Background

Threat Hunter's global intelligence network, from May 13-28, 2025, detected active campaigns involving transnational refund fraud syndicates.

These groups extensively utilized public social media and messaging platforms (Telegram, Facebook) for illicit service promotion, employing social engineering tactics (e.g., showcasing successful order IDs) to onboard non-malicious actors into their fraud-as-a-service (FaaS) ecosystem as agents or consumers of their refund methodologies.

Key Findings

• Analysis confirms that the underground industry (UI) operates via a robust transnational collaborative network, delivering services across diverse markets, including the UK, US, Germany, Canada, and France, indicative of highly evolved cross-border operational capabilities and demonstrating a clear global operational footprint.

  
  

• A notable shift in fraud methodology involves the hybridization of conventional refund techniques with sophisticated digital enablers. This includes ephemeral thermal printing for logistics labels (auto-disintegration within 24-48 hours) and AI-driven document fabrication for credential spoofing.

  
  

• Intelligence confirms the UI's mature organizational structure, characterized by a clear functional segmentation across technology development, fraudulent user recruitment, refund process orchestration, and illicit financial laundering.

  
  

• Analysis of the underground economy's monetization strategy reveals a variable-rate commission model. This typically involves a tiered commission system(e.g., 10% first order, 15% subsequent) to incentivize repeat illicit transactions.

  
  

• Analysis of 17 confirmed malicious refund events offers significant risk intelligence. These validated incidents highlight critical fraud patterns worth paying attention to.

  
  

Analyzing The Methodological Evolution of Adversaries

Tracking e-commerce refund fraud from 2023-2025, we've identified a clear evolutionary path in the underground industry (UI) methodologies. Evidencing a systematic evolution in their operational TTPs.

Legacy Refund Fraud Tactics (Pre-2023)

Our analysis of pre-2023 refund fraud cases identifies several legacy tactics that formed the foundational fraud vector:

• False Logistics Fraud: Manipulation of tracking IDs to simulate delivered returns.

• Product Substitution/Empty Box Fraud: Illicit removal of goods followed by return of the empty package, or replacement of high-value items with lower-value items.

• Fabricated Quality/Damage Claims: Submission of deceptive claims regarding product defects or incorrect shipments.

• Policy Exploitation: Systematic abuse of platform return policies for short-term product utilization and/or promotional offers.

Technical Enhancements in Refund Schemes (2024)

By 2024, malicious refund methodologies saw significant technological upgrades:

• Self-Deleting Shipping Labels: Fraudsters are using thermal printers to create logistics labels where recipient information vanishes within 1-2 days, hindering investigations.

• AI-Powered Forgeries: The emergence of AI-generated visual proof (photos/videos) to substantiate false claims of product damage or discrepancies.

• Distributed Fraud Operations: Coordinated multi-platform refund attacks on the same product to bypass individual platform risk aggregation and correlation engines.

Emerging Advanced TTPs (2025)

In 2025, highly sophisticated fraudulent TTPs are emerging:

• Fabricated Hazardous Waste Claims: After receiving the goods, fraudsters falsely claim product contamination/hazardous material leakage (e.g., battery/liquid). This tactic facilitates a refusal to return goods on safety grounds, asserting destruction as bio-hazardous waste to obstruct recovery.

• Adaptive Social Engineering Playbooks: Adversary social engineering “playscripts” are undergoing continuous optimization. This involves adaptive conversational linguistics to manipulate customer service interactions,often featuring continually refined empathetic language and optimized psychological manipulation.

  
  

• Insider-Enabled Fraud-as-a-Service (FaaS): The emergence of internal collusion models allows external fraud actors to leverage compromised platform employees (e.g., internal after-sales, customer support) for the illicit processing of cash-on-delivery (COD) refunds through privileged internal systems.

Adversary Organizational Architecture and Execution Frameworks

During the monitoring period, Threat Hunter identified a transnational refund underground industry (UI) network operating with a well-defined organizational architecture, featuring the following key attributes:

• Cross-border collaboration network: Analysis of WhatsApp number attribution reveals a robust cross-border collaboration network, primarily operated from Pakistan (+92) and Mexico (+52). This network effectively targets major e-commerce markets, including the UK, US, Germany, Canada, and France, demonstrating a significant global operational footprint.

  
  

•  Clear professional division of labor: Analysis of these groups’ operations reveals a highly specialized division of labor, encompassing dedicated functions for technical tool development, illicit customer acquisition, refund process orchestration, and illicit fund exfiltration/settlement.

