AgentPolicyForge: From Executable Environments to Policy Benchmarks and Adaptive Guards

Key points

• Customized agent deployments define policies over tools, resources, and execution conditions.1
• Providing these policies in the task agent's prompt does not reliably prevent violations.1
• The case construction process supports iterative guard training: an attacker generates requests using retrieved strategies, and the guard is fine-tuned on balanced, environment-verified examples.1
• We compare policy-aware task agent self-regulation with a trained policy guard.1

Sources (1)

• [1]AgentPolicyForge: From Executable Environments to Policy Benchmarks and Adaptive Guards
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 11:31 PM

  We introduce AgentPolicyForge, a framework that uses executable environments to construct policy benchmarks and iteratively train policy guards.

  Customized agent deployments define policies over tools, resources, and execution conditions.

Extractive summary: sentences quoted from the sources.