John Doe <johndoe@gmail.com>
FinCEN Enforcement: Paxful, Inc. and Paxful USA, Inc.
regbrief@gmail.com <regbrief@gmail.com> Wed, Dec 10, 2025 at 6:19 PM
To: johndoe@gmail.com
FinCEN

Paxful, Inc. and Paxful USA, Inc.

Crypto / Virtual Asset Service Provider (VASP)
Penalty
$3,500,000
Violation Period
2015-02-03 – 2023-04-04

Executive Summary

  • “What happened?” — FinCEN found Paxful willfully violated the BSA (Feb 2015–Apr 2023) by operating as an unregistered MSB, lacking effective AML/KYC/monitoring, and failing to file SARs while facilitating extensive illicit activity; FinCEN assessed a $3.5M civil money penalty (with $1.75M credited to a DOJ payment).
  • “Why it happened (Root cause)?” — Governance and cultural failures: leadership deprioritized compliance, management complicity instructed staff not to file SARs, an unqualified compliance lead, lapsed MSB registration, and inadequate monitoring, sanctions, and data controls enabled evasion and structuring.
  • “What you can do for your institution” — Implement the roadmap: mandatory risk‑based KYC/EDD and freeze high‑risk accounts; appoint a credentialed BSA/AML officer and governance committee; deploy enterprise monitoring across tokens and prepaid rails, IP geofencing, real‑time sanctions screening, SAR decision standards, independent AML testing, license inventory, and a centralized compliance data layer. Apply AI: graph ML for mixer/darknet detection, ML/LLM for alert triage and SAR drafting, and predictive customer risk scoring.

What the Order Says

FinCEN found that Paxful, a P2P virtual asset exchange and hosted wallet provider, willfully violated the BSA by operating as an unregistered money services business, failing to implement an effective AML program, and failing to file required suspicious activity reports. From 2015 to 2019, Paxful had virtually no KYC, transaction monitoring, or SAR program and then implemented only partial, easily circumvented controls, allowing extensive illicit use including exposure to North Korea and Iran, ransomware, darknet markets, CSAM platforms, unregistered mixers, terrorist financing, and large fraud schemes. FinCEN classified the violations as egregious, citing systemic, culture‑driven noncompliance, management complicity, and significant harm to law enforcement and national security objectives. Mitigating factors included leadership and ownership changes in 2023, subsequent engagement of independent consultants, remediation efforts, and cooperation with FinCEN during the investigation. FinCEN imposed a $3.5 million civil money penalty, crediting $1.75 million against a parallel $4 million DOJ resolution so that Paxful will pay $1.75 million to Treasury, and did not treat the matter as a voluntary self‑disclosure.

Penalty Determination
Base Penalty $3,500,000
Final Penalty $3,500,000 (FinCEN assessed a $3.5M civil money penalty and credited $1.75M against Paxful’s $4M payment to DOJ, leaving $1.75M payable to Treasury under this order.)
Self-Disclosure No
Classification egregious
Aggravating Factors
  • Systemic, multi‑year failure to implement any meaningful AML program, KYC, or SAR processes despite known BSA obligations
  • Operation as an unregistered MSB for 974 days while continuing to transmit funds
  • Facilitation of substantial illicit activity including sex trafficking and CSAM marketplaces, ransomware, darknet markets, sanctions evasion, terrorist financing, and large‑scale fraud schemes
  • Management complicity and culture of non‑compliance, including C‑suite solicitation of high‑risk business (e.g., Backpage, MMM) and instruction not to file SARs
  • Significant detrimental impact on FinCEN’s mission and withholding of critical reporting related to national security priorities (DPRK, Iran, terrorism, ransomware, CSAM)
  • Prolonged duration of violations and structural nature of control failures across all business lines and products
  • Financial benefit and growth partly fueled by servicing high‑risk and illicit customers without appropriate AML controls
Mitigating Factors
  • Change of ownership and senior management in April 2023, removing those in place during the violation period
  • Engagement of independent consultants post‑April 2023 to review historical activity and file thousands of backlogged SARs
  • Implementation of some AML controls beginning in 2019 and addition of blockchain analytics in 2020, albeit belated and incomplete
  • Cooperation with FinCEN, including complete and timely productions, tolling the statute of limitations, and providing ongoing remediation updates
  • No prior criminal, civil, or regulatory enforcement actions against Paxful before this matter
Related Regulatory Actions
Paxful, Inc. and Paxful USA, Inc.
DOJ • Civil/Criminal Resolution
Paxful agreed to pay $4 million to the Department of Justice in a separate but parallel investigation; FinCEN credited $1.75 million of that payment against its $3.5 million civil money penalty.
Larry Dean Harmon d/b/a Helix
FinCEN • Civil Money Penalty • 2020-10-19
FinCEN imposed a $60 million penalty for BSA violations involving the Helix CVC mixing service; Paxful transacted over $35 million equivalent with Helix and other unregistered mixers without filing SARs.
Artur Schaback (former Paxful CTO)
DOJ • Criminal Plea Agreement • 2024-07-08
Former Paxful CTO admitted allowing Paxful’s MSB registration to lapse while continuing to operate as a money transmitter (referenced in Schaback plea agreement 2:24-cr-00072-KJM).