  

  
  

• Brand operation: A notable trend indicates the professionalization of the underground industry (UI) through the adoption of unified brand identities and formalized service commitments (e.g., 'XXX Refund Pro,' 'Shop Return Master'). This strategy aims to enhance their perceived legitimacy and operational sophistication.

  
  

• Multi-level agent structure: Analysis confirms a hierarchical operational model, utilizing a three-level agent framework (General, Regional, Operator). This structure facilitates rapid scaling of illicit activities and broadens their geographical footprint.

Monetization & Operational Models

• Tiered Commission Structure: Threat Hunter observed a laddered commission system (e.g., an introductory 10% for the initial transaction, escalating to 15% for subsequent operations). This incentive model aims to drive user adoption and retention within their illicit services.

  
  

• Dynamic Proportionate Commission: The underground industry employs a dynamic commission structure, charging 10-30% of the commodity's payment amount. This rate is reportedly adjusted based on the perceived difficulty and monetary value of the refund attempt.

  
  

• Publicized Success Metrics: To bolster service credibility and attract new participants, these groups utilize an Order Number Disclosure System. This involves publicly displaying successful refund order IDs, acting as a form of illicit social proof.

  
  

• Geographically Targeted Campaigns: Fraudsters engage in regional precision marketing, developing customized refund methodologies that exploit specific policy variations across different e-commerce platforms and geographical jurisdictions.

Diversified Revenue Streams & Risk Management in Illicit Operations

• Diversified Revenue Streams: Beyond direct refund commissions, the underground industry (UI) expands its illicit profit generation through the sale of fraudulent methodology tutorials and the provision of premium VIP membership services. This indicates a more sophisticated and layered monetization strategy.

  
  

• Illicit Fund Recycling & Amplification: Funds acquired from successful malicious refunds are systematically recycled through the repurchase of high-value goods. This creates a circular fund flow, effectively amplifying illicit gains and complicating financial tracing.

  
  

• Mitigation of User Risk Threshold: The UI employs various risk-sharing mechanisms to lower user entry barriers and bolster confidence in their "100% refund" guarantee. This includes models such as:

  • Partial Upfront Deposit (Refundable): Collection of a partial deposit with a promise of refund in case of operational failure, de-risking the user's initial commitment.

  • Post-Success Commission: Advanced refund execution with commission collection only upon confirmed success, directly incentivizing user participation by eliminating upfront financial risk. These strategies effectively reduce the user's perceived risk threshold and facilitate broader participation in illicit refund schemes.

Case Studies & Attack Chain Dissection

Case Study 1: E-commerce Malicious Refund - German Modus Operandi

On May 19, 2025, the Threat Hunter team identified a targeted malicious refund operation specific to a particular e-commerce platform within the German region. This operation was executed by a local German cell, leveraging common refund methodologies.

Attack Chain Analysis:

1. Customer Acquisition Phase: Precision targeting of German-speaking Facebook user groups for illicit service advertisement.

2. Communication Phase: Direct engagement with prospective refund beneficiaries, initiating collaborative refund schemes.

3. Operation Phase: Execution of refund requests utilizing established, common refund vectors provided by the underground industry (UI) cell.

4. Refund Realization Phase: The customer initiates the refund process based on the UI team's instructions, culminating in a successful credit.

5. Fund Settlement Phase: Post-successful refund, the customer remits the predetermined service fee to the UI, adhering to the agreed-upon commission structure.

The UI team further enhanced perceived legitimacy by disseminating proof-of-concept (PoC) images of successful refund pages.

Case Study 2: Emerging TTP - The "Hazardous Substance Leakage" Scheme

In recent monitoring, Threat Hunter identified a novel refund vector: the "hazardous substance leakage" declaration. This technique ingeniously exploits e-commerce platforms' heightened sensitivity to safety protocols and regulatory compliance frameworks.

Technical Breakdown of Technique:

• Target Selection: Prioritization of products containing batteries, liquids, or chemical components.

• Evidence Fabrication: Utilization of harmless substances (e.g., food coloring, saline) to simulate compelling leakage anomalies.

• Safety Protocol Declaration: Notification to the platform of purported hazardous material leakage, asserting the goods have been isolated/quarantined due to safety concerns.

• Regulatory Citation: Referencing and misapplying local hazardous goods handling regulations to declare the items as disposed of and therefore non-returnable.

• Platform Expedited Remediation: Platforms typically process refund applications rapidly to mitigate security and legal liabilities, often waiving return requirements.

Platform Vulnerabilities Exploited by the Underground Industry (UI):

• Prioritization of Safety-Related Escalations: Platforms' automated and manual workflows prioritize safety complaints, bypassing standard return verification.

