REVIEW 3 major objections 5 minor 29 references
Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services
T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read Opaque LLM services bill users for hidden operations nobody can audit; the paper argues auditability must be built in.
desk verdict Clear taxonomy and honest limitations, but the framework's provider-generated commitments don't bind actual computation, so it's a research agenda rather than a solution. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the Commercial Opaque LLM Service (COLS), defined as an LLM-based service that exposes only final outputs while hiding internal reasoning traces, agent communications, and tool invocations. The argument is carried by a threat-model equation in which the fair charge is the actual token and call quantities times their unit quality scores, while the reported charge uses inflated quantities and nominal quality, so the difference is extra profit $P$. Around this equation the paper organizes a taxonomy of auditing mechanisms: commitment-based auditing using hash structures such as Merkle trees, predictive auditing that estimates reasonable usage from prompts and answers, behavioral auditing that probes responses for signs of downgrade, signature auditing using fingerprints or timing signals, and hardware-backed verification through trusted execution environments. These mechanisms feed a three-layer framework in which Layer 1 executes the service, Layer 2 generates verifiable commitments and secure logs, and Layer 3 lets users and auditors verify without seeing proprietary internals.
What would settle it
One concrete observation that would settle the central claim: if a large sample of current reasoning-API bills, cross-checked against independently instrumented proxy measurements of token use and model behavior, showed that reported quantities and delivered quality always matched claimed values, the paper's stated accountability gap would largely disappear. A weaker but still decisive test is whether any major provider currently offers a public, verifiable commitment of its billed operations; if none does, the paper's premise of unverifiable billing is confirmed in practice.
Extended reading notes
Core claim
The paper's central claim is an assertion of urgency: there is an urgent need to design an auditing framework for hidden operations in Commercial Opaque LLM Services, because users are billed on the basis of quantities and qualities they cannot verify. It formalizes two risks: quantity inflation, in which a provider overreports tokens, API calls, or model calls; and quality downgrade, in which a provider silently substitutes a cheaper model or skips or simulates tool calls. Both behaviors raise the provider's profit by the gap between what is reported to the user and what is actually delivered, expressed in the paper as $P = (\hat{TQ} \cdot Tq + \hat{CQ} \cdot Cq) - (TQ \cdot \check{Tq} + CQ \cdot \check{Cq})$. The paper does not claim these manipulations are proven in current systems; rather, it treats them as realistic incentives under structural opacity and argues that the only remedy is auditable service design, including commitments, predictive checks, behavioral and signature probing, watermarking, and trusted execution environments.
Load-bearing premise
The load-bearing premise is that a profit-driven provider will cooperate with the very auditing designed to check it, by generating commitments, running in a trusted execution environment, or releasing representative data, while nothing in the framework gives users or auditors enforcement power over that choice.
Editorial extensions
If this is right
- If the central claim is correct, commercial LLM APIs should publish itemized, verifiable usage records, such as hashed reasoning summaries and Merkle-root call logs, at billing time.
- Providers that cannot produce such commitments would face competitive and regulatory pressure, since users would be able to compare auditability across services.
- Token-level and call-level auditing would become a standard part of LLM service contracts, similar to network metering or cloud resource billing.
- Quality auditing would require calibrated challenge prompts and response-fingerprinting to catch silent model or tool substitution before it becomes a systemic trust problem.
- The paper's proposed framework implies that a third-party auditor with partial access, rather than the user alone, is needed to make verification meaningful.
Reading between the lines
- The paper's framework is voluntary: a provider can decline to run in a TEE, refuse to release representative data, or omit commitments, so the practical force of the proposal depends on regulation or market pressure forcing participation.
- One testable extension is to run the paper's hidden-token ratio measurement across current and future reasoning APIs over time, since the ratio is a concrete, falsifiable indicator of the transparency gap.
- The same auditability gap likely applies to enterprise AI procurement and to regulated sectors, where billing transparency could be written into contracts as a compliance requirement.
- If commitment-based auditing is adopted, a natural next step is to make the commitments publicly verifiable on a ledger, turning one-user disputes into a shared evidence base.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This position paper argues that commercial LLM services that hide internal operations (reasoning traces, agent workflows) create a billing-accountability gap: users are charged for quantities and qualities they cannot observe, verify, or contest. It formalizes two attack classes, quantity inflation and quality downgrade, introduces Eq. (1) as a profit identity, and proposes a taxonomy of auditing strategies (commitment-based, predictive, behavioral, signature, plus watermarking and TEEs) organized into a three-layer framework. The paper also reports empirical observations on hidden-token ratios and reasoning-token length prediction difficulty.