Findings by Domain

kyc_onboarding high
No KYC program or written AML policies until 2019, despite billions in CVC and prepaid card activity.
Root Cause
Governance Failure: Leadership deprioritized compliance to accelerate growth and revenue.
Impact
High‑risk customers (e.g., Backpage, CSAM, fraud schemes) operated anonymously, enabling large‑scale illicit use of the platform.
Key Evidence
No KYC before Feb 2019; $3.5B CVC pre‑KYC; 4M Backpage‑related transactions with no SARs.
kyc_onboarding high
Post‑2019 KYC applied only above $1,500 thresholds, with no structuring controls or holistic user linkage.
Root Cause
Process Design Flaw: Threshold‑based KYC not aligned to P2P CVC risks or evasion typologies.
Impact
Users structured activity to avoid verification, and migrated from competitors to exploit Paxful’s weaker KYC.
Key Evidence
Mandatory KYC only for activity exceeding $1,500; social media promotions to users fleeing stricter exchanges.
kyc_onboarding high
No effective process to identify or control unregistered MSBs and P2P exchangers using the platform.
Root Cause
Information Siloing: Written requirement to collect MSB registrations not operationalized or monitored.
Impact
Platform used by unregistered exchangers and mixers for large‑scale money transmission without licensing or SARs.
Key Evidence
Policy to collect MSB registrations existed but not implemented; known risk of smaller P2P exchangers ignored.
kyc_onboarding high
Improper designation of unqualified CEO as BSA/AML compliance officer with no AML training.
Root Cause
Governance Failure: No separation of business and compliance; role assigned nominally without expertise.
Impact
Allowed registration lapse, absent KYC, no SAR filings, and unchecked high‑risk business relationships.
Key Evidence
CEO listed as CCO through 2018 with no BSA/AML training; no SARs filed before Nov 2019.
transaction_monitoring high
No formal transaction monitoring or written procedures until July 2019, four years after launch.
Root Cause
Insufficient Technology: Lack of monitoring systems combined with management disregard for staff warnings.
Impact
Hundreds of millions in suspicious activity across CVC, fiat, and prepaid cards went unreviewed and unreported.
Key Evidence
Minimal monitoring only from 2018; written procedures from July 2019; prior transactions not reviewed.
transaction_monitoring high
Monitoring failed to cover key products and CVCs (e.g., prepaid access, Dogecoin, Ripple, Ethereum, Tron, Tether).
Root Cause
Process Design Flaw: Incomplete coverage and risk assessment across products and supported tokens.
Impact
Inability to detect suspicious activity in more than 15 CVCs and major prepaid card flows despite known risks.
Key Evidence
Blockchain analytics acquired in 2020 omitted several CVCs; prepaid access monitoring gaps acknowledged until 2023.
transaction_monitoring high
No effective monitoring of prepaid access despite it being a dominant payment method.
Root Cause
Cultural/Tone Issues: Prepaid growth prioritized over addressing recognized high fraud and exploitation risks.
Impact
Over $1.7B (2015–2019) in iTunes/Amazon cards and $20M/week in 2020 processed with minimal scrutiny.
Key Evidence
Management comments about 'scammed iTunes cards'; prepaid trades >50% of bitcoin volume in 2020.
transaction_monitoring high
Staff red flags and law enforcement inquiries not translated into enhanced monitoring or customer reviews.
Root Cause
Governance Failure: Escalations disregarded; no feedback loop from investigations to control design.
Impact
Continued transactions with Lazarus‑linked user and other high‑risk entities even after LE and OFAC signals.
Key Evidence
LE inquiries on Tian Dec 2018 and Oct 2019; action only in May 2020 after OFAC attribute listing.
transaction_monitoring high
Inadequate controls to detect geo‑spoofing and high‑risk jurisdiction access using IP/VPN analytics.
Root Cause
Insufficient Technology: No IP‑based geofencing or anomaly detection for VPN‑masked activity.
Impact
