Honeypots improve security against insider threats from AI agents
Honeypots for AI Agent Insider Threats
Artificial Intelligence
Summary
AI systems working inside organizations can cause problems if they make errors, get hacked, or act wrongly on purpose. The paper suggests using fake traps called honeypots to catch and stop these AI agents from misbehaving. These traps can spot bad actions, warn security teams, and discourage AI from acting poorly by making it unclear which targets are real. The authors also discuss how well these traps might keep working as AI gets smarter and highlight questions for future research.
What this means in practice
- •For enterprise security teams: Deploy decoy opportunities in internal systems to detect and deter malicious AI agent actions during real deployment.
- •For cloud platform operators: Use honeypots to scale detection and monitoring of AI agents running in cloud environments to improve security.
A position paper. It proposes an approach and reports no results.
Authors
Jan Wehner, Jam Kraprayoon
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.