• Customer Service Knowledge Gaps: Front-line customer service agents lack the specialized expertise to remotely authenticate hazardous material claims.

• Logistics Refusal for Declared Hazmat: Shipping carriers consistently decline packages declared as hazardous, preventing physical returns.

• Reputation and Legal Risk Aversion: Platforms' strong inclination to resolve issues quickly to avoid negative public relations and potential regulatory/legal repercussions.

Industry Impact and Countermeasure Strategies

Impact Assessment: Malicious Refund Fallout on E-commerce Platforms

Malicious refund activities exert multi-faceted detrimental effects across the e-commerce ecosystem:

Direct Financial Exfiltration

• Merchant-Level Impact: Fraudulent refunds result in direct loss of goods (shrinkage) and payment chargebacks/double refunds, compounded by ancillary costs associated with reverse logistics and manual fraud review overhead.

• Platform-Level Impact: Under a first-party/self-employment fulfillment model, platforms bear the entirety of commodity cost and refund liabilities. Furthermore, platforms face merchant indemnification obligations and heightened exposure to legal dispute risk.

Brand Erosion & Trust Degradation

• Consumer Confidence Erosion: A high frequency of fabricated quality disputes or logistical anomaly claims directly contributes to a quantifiable decline in consumer trust in the brand and its associated vendors.

• Reputational Damage via Disinformation: The dissemination of synthetic product damage evidence across social media channels directly correlates with acute brand reputational degradation.

• Policy Friction & Customer Churn: Reactive tightening of refund policies in response to fraud inadvertently introduces friction for legitimate consumers, leading to a sub-optimal user experience and fostering a negative feedback loop that can drive customer churn.

Data Integrity Compromise & Misinformed Strategies

• Data Pollution for Business Intelligence: High-frequency, anomalous refund events inject data pollution into inventory and sales analytics, compromising the accuracy of business intelligence dashboards and leading to misguided strategic decisions.

• Inventory Miscalibration: The underground industry's (UI) tactic of bulk refunding low-value orders generates misleading signals, causing platforms to misidentify trending products and execute erroneous inventory stocking decisions.

• Algorithm Degeneration & User Experience (UX) Impairment: Anomalous refund data streams corrupt proprietary recommender algorithms, leading to sub-optimal product recommendations and a direct degradation of the overall platform UX.

Malicious Refund Defense Strategy: A Proactive Posture

Based on an in-depth analysis of evolving underground industry (UI) methodologies and organizational structures, Threat Hunter proposes the following innovative defense strategies:

Technical Defense Enhancements

• Intelligent Packaging Integrity Monitoring: Implement specialized covert markings on packaging to enable tamper detection and identify unauthorized access or product substitution.

  
  

• Biometric Signature Authentication (High-Value Goods): For high-value transactions, mandate biometric verification (e.g., facial recognition) at the point of delivery/signature to mitigate identity spoofing and theft risk.

  
  

• AI Visual Verification System: Develop a robust system requiring multi-angle video submission for returned goods. This system will utilize AI-powered comparative analysis to confirm product consistency and detect evidence fabrication.

  
  

• Blockchain Logistics Tracking: Leverage distributed ledger technology (blockchain) to record the entire logistics chain, ensuring immutable tracking data and preventing logistics information tampering.

Strategic Defense Initiatives

• Differentiated Refund Policies: Implement dynamic refund policies tailored to individual user risk profiles (credit rating, purchase history) and product categories, moving beyond a one-size-fits-all approach.

• Refund Behavior Pattern Analysis: Establish a baseline of legitimate refund behavior to proactively identify and flag anomalous refund patterns indicative of fraudulent activity.

• Cross-Platform Intelligence Sharing: Foster industry alliances for the secure exchange of underground industry (UI) intelligence and high-risk account data, creating a collective defense mechanism.

Intelligence-Driven Defense Operations

The Threat Hunter intelligence operation team empowers enterprises to efficiently combat malicious refund risks through network-wide threat monitoring, granular UI behavior analysis, and precision defense strategies.

• Real-time Risk Alerting: Employ AI algorithms augmented by expert human review to actively monitor UI refund service advertisements, tutorials, and compromised order information across platforms like Telegram and Facebook. This facilitates preemptive identification of risky accounts and anomalous behaviors.

• In-depth analysis of adversary methods and tactics: Conduct in-depth research on risk events across the network, providing timely and actionable intelligence briefings to affected platforms regarding evolving UI tactics.

• Attack Chain Interruption Strategy: Deliver precise interception point recommendations tailored to specific attack chains associated with diverse malicious refund methodologies.

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