Significance. If the posed threat model is accepted, the paper identifies a real and timely accountability gap: no technical or policy mechanism currently lets users verify token counts or model identity in opaque reasoning and agentic APIs. The formalization is clear, the taxonomy of attacks and defenses is useful, and the modular three-layer architecture is a sensible organizing principle for future research. The paper also explicitly acknowledges limitations such as the cooperation requirement and the inability of commitment-based auditing to catch adaptive inflation. Its main value is conceptual and agenda-setting rather than a validated protocol; no machine-checked proofs, reproducible code, or fully specified evaluation accompanies the proposed framework.
major comments (3)
- [Section 3.4; Section 5, Layer 2] The commitment-based auditing mechanism lacks an independent root of trust and therefore does not satisfy the Section 2.2 threat model. The paper states that commitment-based auditing "requires COLS's cooperation" and that in Layer 2 the COLS, "possibly under auditor supervision," encodes operations into verifiable commitments. A Merkle-tree commitment only binds the provider to a log string, not to the actual computation: a provider that overreports TQ or CQ can build a commitment over a fabricated log, and all hash-based checks will pass. The paper mentions auditor supervision and TEEs as complements, but does not make independent attestation or regulatory enforcement a mandatory first-class component; as written, the framework cannot detect even naive inflation under its own threat model. The authors should either add a hardware or regulatory root of trust or explicitly reframe the framework as cooperative auditing outside the adversarial setting.
- [Tables 2 and 4] The empirical results used to motivate the paper are reported without methodology. Table 2 gives R/A token ratios for OpenAI APIs but does not state which prompts or datasets were used, how many queries were sampled, the API parameter settings, the dates of collection, or any variance measures; Table 4 reports classification and regression accuracies for reasoning-token length prediction without describing the two-layer network architecture, training/test split, feature normalization, or repeated-seed statistics. Because Section 1 uses Table 2 to claim that "more than 90% of the tokens billed to the user are never exposed" and Section 3.3 uses Table 4 to support the variability challenge, these claims cannot currently be evaluated. Provide the methodology or relabel the numbers as illustrative.
- [Section 3.4; Section 4.4] Predictive auditing and TEE-based auditing both depend on provider cooperation, which the threat model rules out for adversarial providers. Predictive auditing requires representative data samples from the COLS; if the provider is profit-driven and already misreports usage, it has no incentive to release accurate statistics, so the predictive model may be trained on data produced by the same entity under audit. TEE-based auditing is described as best suited for high-stakes deployments, but the provider decides whether to run in an enclave at all, so it does not constrain a non-cooperating provider. These limitations are acknowledged verbally but not resolved; they should be treated as open problems in the research agenda rather than as components of the proposed framework.
minor comments (5)
- [Section 2.2, Eq. (1)] Equation (1) defines Tq and Cq as "unit quality scores" but then uses them as per-unit prices and costs in the profit formula; clarify whether these are price coefficients, cost coefficients, or quality weights, and state explicitly the simplifying assumption that quality is proportional to cost.
- [Section 2.1] There is a duplicated sentence near the end of the agentic API paragraph: "These behaviors are difficult to detect and audit. These manipulations are difficult to detect, making effective auditing especially challenging in agentic APIs." Merge or revise.
- [Section 4.4] The claim that modern TEEs introduce "under 3% throughput loss" is given without a citation; add a reference or qualify the claim as an estimate.
- [References] The reference list includes "Open Thoughts Team. Open Thoughts, January 2025" but this entry is not cited in the body; either cite it where open datasets are discussed or remove it.
- [Tables 1 and 3] Pricing information in Tables 1 and 3 would benefit from collection dates and source URLs for each plan, since commercial pricing changes frequently and the paper does not state when the data was retrieved.
Circularity Check
No circularity: the paper's reasoning is definitional and empirical, with no derived prediction that reduces to its inputs.
full rationale
This is a position and roadmap paper, not a derivation chain. Its only formal expression, Eq. (1), restates the threat model as an accounting identity: profit equals reported quantity times nominal quality minus actual quantity times actual quality. It is a formalization of the assumed incentives, not a result derived from prior premises. The empirical exhibits (Tables 2 and 4) report measurements of current API behavior and a negative predictive-auditing experiment; they are not fitted parameters subsequently relabeled as predictions. The proposed auditing strategies are explicitly framed as research directions with stated limitations, e.g., 'commitment-based auditing requires COLS’ cooperation' and cannot detect adaptive inflation, and TEEs 'require enclave-enabled infrastructure.' These are acknowledged scope constraints, not premises smuggled in as conclusions. Citations to prior work by the present authors (Cai et al. on model substitution; Li et al. on agent vulnerabilities) support background motivation but are not the sole load-bearing evidence for any claimed derived result; the central claim is a normative call for research and policy, not a mathematical consequence of those citations. No step in the paper reduces, by construction, to its own input or to a self-citation chain.
