{"id":"209a55bf-0405-439c-a207-dc282b1ffc67","arxiv_id":"2606.17116","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":1,"one_line_summary":"Defines rotation time as a crypto agility metric, derives its tolerance from risk tolerance via an approximation, and uses CVE data to illustrate values of hours to days.","lead":"This paper introduces rotation time as a measure of crypto agility for quantum-threatened systems and derives an approximation linking its tolerance to security risk tolerance. Using historical CVE data it estimates required rotation times of hours to days.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Classical CVE patch-time statistics may not map to quantum cryptanalytic breaks","rationale":"The reader already flagged the classical-to-quantum extrapolation as the weakest assumption; the full-text inspection confirms that this assumption is load-bearing for the headline numerical claim and is not supported by additional evidence inside the manuscript.","tokens_in":1705,"tokens_out":312,"duration_ms":19116,"concrete_test":"Extract the exact functional form and parameter values of the rotation-time approximation from §3 (or wherever it appears), recompute the illustrative values using only post-2015 CVEs that involve public-key algorithm weaknesses rather than implementation bugs, and check whether the resulting order-of-magnitude range remains hours-to-days or shifts by more than an order of magnitude.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim rests on using historical CVE data to compute illustrative rotation-time tolerances of hours to days, then asserting that this demonstrates crypto-agility + hybrid encryption meets organisational risk tolerances. Classical CVEs predominantly record implementation bugs, configuration errors and side-channel issues whose discovery and remediation times follow a particular empirical distribution. Quantum-enabled breaks (Shor on RSA/ECC, Grover on symmetric primitives) are algorithmic and deterministic once a sufficiently large machine exists; they do not arise from the same stochastic process. The paper’s approximation therefore implicitly treats the two classes of threat as interchangeable for the purpose of setting rotation-time bounds. If that interchangeability fails, the derived numerical tolerances lose their claimed relevance to quantum risk.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces the concept of rotation time as a measure of crypto agility, derives an approximation linking rotation time tolerance to security risk tolerance, and uses historical CVE data to calculate illustrative values on the order of hours to days. It concludes that crypto agility combined with hybrid encryption is an effective approach for quantum-resilient systems, though it may require challenging technical and operational tolerances.","tokens_in":1875,"tokens_out":370,"duration_ms":37895,"significance":"If the approximation is sound and the CVE mapping holds, the work supplies a quantitative framework for deriving crypto-agility tolerances against quantum threats, addressing an explicit gap in the literature on system design requirements. The concrete illustrative numbers from CVE data add practical value for assessing organisational risk.","major_comments":[{"comment":"Abstract: the manuscript asserts a derivation of an approximation that links rotation time tolerance directly to security risk tolerance, yet supplies no equations, derivation steps, or explicit formula, preventing verification of whether the result is independent or whether risk tolerance is effectively defined in terms of the rotation time being quantified.","section":"Abstract"},{"comment":"CVE data section: historical CVE statistics, which predominantly record implementation bugs, configuration errors and side-channel issues, are used to compute rotation-time tolerances for quantum-enabled cryptanalysis; no justification is given for why the empirical distribution of classical vulnerability remediation times applies to deterministic algorithmic breaks such as Shor on RSA/ECC once a large quantum machine exists.","section":"CVE-based calculation"}],"minor_comments":[{"comment":"Abstract: a short statement of the key assumptions underlying the approximation would improve readability without altering the central claim.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the detailed and constructive report. We address each major comment below, indicating planned revisions where appropriate.","responses":[{"response":"The abstract is a concise summary and does not include equations for readability. The full derivation of the approximation, including the explicit formula and steps linking rotation time tolerance to security risk tolerance, appears in Section 3 of the manuscript. To address the concern, we will revise the abstract to state the key formula explicitly.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the manuscript asserts a derivation of an approximation that links rotation time tolerance directly to security risk tolerance, yet supplies no equations, derivation steps, or explicit formula, preventing verification of whether the result is independent or whether risk tolerance is effectively defined in terms of the rotation time being quantified."},{"response":"We agree that CVE data reflects classical issues and that quantum breaks differ in nature. The data is used strictly as an empirical illustration of observed cryptographic update timescales in deployed systems to produce concrete benchmark values. We will add a limitations paragraph clarifying the proxy nature of the mapping and the assumptions involved.","revision_made":"yes","referee_comment":"[CVE-based calculation] CVE data section: historical CVE statistics, which predominantly record implementation bugs, configuration errors and side-channel issues, are used to compute rotation-time tolerances for quantum-enabled cryptanalysis; no justification is given for why the empirical distribution of classical vulnerability remediation times applies to deterministic algorithmic breaks such as Shor on RSA/ECC once a large quantum machine exists."