Accounts from Iran, Syria, Cuba, Crimea, and Sudan and North Korea‑linked trades processed without review or blocking.
Key Evidence
1,500+ accounts with sanctioned‑country IPs (2015–2018); explicit BTC–PayPal in North Korean won trades unflagged.
sanctions_screening high
Failure to prevent or react promptly to transactions involving sanctioned jurisdictions and SDN‑linked actors.
Root Cause
Process Design Flaw: No integrated sanctions risk controls across P2P marketplace and hosted wallets.
Impact
Transactions with Lazarus‑linked Tian, EnExchanger, Iranvisacart, DPRK‑related trades, and Venezuelan Petro without SARs.
Key Evidence
Dozens of transactions with EnExchanger/Iranvisacart; Tian’s address on OFAC SDN in March 2020; no timely action.
sanctions_screening high
No systematic geo‑IP sanctions/geofencing controls until at least 2018; ineffective thereafter due to VPN evasion.
Root Cause
Insufficient Technology: Lack of robust sanctions geolocation analytics and VPN detection.
Impact
Extensive interaction with users in Iran, Syria, Cuba, Crimea, Sudan, and DPRK‑linked flows without appropriate risk treatment.
Key Evidence
Over 1,500 accounts with Iranian/Syrian/Cuban/Crimean/Sudanese IPs; clear North Korean won trades with rapid jurisdiction switching.
investigations_reporting high
No SARs filed at all until November 2019 despite years of clearly suspicious activity.
Root Cause
Cultural/Tone Issues: Leadership instructed staff not to file SARs and resisted improving reporting.
Impact
Hundreds of required SARs on high‑risk activity were missed, depriving law enforcement of critical intelligence.
Key Evidence
Order states leadership refused to improve SAR reporting and instructed employees not to file; zero SARs until Nov 2019.
investigations_reporting high
Post‑2019 SAR program remained untimely and incomplete, with backlog of historical suspicious activity.
Root Cause
Resource Constraints: Inadequate staffing and tooling for SAR review relative to alert volume and historical lookbacks.
Impact
Delayed and inaccurate SARs on ransomware, CSAM, darknet markets, mixers, terrorist financing, and fraud schemes.
Key Evidence
FinCEN identified hundreds of missed SARs; independent consultants later filed thousands of retroactive SARs.
investigations_reporting high
Law enforcement inquiries did not consistently trigger investigations, SARs, or account restrictions.
Root Cause
Governance Failure: No formal escalation protocol linking LE contact to mandatory SAR review and enhanced due diligence.
Impact
Continued activity by high‑risk users even after direct LE outreach, compounding exposure.
Key Evidence
LE inquiries regarding Tian in 2018 and 2019; Paxful took action only in May 2020.
investigations_reporting medium
Only a single independent AML test conducted during an eight‑year period, despite high risk profile.
Root Cause
Governance Failure: Lack of board/senior oversight to mandate periodic independent reviews.
Impact
Structural program weaknesses and SAR failures persisted undetected and unremediated.
Key Evidence
Order notes one independent review only, not commensurate with transaction volume or risk.
other medium
MSB registration allowed to lapse while continuing to operate as a money transmitter for 974 days.
Root Cause
Governance Failure: No regulatory licensing oversight or renewal calendar; CTO admitted knowing lapse.
Impact
Unregistered MSB operations exposed Paxful and customers to legal and regulatory risk and undercut regulatory visibility.
Key Evidence
Initial registration July 27, 2015; required renewal by Dec 31, 2016; renewed only Sept 3, 2019.
other medium
Data governance deficiencies, including lack of systematic use of IP, device, and blockchain data for risk decisions.
Root Cause
Data Quality Issues: Fragmented data with limited integration into monitoring and onboarding controls.
Impact
Available data (usernames, emails, IPs, wallet links) not leveraged to detect Backpage, CSAM, or sanctions risks.
Key Evidence
Records contained Backpage‑related details and high‑risk IPs but no KYC or monitoring response.

