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Analyze » Stripe » PREPAYSTRADOWOO1788171946

Incident Score: Analysis & Impact (PREPAYSTRADOWOO1788171946)

The details regarding individual company incidents & reports gives you full view from every side.

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-29
Company Score Before Incident719 / 1000
Company Score After Incident690 / 1000
INCIDENT NUMBERPREPAYSTRADOWOO1788171946
Type of Cyber IncidentCyber Attack
ATTACK VECTORServer compromise, JavaScript injection, Ethereum smart contracts
DATA EXPOSEDPayment card numbers, CVVs, expiry...
INCIDENT DATE28/02/2026
STATUSpublished

Key Highlights From The Incident Analysis

  • Timeline of Stripe's Cyber Attack and lateral movement inside company's environment.
  • Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
  • How Rankiteo’s incident engine converts technical details into a normalized incident score.
  • How this cyber incident impacts Stripe Rankiteo cyber scoring and cyber rating.
  • Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.

Full Incident Analysis Transcript

In this Rankiteo incident briefing, we review the Stripe breach identified under incident ID PREPAYSTRADOWOO1788171946.

The analysis begins with a detailed overview of Stripe's information like the linkedin page: https://www.linkedin.com/company/stripe, the number of followers: 1654923, the industry type: Technology, Information and Internet and the number of employees: 17148 employees

After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 719 and after the incident was 690 with a difference of -29 which is could be a good indicator of the severity and impact of the incident.

In the next step of the video, we will analyze in more details the incident and the impact it had on Stripe and their customers.

A newly reported cybersecurity incident, "HexMage Magecart Campaign Exploits Ethereum Smart Contracts to Skim Payment Data from 40+ E-Commerce Sites", has drawn attention.

A sophisticated Magecart campaign, dubbed HexMage, has compromised over 40 e-commerce storefronts across 15+ countries, leveraging Ethereum smart contracts to deliver and conceal payment-card skimmers.

The disruption is felt across the environment, affecting E-commerce storefronts (WooCommerce, PrestaShop, Magento, WordPress), and exposing Payment card numbers, CVVs, expiry dates, cardholder names, billing emails.

In response, moved swiftly to contain the threat with measures like Monitoring for unexpected Web3 library loads, JSON-RPC requests, and unauthorized JavaScript in payment templates.

The case underscores how teams are taking away lessons such as The campaign highlights the evolving tactics of Magecart groups, combining server-side compromises, browser-based malware delivery, and decentralized infrastructure (Ethereum smart contracts) to evade detection and maximize persistence, and recommending next steps like Monitor checkout pages for unexpected Web3 library loads (e.g., ethers.js), Detect JSON-RPC requests to public blockchain endpoints (e.g., 0xrpc.io) and Inspect GTM-like code that fails to fetch gtm.js for malicious intent.

Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.

The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.

MITRE ATT&CK® Correlation Analysis

Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with high confidence (90%), supported by evidence indicating compromised WooCommerce, PrestaShop, Magento, and WordPress installations. Under the Execution tactic, the analysis identified JavaScript (T1059.007) with high confidence (95%), supported by evidence indicating injecting a lightweight JavaScript loader disguised as a Google Tag Manager snippet and Exploitation for Client Execution (T1203) with moderate to high confidence (80%), supported by evidence indicating final-stage skimmer overlays or replaces legitimate payment fields. Under the Persistence tactic, the analysis identified Browser Extensions (T1176) with moderate to high confidence (70%), supported by evidence indicating javaScript loader disguised as GTM snippet with fake comments and obfuscated data and Modify Authentication Process (T1556) with moderate to high confidence (80%), supported by evidence indicating skimmer overlays or replaces legitimate payment fields to capture data. Under the Defense Evasion tactic, the analysis identified Obfuscated Files or Information (T1027) with high confidence (90%), with evidence including obfuscated Base64 data in JavaScript loader, and base64-encoded exfiltration of stolen data, Deobfuscate/Decode Files or Information (T1140) with high confidence (90%), supported by evidence indicating loader decodes Base64-encoded skimmer URL directly as fallback, Reflective Code Loading (T1620) with moderate to high confidence (80%), supported by evidence indicating fetches ethers.js from jsDelivr CDN and queries Ethereum Sepolia endpoint, and Indicator Removal: Clear Windows Event Logs (T1070.001) with moderate confidence (60%), supported by evidence indicating avoids execution when WordPress admin is logged in to reduce detection. Under the Credential Access tactic, the analysis identified Adversary-in-the-Middle: Web Session Cookie (T1557.003) with high confidence (90%), supported by evidence indicating skimmer captures card numbers, CVVs, expiry dates, cardholder names, billing emails. Under the Collection tactic, the analysis identified Input Capture: Web Portal Capture (T1056.003) with high confidence (95%), supported by evidence indicating final-stage skimmer overlays or replaces legitimate payment fields to capture data and Automated Collection (T1119) with high confidence (90%), supported by evidence indicating tailored malware captures payment data during checkout processes. Under the Command and Control tactic, the analysis identified Web Service: Bidirectional Communication (T1102.002) with moderate to high confidence (80%), supported by evidence indicating queries 0xrpc.io, a public Ethereum Sepolia endpoint for payload domains and Application Layer Protocol: DNS (T1071.004) with moderate to high confidence (70%), supported by evidence indicating uses Ethereum smart contracts to dynamically rotate malicious infrastructure. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (90%), supported by evidence indicating base64-encoded exfiltration of stolen payment data before restoring interface and Exfiltration Over Web Service: Exfiltration to Cloud Storage (T1567.002) with moderate to high confidence (70%), supported by evidence indicating uses decentralized Ethereum smart contracts for data staging. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
Exploit Public-Facing Application (90%)
Execution
JavaScript (95%)
Exploitation for Client Execution (80%)
Persistence
Browser Extensions (70%)
Modify Authentication Process (80%)
Defense Evasion
Obfuscated Files or Information (90%)
Deobfuscate/Decode Files or Information (90%)
Reflective Code Loading (80%)
Indicator Removal: Clear Windows Event Logs (60%)
Credential Access
Adversary-in-the-Middle: Web Session Cookie (90%)
Collection
Input Capture: Web Portal Capture (95%)
Automated Collection (90%)
Command and Control
Web Service: Bidirectional Communication (80%)
Application Layer Protocol: DNS (70%)
Exfiltration
Exfiltration Over C2 Channel (90%)
Exfiltration Over Web Service: Exfiltration to Cloud Storage (70%)

Sources & References