Newest papers

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

Japanese

AIエージェントが組織内で展開する場合、ミスを犯したり、攻撃者によって脅かされる場合、内部エージェントの脅威を構成する場合、AIエージェントは、内部エージェントの展開のセキュリティを改善するためのコントロール方法としてミッドポットを提案します。インテリジェントエージェントの評価のためにミッドポットを使用するのではなく、弁護士は、不正行為を検出し、悪意のあるエージェントを非難し、攻撃を抑制するために、生産環境全体で有害なアクションのためのオプションを確立し、安全チームに悪意のあるエージェントに関する信頼できる警告を提供し、多くのエージェントに安価に拡大する必要があります。ミッドポットの存在は、実際の展開条件下

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

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