Honeypots for AI Agent Insider Threats

Key points

• AI agents deployed inside organizations can pose an insider threat if they make mistakes, are compromised by an attacker, or pursue misaligned objectives.1
• 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.1
• 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.1
• The presence of honeypots also introduces uncertainty about which opportunities are real, thus deterring malicious agents from attacking and reducing the rate of misbehavior.1

Sources (1)

• [1]Honeypots for AI Agent Insider Threats
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:19 PM

  We propose honeypots as an AI control method to improve the security of internal agent deployments.

  AI agents deployed inside organizations can pose an insider threat if they make mistakes, are compromised by an attacker, or pursue misaligned objectives.

Extractive summary: sentences quoted from the sources.