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Vulnerability Database/CVE-2025-44000

CVE-2025-44000: MedDream PACS Premium XSS Vulnerability

CVE-2025-44000 is a reflected XSS vulnerability in MedDream PACS Premium 7.3.6.870 that enables arbitrary JavaScript execution through malicious URLs. This article covers the technical details, affected versions, and mitigation.

Published:

CVE-2025-44000 Overview

A reflected cross-site scripting (XSS) vulnerability exists in the sendOruReport functionality of MedDream PACS Premium 7.3.6.870. This vulnerability allows attackers to craft malicious URLs that, when visited by a victim, execute arbitrary JavaScript code in the context of the victim's browser session. The vulnerability stems from improper input sanitization in the sendOruReport endpoint, enabling injection of malicious scripts through specially crafted URL parameters.

Critical Impact

Successful exploitation allows attackers to steal session cookies, hijack user sessions, perform actions on behalf of authenticated users, and potentially access sensitive medical imaging data stored in the PACS system.

Affected Products

  • MedDream PACS Premium 7.3.6.870

Discovery Timeline

  • 2026-01-20 - CVE-2025-44000 published to NVD
  • 2026-01-20 - Last updated in NVD database

Technical Details for CVE-2025-44000

Vulnerability Analysis

This reflected XSS vulnerability (CWE-79) affects the sendOruReport functionality within MedDream PACS Premium, a medical imaging system commonly used in healthcare environments. The vulnerability requires user interaction - specifically, a victim must click on a malicious link crafted by the attacker. When exploited, the malicious JavaScript executes within the victim's browser session, inheriting all privileges associated with that session.

Healthcare PACS systems often contain sensitive patient data and diagnostic images, making this vulnerability particularly concerning in medical environments. An attacker could potentially leverage this XSS flaw to steal authentication tokens, modify displayed medical data, or redirect users to phishing pages designed to capture credentials.

Root Cause

The root cause of this vulnerability is insufficient input validation and output encoding in the sendOruReport functionality. User-supplied input is reflected back to the browser without proper sanitization, allowing script tags and JavaScript event handlers to be injected and executed. The application fails to implement appropriate context-aware output encoding when rendering user-controlled data in HTML responses.

Attack Vector

The attack requires an attacker to craft a malicious URL containing JavaScript payload in vulnerable parameters of the sendOruReport endpoint. The attacker must then socially engineer a victim into clicking the link - typically through phishing emails, malicious links on other websites, or instant messages. When the victim clicks the link while authenticated to the MedDream PACS system, the injected JavaScript executes with the victim's session privileges.

The vulnerability is network-accessible and requires no authentication from the attacker's perspective, though it does require user interaction. The scope is changed, meaning the vulnerable component impacts resources beyond its security scope.

Detection Methods for CVE-2025-44000

Indicators of Compromise

  • Unusual URL patterns in web server logs containing encoded script tags or JavaScript event handlers targeting the sendOruReport endpoint
  • Web application firewall (WAF) alerts for XSS payloads in HTTP requests to MedDream PACS
  • User reports of unexpected browser behavior or redirects when accessing the PACS system
  • Network traffic analysis showing requests to the sendOruReport functionality with suspicious query parameters

Detection Strategies

  • Deploy web application firewall rules specifically monitoring for XSS patterns in requests to MedDream PACS endpoints
  • Implement Content Security Policy (CSP) headers to detect and block inline script execution attempts
  • Enable detailed logging on the MedDream PACS application server to capture full request URLs
  • Utilize browser-based XSS auditors and security headers to catch exploitation attempts

Monitoring Recommendations

  • Monitor authentication logs for session anomalies that may indicate session hijacking post-exploitation
  • Configure SIEM rules to alert on URL patterns containing common XSS payloads directed at PACS infrastructure
  • Review web server access logs regularly for requests containing script injection patterns
  • Implement real-time alerting for multiple failed or suspicious requests to the sendOruReport endpoint

How to Mitigate CVE-2025-44000

Immediate Actions Required

  • Restrict access to the MedDream PACS system to trusted networks and users until a patch is applied
  • Implement web application firewall rules to filter known XSS patterns on the sendOruReport endpoint
  • Educate users about the risks of clicking suspicious links, especially those targeting internal medical systems
  • Enable strict Content Security Policy headers to mitigate the impact of any successful XSS exploitation

Patch Information

Contact MedDream support for security updates addressing this vulnerability. Review the Talos Intelligence Vulnerability Report for additional remediation guidance and vendor communication details.

Workarounds

  • Implement a reverse proxy or WAF in front of MedDream PACS to filter malicious input
  • Configure Content Security Policy headers with script-src 'self' to prevent execution of injected inline scripts
  • Restrict access to the sendOruReport functionality to only authenticated and authorized users from trusted IP ranges
  • Consider disabling the sendOruReport functionality temporarily if it is not critical to operations
bash
# Example Apache configuration to add CSP headers
# Add to MedDream PACS virtual host configuration
Header set Content-Security-Policy "default-src 'self'; script-src 'self'; style-src 'self' 'unsafe-inline'"
Header set X-XSS-Protection "1; mode=block"
Header set X-Content-Type-Options "nosniff"

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

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