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Vulnerability Database/CVE-2026-23717

CVE-2026-23717: Siemens Simcenter Femap RCE Vulnerability

CVE-2026-23717 is a remote code execution flaw in Siemens Simcenter Femap caused by out of bounds read when parsing XDB files. Attackers can exploit this to execute arbitrary code. This article covers technical details, affected versions, impact assessment, and mitigation strategies.

Published:

CVE-2026-23717 Overview

A vulnerability has been identified in Siemens Simcenter Femap and Simcenter Nastran that affects all versions prior to V2512. The affected applications contain an out-of-bounds read vulnerability that occurs while parsing specially crafted XDB files. Successful exploitation of this vulnerability could allow an attacker to execute arbitrary code in the context of the current process, potentially leading to full system compromise.

Critical Impact

Attackers can exploit malicious XDB files to achieve code execution, potentially compromising engineering workstations and sensitive simulation data.

Affected Products

  • Siemens Simcenter Femap (All versions < V2512)
  • Siemens Simcenter Nastran (All versions < V2512)

Discovery Timeline

  • 2026-02-10 - CVE-2026-23717 published to NVD
  • 2026-02-11 - Last updated in NVD database

Technical Details for CVE-2026-23717

Vulnerability Analysis

This vulnerability is classified as CWE-125 (Out-of-Bounds Read), a memory corruption flaw that occurs when software reads data past the end or before the beginning of the intended buffer. In the context of Simcenter Femap and Nastran, the vulnerability manifests during the parsing of XDB files, which are proprietary file formats used for finite element analysis data exchange.

When a user opens a maliciously crafted XDB file, the parser fails to properly validate buffer boundaries, allowing read operations to access memory outside the allocated buffer. While out-of-bounds read vulnerabilities are often associated with information disclosure, this particular instance enables code execution, suggesting the attacker can leverage the memory access violation to corrupt program control flow or combine it with other exploitation techniques.

The local attack vector requires user interaction, meaning an attacker must convince a victim to open a malicious XDB file. This is typically achieved through social engineering tactics such as phishing emails containing malicious attachments or hosting compromised files on file-sharing platforms commonly used by engineering teams.

Root Cause

The root cause of this vulnerability lies in insufficient boundary checking within the XDB file parser. When processing XDB file structures, the application fails to validate that index values or size parameters fall within expected ranges before using them to access memory buffers. This allows crafted input to trigger reads beyond the allocated buffer boundaries.

Attack Vector

This vulnerability requires local access combined with user interaction. An attacker must craft a malicious XDB file and deliver it to a victim through social engineering methods. When the victim opens the file using Simcenter Femap or Simcenter Nastran, the vulnerable parsing code processes the malformed data, triggering the out-of-bounds read condition. The attack complexity is considered high due to the specific conditions required for successful exploitation.

The vulnerability manifests in the XDB file parsing functionality when processing specially crafted file structures. Technical details regarding the specific parsing routines and exploitation mechanics are documented in the Siemens Security Advisory SSA-965753.

Detection Methods for CVE-2026-23717

Indicators of Compromise

  • Unexpected crashes or abnormal termination of Simcenter Femap or Nastran applications
  • Presence of suspicious or unexpected XDB files from untrusted sources in user directories
  • Unusual process behavior following the opening of XDB files, such as child process spawning or network connections

Detection Strategies

  • Monitor file system activity for XDB file access from untrusted locations or email attachments
  • Implement application behavior monitoring to detect anomalous memory access patterns in Simcenter applications
  • Deploy endpoint detection and response (EDR) solutions to identify exploitation attempts and post-exploitation activity

Monitoring Recommendations

  • Enable detailed logging for Simcenter Femap and Nastran application events
  • Monitor for unusual file access patterns involving XDB files, particularly from temporary directories or email attachment folders
  • Implement network monitoring for suspicious outbound connections from engineering workstations following file operations

How to Mitigate CVE-2026-23717

Immediate Actions Required

  • Upgrade Simcenter Femap to version V2512 or later immediately
  • Upgrade Simcenter Nastran to version V2512 or later immediately
  • Restrict handling of XDB files from untrusted sources until patches are applied
  • Educate users about the risks of opening files from unknown or suspicious sources

Patch Information

Siemens has released version V2512 for both Simcenter Femap and Simcenter Nastran to address this vulnerability. Organizations should obtain the updated software through their standard Siemens support channels. Detailed patch information and remediation guidance are available in the Siemens Security Advisory SSA-965753.

Workarounds

  • Implement strict file validation policies that restrict XDB file handling to trusted sources only
  • Consider sandboxing or virtualization for opening XDB files from external parties until patching is complete
  • Deploy application whitelisting to prevent unauthorized code execution on engineering workstations
bash
# Configuration example: Restrict XDB file associations (Windows example)
# Remove file association for XDB files from untrusted locations
ftype XDBFile="C:\Program Files\Siemens\Femap\femap.exe" "%1"
# Consider implementing Group Policy to block XDB attachments in email
# or restrict execution from temporary/download directories

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

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