}],"tokens_in":1278,"tokens_out":345,"duration_ms":42662,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main things to know are that the paper defines rotation time as a crypto agility metric and derives an approximation from CVE data suggesting tolerances of hours to days for quantum-resilient systems.\n\nIt is new in this specific framing and linkage. The paper does a reasonable job of making the case that crypto agility combined with hybrids could address quantum risks if operational requirements are met. Credit to the authors for trying to turn a qualitative concept into something with numbers.\n\nThe soft spot is the reliance on classical CVE statistics for setting those tolerances. Those data reflect a different kind of threat process than the algorithmic breaks expected from quantum computers, so the mapping needs stronger justification than the abstract provides. Without the explicit formula it is also hard to judge if the approximation introduces circularity or depends on unstated parameters.\n\nThis work is for practitioners who need quantitative guidance on migration timelines rather than pure theorists. A serious referee could help clarify the assumptions and check the derivation against the stress test concern about threat types.\n\nI recommend sending it for peer review to get feedback on the threat model alignment and whether the numerical claims survive scrutiny.","headline":"The paper defines rotation time as a crypto agility metric and approximates it from CVE data, but the classical-to-quantum threat mapping is the main weakness.","tokens_in":2350,"tokens_out":300,"would_cite":false,"duration_ms":39860,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Rotation time measures crypto agility by approximating how quickly keys must rotate to match an organization's security risk tolerance.","keywords":["crypto agility","rotation time","quantum risk","key rotation","hybrid encryption","CVE analysis","security tolerance","quantum resilience"],"falsifier":"A calculation or dataset showing that quantum threat timelines require rotation times differing by an order of magnitude from the hours-to-days range derived from CVE data would invalidate the approximation.","tokens_in":2595,"feed_emoji":"🔐","tokens_out":564,"duration_ms":28424,"temperature":0.7,"pith_summary":"The paper introduces rotation time as a metric for how fast a system can switch cryptographic algorithms when facing quantum threats. It derives an approximation that ties this rotation tolerance directly to the level of risk an organization is willing to accept. Historical records of classical software vulnerabilities are then used to produce concrete example values. These examples fall in the range of hours to days, showing that hybrid encryption plus agility can address quantum risk but only if operational speeds meet those tight windows.","feed_headline":"Rotation time links crypto agility to risk tolerance in hours to days","feed_subtitle":"An approximation derived from historical vulnerability data gives concrete targets for how fast systems must update encryption against quant","key_machinery":"Rotation time, the period required to rotate cryptographic algorithms or keys, serves as the central measure that converts risk tolerance into an operational agility target.","core_discovery":"Rotation time is defined as the interval in which cryptographic primitives must be updated to keep risk within bounds; an approximation relates this interval to security risk tolerance, and calculations from CVE data place acceptable rotation times at the order of hours to days for typical organizational risk levels.","pith_inferences":["Rotation time could be adapted as a general metric for agility against any future class of cryptanalytic advance.","Standards bodies might adopt rotation-time targets as a compliance check for quantum-ready systems.","Empirical measurement of actual rotation performance in deployed systems would allow direct comparison against the CVE-derived tolerances."],"forward_implications":["Hybrid encryption schemes become viable for quantum resilience only when systems achieve rotation times within the derived tolerance window.","Security architectures must incorporate rapid algorithm-update mechanisms to stay inside organizational risk limits.","Operational processes for key and algorithm management face strict time constraints of hours to days.","The approximation supplies a quantitative target that can be used to evaluate whether a given system's agility meets risk goals."],"fun_headline_variants":["Rotation time links quantum risk to crypto updates in hours to days","CVE data reveals crypto rotation time tolerance of hours to days","Quantum threat mitigation via crypto agility measured in hours to days","Crypto agility risk tied to rotation time from vulnerability history"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Historical patterns of classical vulnerabilities can be used to set rotation tolerances that will apply to future quantum attacks.","fun_headline_variants_meta":{"raw":{"variants":["Rotation time links quantum risk to crypto updates in hours to days","CVE data reveals crypto rotation time tolerance of hours to days","Quantum threat mitigation via crypto agility measured in hours to days","Crypto agility risk tied to rotation time from vulnerability history"]},"model":"grok-4.3","cost_usd":0.00455,"raw_usage":{"total_tokens":2229,"prompt_tokens":603,"num_sources_used":0,"completion_tokens":65,"cost_in_usd_ticks":45499500,"prompt_tokens_details":{"text_tokens":603,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1561,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":603,"tokens_out":65,"duration_ms":17418,"temperature":1.0,"reasoning_tokens":1561,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T03:32:32.741822+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A calculation or dataset showing that quantum threat timelines require rotation times differing by an order of magnitude from the hours-to-days range derived from CVE data would invalidate the approximation.","supporting_citations":[],"review_version":1}