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Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services

Guoheng Sun, Ziyao Wang, Xuandong Zhao, Bowei Tian, Zheyu Shen, Yexiao He, Jinming Xing, Ang Li

TL;DR

Addresses the transparency gap in Commercial Opaque LLM Services (COLS) where users are billed for hidden operations. The authors formalize actual quantities $T_Q$, $C_Q$ and unit qualities $T_q$, $C_q$, and analyze two failure modes: quantity inflation and quality downgrade. They propose a three-layer auditing framework and a suite of auditing methods, including commitment-based, predictive, behavioral, and signature-based approaches, with optional watermarking and TEEs. The framework aims to provide verifiable, privacy-preserving auditability that balances provider confidentiality with user accountability, guiding policy and governance in commercial LLM ecosystems.

Abstract

Modern large language model (LLM) services increasingly rely on complex, often abstract operations, such as multi-step reasoning and multi-agent collaboration, to generate high-quality outputs. While users are billed based on token consumption and API usage, these internal steps are typically not visible. We refer to such systems as Commercial Opaque LLM Services (COLS). This position paper highlights emerging accountability challenges in COLS: users are billed for operations they cannot observe, verify, or contest. We formalize two key risks: \textit{quantity inflation}, where token and call counts may be artificially inflated, and \textit{quality downgrade}, where providers might quietly substitute lower-cost models or tools. Addressing these risks requires a diverse set of auditing strategies, including commitment-based, predictive, behavioral, and signature-based methods. We further explore the potential of complementary mechanisms such as watermarking and trusted execution environments to enhance verifiability without compromising provider confidentiality. We also propose a modular three-layer auditing framework for COLS and users that enables trustworthy verification across execution, secure logging, and user-facing auditability without exposing proprietary internals. Our aim is to encourage further research and policy development toward transparency, auditability, and accountability in commercial LLM services.

Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services

TL;DR

Addresses the transparency gap in Commercial Opaque LLM Services (COLS) where users are billed for hidden operations. The authors formalize actual quantities , and unit qualities , , and analyze two failure modes: quantity inflation and quality downgrade. They propose a three-layer auditing framework and a suite of auditing methods, including commitment-based, predictive, behavioral, and signature-based approaches, with optional watermarking and TEEs. The framework aims to provide verifiable, privacy-preserving auditability that balances provider confidentiality with user accountability, guiding policy and governance in commercial LLM ecosystems.

Abstract

Modern large language model (LLM) services increasingly rely on complex, often abstract operations, such as multi-step reasoning and multi-agent collaboration, to generate high-quality outputs. While users are billed based on token consumption and API usage, these internal steps are typically not visible. We refer to such systems as Commercial Opaque LLM Services (COLS). This position paper highlights emerging accountability challenges in COLS: users are billed for operations they cannot observe, verify, or contest. We formalize two key risks: \textit{quantity inflation}, where token and call counts may be artificially inflated, and \textit{quality downgrade}, where providers might quietly substitute lower-cost models or tools. Addressing these risks requires a diverse set of auditing strategies, including commitment-based, predictive, behavioral, and signature-based methods. We further explore the potential of complementary mechanisms such as watermarking and trusted execution environments to enhance verifiability without compromising provider confidentiality. We also propose a modular three-layer auditing framework for COLS and users that enables trustworthy verification across execution, secure logging, and user-facing auditability without exposing proprietary internals. Our aim is to encourage further research and policy development toward transparency, auditability, and accountability in commercial LLM services.

Paper Structure

This paper contains 17 sections, 1 equation, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Problem Formulation
  3. Commercial Opaque LLM Service
  4. Threat Model
  5. Auditing Principles
  6. Quantity Inflation and Auditing of Hidden Operations
  7. Reasoning LLM: Token Inflation and Token Auditing
  8. Agentic LLMs: Call Inflation and Call Auditing
  9. Challenges of Quantity Auditing
  10. Possible Solutions
  11. Quality Downgrade and Auditing of Hidden Operations
  12. Reasoning LLM: Model Downgrade and Model Auditing
  13. Agentic LLMs: Tool Downgrade and Tool Auditing
  14. Challenges
  15. Possible Solutions
  16. ...and 2 more sections

Figures (2)

  • Figure 1: Overview of Commercial Opaque LLM Services and their hidden operations. Part of the illustration was generated by GPT-4o hurst2024gpt.
  • Figure 2: Three-layer architecture of the auditing framework. Layer 1 handles execution, Layer 2 generates verifiable commitments, and Layer 3 provides auditing services.