Solution Roadmap

kyc_onboarding
No or inadequate KYC for P2P CVC and prepaid card users; inability to identify high‑risk and unregistered MSB customers.
Immediate Fix
Implement mandatory, risk‑based KYC for all new users at onboarding (ID, liveness, sanctions/PEP screening) and freeze high‑risk existing accounts pending verification.
Tactical Solution
Deploy a tiered, global CDD/EDD framework specific to P2P and prepaid risks (e.g., enhanced checks for high‑volume traders, gift‑card heavy flows, high‑risk geographies, and potential unregistered MSBs).
Strategic Transformation
Build an enterprise‑wide digital identity and customer risk‑rating platform integrating KYC, behavioral data, device/geo data, and blockchain analytics for continuous risk re‑scoring.
Success Metrics
100% of active users have verified identities appropriate to risk tier; ≥95% of high‑risk users assigned high‑risk rating and subject to EDD; Measured reduction in unidentified high‑risk counterparties (e.g., darknet, CSAM links) over 12–18 months; Zero days of lapsed MSB or equivalent licensing in relevant jurisdictions
kyc_onboarding
Unqualified compliance leadership and weak AML governance structure.
Immediate Fix
Appoint an experienced BSA/AML officer with direct board reporting and issue a board‑approved AML and sanctions governance charter.
Tactical Solution
Establish a compliance risk committee (compliance, product, tech, legal) with defined RACI, meeting cadence, and formal escalation procedures for high‑risk issues.
Strategic Transformation
Integrate AML and sanctions risk into enterprise risk management with board‑level KRIs, formal risk appetite statements, and linkage to executive compensation.
Success Metrics
Compliance officer role filled with credentialed AML professional within defined timeframe; Quarterly board compliance reports delivered and minuted; Closure of >90% of audit and regulatory findings within agreed SLAs; Positive independent review opinion on AML governance within 24 months
transaction_monitoring
Lack of comprehensive monitoring coverage across all CVCs, prepaid access, and P2P patterns.
Immediate Fix
Stand up basic rules‑based monitoring across all supported assets and payment methods and implement temporary volume thresholds for high‑risk segments pending full tuning.
Tactical Solution
Deploy or upgrade a monitoring platform with CVC‑specific typologies (ransomware, mixers, darknet, CSAM, fraud, sanctions evasion) using blockchain analytics and internal behavioral data.
Strategic Transformation
Move to a unified, risk‑based case management and monitoring ecosystem enabling cross‑product views, scenario analytics, and periodic model/rule validation aligned with VASP best practices.
Success Metrics
100% of transaction value in all supported tokens and payment rails covered by monitoring; Documented library of typologies with mapped scenarios and annual review; Year‑on‑year decrease in confirmed missed SAR‑worthy events from internal QA; Independent model validation with no high‑severity issues
transaction_monitoring
Inability to detect geo‑spoofing and high‑risk jurisdiction access.
Immediate Fix
Implement IP‑based geofencing, block logins and trading from sanctioned jurisdictions, and flag VPN/proxy usage for enhanced review.
Tactical Solution
Integrate device fingerprinting, IP reputation, and geo‑anomaly detection into monitoring and KYC, with tailored rules for jurisdiction hopping and currency/geography mismatches.
Strategic Transformation
Adopt continuous behavioral analytics capable of dynamic risk adjustments for customers exhibiting geo‑spoofing patterns or accessing from emerging high‑risk regions.
Success Metrics
0 successful logins or trades from comprehensively sanctioned jurisdictions absent specific licenses; Detection and review of ≥95% of VPN/proxy logins associated with high‑risk behavior; Reduction in unidentified high‑risk jurisdiction activity over time
sanctions_screening
Insufficient sanctions controls for CVC flows and high‑risk jurisdictions.
Immediate Fix
Implement real‑time sanctions list screening for all customers, counterparties, and on‑chain addresses using up‑to‑date OFAC and other lists; freeze and investigate positive matches.
Tactical Solution