Assumptions & free parameters
assumptions (5)
- domain assumption COLS will not fully expose hidden operations; any disclosure must be protected.
- domain assumption COLS are profit-driven and may falsify quantity or quality metadata.
- domain assumption Users and auditors have no access to internal traces unless the provider grants it.
- ad hoc to paper A sufficient number of providers and auditors will adopt commitments, TEEs, or data-release protocols despite no enforcement mechanism.
- ad hoc to paper Representative proxy datasets for usage estimation will be available to train predictive auditors.
Cite this review
Pith. "Pith review of Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services." pith.science (2026). https://pith.science/paper/FM74XULL
@misc{pith2026250518471,
author = {Pith},
title = {Pith review of: Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services},
year = {2026},
howpublished = {\url{https://pith.science/paper/FM74XULL}},
note = {Machine review of arXiv:2505.18471}
}
read the original 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.
Figures
Reference graph
Works this paper leans on
-
[3]
URL https://www.anthropic.com/news/ claude-4. Accessed: 2025-05-22. Will Cai, Tianneng Shi, Xuandong Zhao, and Dawn Song. Are you getting what you pay for? auditing model substitution in llm apis. arXiv preprint arXiv:2504.04715,
arXiv 2025
-
[4]
Stealing part of a production language model
Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, et al. Stealing part of a production language model. arXiv preprint arXiv:2403.06634,
-
[6]
URL https://arxiv.org/abs/2501.12948. Hugging Face. Open r1: A fully open reproduction of deepseek-r1, January
-
[7]
Xueyang Feng, Zhi-Yuan Chen, Yujia Qin, Yankai Lin, Xu Chen, Zhiyuan Liu, and Ji-Rong Wen
URL https: //github.com/huggingface/open-r1. Xueyang Feng, Zhi-Yuan Chen, Yujia Qin, Yankai Lin, Xu Chen, Zhiyuan Liu, and Ji-Rong Wen. Large language model-based human-agent collaboration for complex task solving. arXiv preprint arXiv:2402.12914,
-
[8]
URL https://docs.firecrawl.dev/features/ alpha/deep-research. Accessed: 2025-05-22. Glaive AI. Glaive reasoning v1 20m. https://huggingface.co/datasets/glaiveai/ reasoning-v1-20m,
work page 2025
-
[9]
Available from Hugging Face Datasets. Company:https://glaive. ai. Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, et al. Planning, creation, usage: Benchmarking llms for comprehensive tool utilization in real-world complex scenarios. arXiv preprint arXiv:2401.17167,
-
[10]
Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276,
-
[11]
10 Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai o1 system card. arXiv preprint arXiv:2412.16720,
Show all 29 references
-
[12]
Commercial llm agents are already vulnerable to simple yet dangerous attacks
Ang Li, Yin Zhou, Vethavikashini Chithrra Raghuram, Tom Goldstein, and Micah Goldblum. Commercial llm agents are already vulnerable to simple yet dangerous attacks. arXiv preprint arXiv:2502.08586,
-
[13]
Toolsandbox: A stateful, conversational, interactive evaluation benchmark for llm tool use capabilities
Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, et al. Toolsandbox: A stateful, conversational, interactive evaluation benchmark for llm tool use capabilities. arXiv preprint arXiv:2408.04682,
-
[14]
Accessed: 2025-05-22
URL https://www.manusai.io/. Accessed: 2025-05-22. Ralph C Merkle. A digital signature based on a conventional encryption function. In Conference on the theory and application of cryptographic techniques, pages 369–378. Springer,
2025
-
[16]
Can llm watermarks robustly prevent unauthorized knowledge distillation? arXiv preprint arXiv:2502.11598,
Leyi Pan, Aiwei Liu, Shiyu Huang, Yijian Lu, Xuming Hu, Lijie Wen, Irwin King, and Philip S Yu. Can llm watermarks robustly prevent unauthorized knowledge distillation? arXiv preprint arXiv:2502.11598,
-
[17]
Teach llms to phish: Stealing private information from language models
