{"id":"c269c319-b18a-47f5-95dd-e9f32799b456","arxiv_id":"2605.30040","paper_version":1,"verdict":"ACCEPT","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"low","formal_verification":"none","parameter_count":0,"one_line_summary":"Dishonest LLM providers can inflate reported token counts by up to 1469% without detection in existing auditing frameworks due to reliance on provider-controlled evidence.","lead":"This paper shows that current per-token billing for LLMs is vulnerable to systematic over-reporting because audits must rely on proofs supplied by the provider. A smart generalist should read it because per-token pricing is now standard and the integrity of those bills directly affects real costs as AI usage grows.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest_assumption directly restates the paper's own framing of the trust paradox. Because the demonstrations are conditional on that premise and the paper does not claim stronger auditability, the load-bearing condition is acknowledged rather than hidden. No adjustment to ACCEPT is warranted.","tokens_in":1794,"tokens_out":245,"duration_ms":14462,"concrete_test":"Reproduce the inflation experiments on the three auditing frameworks using the exact provider-report generation procedures described; confirm whether the 1,469% and 50.85% figures are recovered when the auditor applies only the published consistency checks.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is that token-count audits reduce to consistency checks on provider-controlled artifacts (model, tokenizer, execution trace), enabling systematic inflation (1,469% hidden reasoning, 50.85% via tokenization ambiguity). This trust paradox is stated explicitly as the premise rather than an unexamined assumption, and the reported inflation figures are presented as direct consequences of that premise within the three studied frameworks. No internal inconsistency or unsupported leap appears in the argument structure.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript claims that per-token billing for commercial LLMs is difficult to audit by design because providers hide the model, tokenizer, and execution trace to protect IP, mitigate jailbreaks, and preserve privacy. This reduces any audit to a consistency check on provider-supplied reports, creating a 'trust paradox' in which the artifacts an auditor must trust are precisely those a provider has incentive to manipulate. Through empirical examination of three recent token auditing frameworks, the authors demonstrate that a provider with ordinary commercial capabilities can systematically inflate billed token counts, reaching 1,469% average inflation for hidden reasoning usage in the most permissive setting (turning a $100 honest bill into roughly $1,569) and 50.85% over-reporting via tokenization ambiguity even when the full reasoning string is visible. The paper concludes that restoring honest billing requires verification mechanisms independent of the provider, such as trusted execution attestation, cryptographic proofs of inference, or third-party re-execution.","tokens_in":1862,"tokens_out":486,"duration_ms":27099,"significance":"If the empirical results hold, the work identifies a systemic and previously under-examined vulnerability in the dominant per-token pricing model for LLMs, with direct financial consequences at frontier reasoning prices. The concrete inflation percentages derived from existing frameworks, the explicit framing of the trust paradox as a premise rather than an unexamined assumption, and the call for provider-independent verification constitute a clear, falsifiable contribution. The empirical focus on three frameworks supplies reproducible evidence of the problem's generality rather than framework-specific flaws.","major_comments":[],"minor_comments":[{"comment":"The abstract states that three frameworks were evaluated but does not name them or provide citations; the introduction should explicitly identify the frameworks and their original references to allow readers to locate the baseline implementations.","section":"Abstract"},{"comment":"The 1,469% and 50.85% figures are presented as averages or thresholds; adding the number of queries or samples underlying each figure (and any variance) would improve interpretability of the reported inflation rates.","section":null},{"comment":"The manuscript would benefit from a short table summarizing the three frameworks, their audit mechanisms, and the specific inflation vectors demonstrated for each.","section":null}],"recommendation":"accept","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their positive assessment of the manuscript and recommendation to accept. The provided summary accurately captures the core claims, empirical results on token inflation, and the framing of the trust paradox.","responses":[],"tokens_in":1391,"tokens_out":58,"duration_ms":7065,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core finding is that token-count audits for commercial LLMs reduce to consistency checks on artifacts the provider controls, so a provider can inflate billed usage without detection. In the most open setting they tested, hidden reasoning adds 1,469% on average; even with visible reasoning, tokenization choices allow 50.85% over-reporting below the threshold. At frontier prices this turns a $100 honest charge into roughly $1,569.\n\nThe paper does the useful work of naming the trust paradox explicitly and then measuring its effect on three recent auditing frameworks. The numbers are tied to real pricing, which makes the economic stake clear. The argument does not rely on new math or fitted parameters; it follows from the standard fact that providers hide the model, tokenizer, and trace for IP and safety reasons.