Define and enforce sanctions risk policy for VASP interactions (e.g., Iran/Venezuela exchanges, Petro), including counterparty whitelisting/blacklisting and enhanced review for high‑risk corridors.
Strategic Transformation
Integrate sanctions screening with blockchain analytics to continuously scan address clusters, ensure 50%‑rule style ownership logic, and align with evolving sanctions expectations for VASPs.
Success Metrics
100% of new and existing customers screened against updated sanctions lists at required frequencies; Zero known transactions with SDN‑linked or comprehensively sanctioned counterparties absent OFAC authorization; Timely documented investigation and disposition of all potential sanctions alerts
investigations_reporting
SARs not filed or significantly delayed; poor investigation workflows and escalation.
Immediate Fix
Publish SAR decision standards and timelines; immediately establish an investigations team and SAR committee with clear SLAs (e.g., 30 days from detection).
Tactical Solution
Implement an investigations case management system linking alerts, customer data, blockchain analytics, and evidence, with QA reviews and SAR narrative templates tailored to CVC typologies.
Strategic Transformation
Develop a mature FIU function with thematic reviews, typology feedback into monitoring design, and regular law‑enforcement outreach, coordinated with sanctions and fraud teams.
Success Metrics
≥95% of SARs filed within regulatory timeframes; QA error rate on SAR filings below defined threshold (e.g., <5% material errors); Reduction in repeat SARs on same root‑cause risk without remediation; Positive regulator and independent reviewer feedback on SAR quality
investigations_reporting
Infrequent and inadequate independent AML testing.
Immediate Fix
Engage an external audit or consulting firm to perform a comprehensive AML and sanctions program review within the next cycle and remediate high‑risk findings quickly.
Tactical Solution
Establish a 12–18 month independent testing schedule covering all program pillars, with agreed scope, methodology, and formal issue‑tracking and remediation processes.
Strategic Transformation
Integrate AML testing into an enterprise assurance model (first/second/third line) with continuous controls monitoring and risk‑based scoping driven by metrics and incidents.
Success Metrics
Completion of independent AML review with remediation plan approved by the board; Closure of all high‑severity findings within agreed timelines; Demonstrable reduction in repeat or long‑aging findings across test cycles
other
MSB registration lapse and weak regulatory license management.
Immediate Fix
Create and populate a license and registration inventory with renewal dates, owners, and automated reminders; assign responsibility within compliance/legal.
Tactical Solution
Implement a GRC or licensing management tool tracking MSB and analogous registrations across jurisdictions, linking to product rollouts and marketing approvals.
Strategic Transformation
Embed licensing impact assessments into strategic planning and product governance to ensure no operations commence or continue without required registrations.
Success Metrics
Zero missed or late license/MSB renewals; All new products and jurisdictions with documented licensing analysis before launch; Positive licensing compliance attestations in internal and external reviews
other
Poor data integration and governance for AML, sanctions, and investigations.
Immediate Fix
Identify and map critical AML data sources (KYC, transactions, IPs, blockchain analytics) and implement interim ETL or reporting to support investigations and remediation lookbacks.
Tactical Solution
Build a centralized compliance data mart or lake with standardized schemas, data quality checks, and lineage documentation accessible to AML and sanctions tools.
Strategic Transformation
Develop an enterprise data governance framework with data ownership, quality metrics, cataloging, and privacy/security controls aligned to regulatory expectations for VASPs.
Success Metrics
Data completeness and accuracy metrics meeting defined thresholds for all key AML data domains; Reduction in manual data gathering time per investigation; Ability to perform comprehensive historical lookbacks and typology analysis without major data gaps

