paperAugust 2026Unreviewed

AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes

Xun Wang, Bihe Zhao, Michael Backes, Franziska Boenisch, Adam Dziedzic

Abstract

Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to save deployment costs. All existing audits decide backbone identity from the text-output channel, which is structurally fragile for agentic APIs because modern serving stacks (OpenAI, Anthropic, Gemini, Cloudflare Workers AI, LangGraph) discard text and expose only structured actions when the model calls a tool, and provider-injected system prompts

Cite

@misc{wang2026agentprov,
  title = {{AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes}},
  author = {Xun Wang and Bihe Zhao and Michael Backes and Franziska Boenisch and Adam Dziedzic},
  year = {2026},
  month = aug,
  eprint = {2609.00052},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.00052}
}