus deterring malicious agents from attacking and reducing the rate of misbehavior. We discuss the limitations of honeypots and give reasons to expect them to remain effective as agent capabilities increase. Finally, we outline key research questions to measure and improve the effectiveness of honeypots: testing the robustness of honeypots, comparing their detection effectiveness with other monitoring methods, understanding their incrimination and deterrence effects, and building supporting infrastructure.

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Topics: Honeypot, Computer security, Insider, Computer science, Software deployment, Deterrence theory

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange