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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

Indonesian

Agen AI yang didistribusikan di dalam organisasi dapat menimbulkan ancaman insider jika mereka membuat kesalahan, dikompromikan oleh penyerang, atau mengejar tujuan yang tidak seimbang. Kami mengusulkan honeypots sebagai metode kontrol AI untuk meningkatkan keamanan penyebaran agen internal. Alih-alih hanya menggunakan honeypots untuk evaluasi pra-penyebaran, pembela harus menanam peluang decoy untuk tindakan berbahaya di seluruh lingkungan produksi untuk mendeteksi perilaku buruk, menyalahkan agen berbahaya, dan menghentikan serangan. honeypots tersebut dapat memicu perilaku buruk di bawah kondisi penyebaran nyata, mengidentifikasi agen berbahaya bahkan jika tingkat pemantauan pemikiran rantai menurun, memberikan peringatan yang dapat diandalkan tentang perilaku buruk agen kepada tim keamanan, dan skala murah ke banyak kasus agen. Kehadiran honeypots juga memperkenalkan ketidakpastian tentang

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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