\n\nThe main soft spot is that the abstract gives the headline percentages without the full experimental setup, so a reader cannot yet judge how the inflation was produced or how representative the three frameworks are. That is a normal gap at this stage rather than a flaw in the logic.\n\nThe work is aimed at researchers who care about verifiable inference and the economics of LLM services. It is worth sending to peer review because the central claim is grounded in the actual constraints of deployed systems and the reported effect sizes are large enough to matter.","headline":"The paper shows providers can inflate LLM token bills by large margins because audits depend entirely on provider-controlled reports, with concrete numbers from three frameworks.","tokens_in":2348,"tokens_out":342,"would_cite":true,"duration_ms":20000,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"LLM providers can inflate billed token counts by 1469 percent on average without detection by current audits.","keywords":["token inflation","LLM billing","provider honesty","audit security","reasoning tokens","tokenization ambiguity","trust paradox"],"falsifier":"An independent party obtains the input and output strings, applies a known tokenizer to them, and finds that the resulting token count differs from the provider's reported count by more than the detection threshold.","tokens_in":2694,"feed_emoji":"💰","tokens_out":670,"duration_ms":24718,"temperature":0.7,"pith_summary":"Per-token billing for large language models is difficult to audit because providers hide the model, tokenizer, and execution details to protect IP and privacy. This forces any audit to reduce to a consistency check on the provider's own reports, creating a trust paradox. The paper demonstrates that a provider with ordinary capabilities can over-report hidden reasoning tokens by 1469 percent on average, turning a $100 honest bill into roughly $1,569 at frontier prices. Even when reasoning strings are visible to the user, tokenization ambiguity alone permits 50.85 percent over-reporting below detection thresholds. Honest billing therefore requires verification mechanisms tied to evidence outside the provider's control.","feed_headline":"Providers can overcharge LLM users by 1469% on token counts","feed_subtitle":"Audits that trust only provider reports allow hidden inflation even at frontier reasoning prices.","key_machinery":"The trust paradox, in which every audit must trust some artifact but current frameworks trust exactly the ones a provider has the strongest reason to manipulate.","core_discovery":"A provider with ordinary commercial capabilities can systematically inflate billed token counts because the audit reduces to a consistency check on the provider's own reports. In the most permissive setting, hidden reasoning usage can be inflated by 1,469 percent on average without detection. At current frontier reasoning prices, that turns a $100 honest bill into roughly a $1,569 bill on the same query. Even when the user can see the full reasoning string, tokenization ambiguity alone still allows 50.85 percent over-reporting below the detection threshold. These results suggest the problem is not in any specific auditor but in any audit whose evidence comes from the audited party.","pith_inferences":["Users may start demanding local token counters as a cross-check before accepting provider bills.","Market incentives could shift toward providers that voluntarily expose tokenization details.","The same reporting dependency could affect billing accuracy in other metered AI services beyond LLMs."],"forward_implications":["Existing token auditing frameworks cannot prevent systematic over-reporting of billed usage.","Restoring honest billing requires verification that ties reported token counts to evidence the provider does not control.","Trusted execution attestation, cryptographic proofs of inference, or third-party re-execution become necessary.","The vulnerability is inherent to any audit whose evidence originates from the audited party."],"fun_headline_variants":["Providers inflate LLM token counts by 1469% undetected","Hidden reasoning enables 1469% token bill inflation in commercial LLMs","LLM audits based on provider reports permit 1469% token overbilling","Tokenization ambiguity allows 50.85% undetected overreporting in LLMs","The LLM billing trust paradox leads to systematic token count inflation"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Auditors have no independent access to the model, tokenizer, or execution and must rely entirely on reports supplied by the provider.","fun_headline_variants_meta":{"raw":{"variants":["Providers inflate LLM token counts by 1469% undetected","Hidden reasoning enables 1469% token bill inflation in commercial LLMs","LLM audits based on provider reports permit 1469% token overbilling","Tokenization ambiguity allows 50.85% undetected overreporting in LLMs","The LLM billing trust paradox leads to systematic token count inflation"]},"model":"grok-4.3","cost_usd":0.005884,"raw_usage":{"total_tokens":2763,"prompt_tokens":765,"num_sources_used":0,"completion_tokens":90,"cost_in_usd_ticks":58840500,"prompt_tokens_details":{"text_tokens":765,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1908,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":765,"tokens_out":90,"duration_ms":18806,"temperature":1.0,"reasoning_tokens":1908,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T06:54:31.899205+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"An independent party obtains the input and output strings, applies a known tokenizer to them, and finds that the resulting token count differs from the provider's reported count by more than the detection threshold.","supporting_citations":[],"review_version":1}