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

French

Les agents d'IA déployés au sein d'organisations peuvent constituer une menace d'intérieur s'ils commettent des erreurs, sont compromis par un attaquant ou poursuivent des objectifs désalignés. Nous proposons des potes de miel comme méthode de contrôle d'IA pour améliorer la sécurité des déploiements d'agents internes. Plutôt que d'utiliser seulement des potes de miel pour des évaluations de pré-déploiement, les défenseurs devraient planter des opportunités de décoyage pour des actions nocives dans tout l'environnement de production pour détecter des comportements défavorables, incriminer des agents malveillants et dissuader les attaques. Ces potes de miel peuvent déclencher des comportements défavorables dans des conditions réelles de déploiement,

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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