AI Opportunities

transaction_monitoring medium
Gap Addressed
Inability to effectively detect complex patterns such as mixers, darknet, CSAM, ransomware, and cross‑jurisdictional P2P abuse.
Use Case
Pattern Recognition for CVC and P2P network anomalies (e.g., clustering, mixer detection, typology‑specific behavior).
Technology
ML/Graph Analytics
Expected ROI
12–24 months to measurable reduction in undetected illicit activity and improved investigative efficiency.
Expected Benefit
Improved detection of hidden networks (mixers, darknet markets, fraud rings) and reduction in undetected SAR‑worthy events with better risk segmentation.
Prerequisites
• Consolidated on‑chain and off‑chain transaction data with reliable identifiers
• Integration with blockchain analytics provider APIs
• Defined typology library and labeled historical cases for model training
• Model governance framework (validation, monitoring, documentation)
⚠ Risks
• Model risk from false negatives on high‑risk activity
• Regulatory concerns if models are opaque and not explainable
• Operational dependency on third‑party analytics and data quality
• Increased alert volume if models are not properly tuned
investigations_reporting medium
Gap Addressed
Resource‑intensive SAR investigations and narrative drafting leading to delays and inconsistent quality.
Use Case
Process Automation and Decision Support for alert triage and SAR narrative generation.
Technology
NLP/LLM + ML
Expected ROI
6–18 months to reduce investigation time per case and improve on‑time SAR filing percentages.
Expected Benefit
Faster triage of low‑risk alerts, standardized narratives, and more time for analysts to focus on complex cases, improving timeliness and consistency.
Prerequisites
• Structured case data, including alert details, KYC, and transaction histories
• Historical SARs as training or reference corpus (with proper safeguards)
• Strong human‑in‑the‑loop review to ensure analysts remain decision‑makers
• Controls to prevent inclusion of sensitive PII in external model training
⚠ Risks
• Compliance risk if staff over‑rely on AI recommendations without adequate review
• Explainability challenges for why a case is recommended as SAR/non‑SAR
• Potential data leakage or privacy concerns if cloud models are misconfigured
kyc_onboarding medium
Gap Addressed
Difficulty identifying high‑risk users at onboarding and over time, including unregistered MSBs and mule networks.
Use Case
Predictive customer risk scoring and entity resolution across identities, devices, and behavior.
Technology
ML/Graph + Entity Resolution
Expected ROI
12–18 months as fewer truly high‑risk customers slip through initial onboarding and periodic review processes.
Expected Benefit
Earlier identification of high‑risk customers (e.g., exchangers, fraud rings) and better prioritization of EDD and ongoing monitoring.
Prerequisites
• Unified customer master data (KYC, accounts, devices, IPs, payment methods)
• Labels for known high‑risk customers (e.g., LE inquiries, prior SARs)
• Clear policy on how risk scores are used and thresholds for action
• Governance to periodically review model performance and bias
⚠ Risks
• Potential bias against certain geographies or customer segments
• Regulatory scrutiny if scores are not explainable or are misused
• Customer friction if false positives trigger unnecessary escalations
sanctions_screening high
Gap Addressed
High false positives and difficulty linking related crypto addresses and entities for sanctions risk.
Use Case
Entity resolution and sanctions risk propagation across wallet clusters and counterparties.
Technology
Graph ML/Pattern Recognition
Expected ROI
18–24 months, primarily via improved sanctions risk coverage and more efficient use of sanctions investigation resources.
Expected Benefit
Improved identification of indirect exposure to sanctioned actors (e.g., OFAC‑linked clusters) with more targeted alerts and fewer benign matches.
Prerequisites
• High‑quality blockchain clustering data (internal and vendor‑provided)
• Robust sanctions reference data and ownership logic
• Legal and compliance sign‑off on risk propagation methodology
• Strong documentation and testing for model explainability
⚠ Risks
• Regulatory uncertainty around algorithmic cluster attribution for sanctions purposes
• Potential over‑blocking of legitimate activity if risk propagation is too aggressive
• Model drift as illicit actors change behaviors
other medium
Gap Addressed
Fragmented AML data and difficulty performing retrospective lookbacks and typology discovery.
Use Case
Natural Language Processing and anomaly detection for typology discovery and regulatory change monitoring.
Technology
NLP/Unsupervised ML
Expected ROI
Qualitative; improved adaptability of the AML program and earlier response to new risks over time.
Expected Benefit
Earlier detection of emerging illicit typologies in P2P CVC and prepaid markets, and faster incorporation of regulatory guidance into controls.
Prerequisites
• Centralized repository of internal cases, SARs, and alerts
• Feeds of external data (LE advisories, regulatory notices, open‑source intelligence)
• AML SME involvement to interpret model‑flagged patterns and texts
⚠ Risks
• False insights if patterns are misinterpreted without domain expertise
• Limited direct regulatory precedent on using unsupervised AI for core AML controls
• Need for strong change‑management so findings are properly vetted before operationalization

Source Documents
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Dec 10, 2025

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