Ashwinee Panda, Christopher A Choquette-Choo, Zhengming Zhang, Yaoqing Yang, and Prateek Mittal. Teach llms to phish: Stealing private information from language models. arXiv preprint arXiv:2403.00871,
-
[18]
The what, why, and how of context length extension techniques in large language models–a detailed survey
Saurav Pawar, SM Tonmoy, SM Zaman, Vinija Jain, Aman Chadha, and Amitava Das. The what, why, and how of context length extension techniques in large language models–a detailed survey. arXiv preprint arXiv:2401.07872,
-
[19]
Accessed: 2025- 05-22
URL https://relevanceai.com/. Accessed: 2025- 05-22. Matthew Renze and Erhan Guven. Self-reflection in llm agents: Effects on problem-solving perfor- mance. arXiv preprint arXiv:2405.06682,
2025 arXiv
-
[20]
Great, now write an article about that: The crescendo multi-turn llm jailbreak attack
Mark Russinovich, Ahmed Salem, and Ronen Eldan. Great, now write an article about that: The crescendo multi-turn llm jailbreak attack. arXiv preprint arXiv:2404.01833,
-
[21]
Antidistillation sampling
Yash Savani, Asher Trockman, Zhili Feng, Avi Schwarzschild, Alexander Robey, Marc Finzi, and J Zico Kolter. Antidistillation sampling. arXiv preprint arXiv:2504.13146,
-
[22]
Stop overthinking: A survey on efficient reasoning for large language models
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Hanjie Chen, et al. Stop overthinking: A survey on efficient reasoning for large language models. arXiv preprint arXiv:2503.16419,
-
[23]
Why and how llms hallucinate: Connecting the dots with subsequence associations
Yiyou Sun, Yu Gai, Lijie Chen, Abhilasha Ravichander, Yejin Choi, and Dawn Song. Why and how llms hallucinate: Connecting the dots with subsequence associations. arXiv preprint arXiv:2504.12691,
-
[24]
Multi-agent collaboration mechanisms: A survey of llms
11 Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O’Sullivan, and Hoang D Nguyen. Multi-agent collaboration mechanisms: A survey of llms. arXiv preprint arXiv:2501.06322,
-
[25]
Agentxploit: End-to-end redteaming of black-box ai agents
Zhun Wang, Vincent Siu, Zhe Ye, Tianneng Shi, Yuzhou Nie, Xuandong Zhao, Chenguang Wang, Wenbo Guo, and Dawn Song. Agentxploit: End-to-end redteaming of black-box ai agents. arXiv preprint arXiv:2505.05849,
-
[26]
Plangenllms: A modern survey of llm planning capabilities
Hui Wei, Zihao Zhang, Shenghua He, Tian Xia, Shijia Pan, and Fei Liu. Plangenllms: A modern survey of llm planning capabilities. arXiv preprint arXiv:2502.11221,
-
[27]
A survey on trustworthy llm agents: Threats and countermeasures
Miao Yu, Fanci Meng, Xinyun Zhou, Shilong Wang, Junyuan Mao, Linsey Pang, Tianlong Chen, Kun Wang, Xinfeng Li, Yongfeng Zhang, et al. A survey on trustworthy llm agents: Threats and countermeasures. arXiv preprint arXiv:2503.09648,
-
[28]
Verbosity̸= veracity: Demystify verbosity compensation behavior of large language models
Yusen Zhang, Sarkar Snigdha Sarathi Das, and Rui Zhang. Verbosity̸= veracity: Demystify verbosity compensation behavior of large language models. arXiv preprint arXiv:2411.07858,
-
[29]
Distillation-resistant watermarking for model protection in nlp
Xuandong Zhao, Lei Li, and Yu-Xiang Wang. Distillation-resistant watermarking for model protection in nlp. arXiv preprint arXiv:2210.03312,
-
[1987]
s1: Simple test-time scaling
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. s1: Simple test-time scaling. arXiv preprint arXiv:2501.19393,
-
[2022]
Provable robust watermarking for ai-generated text
Xuandong Zhao, Prabhanjan Ananth, Lei Li, and Yu-Xiang Wang. Provable robust watermarking for ai-generated text. arXiv preprint arXiv:2306.17439,
-
[2023]
arXiv preprint ARXIV .2312.11805, pages 24–28,
doi: 10.48550. arXiv preprint ARXIV .2312.11805, pages 24–28,
-
[2024]
Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wanxiang Che
URL https://arxiv.org/abs/2412.18925. Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wanxiang Che. Towards reasoning era: A survey of long chain-of- thought for reasoning large language models. arXiv preprint ar...
Reviewed August 7, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.