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VulnerabilityPublished

CVE-2025-23265: NVIDIA Megatron LM · Code Injection

Affects NVIDIA Megatron LMSource CVE Database V5

0

Not known to be exploited · CVSS 7.8 High : upgrade to 0.12.0 soon.

Data as of 26 Oct 2026, 17:16 UTC

CVSS 3.1 base score
7.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.2% chance of exploitation in 30 days – higher than 20% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.12.0
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 24 Jun 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

Upgrade megatron-lm to 0.12.0.

Affected software

ProductAffectedFixed in
PyPImegatron-lmpkg:pypi/megatron-lmOther threatsAffected
  • <0.12.0
Fixed in
  • 0.12.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

  1. Immediate upgrade to NVIDIA Megatron-LM version 0.12.0 or later once available, as this version addresses the vulnerability.
  2. If you cannot patch yet, restrict access to systems running Megatron-LM to trusted users only, minimizing the risk of local exploitation.
  3. Implement strict file input validation and sanitization controls to prevent malicious files from being processed by the vulnerable Python component.
  4. Employ application-level sandboxing or containerization to isolate Megatron-LM processes, limiting the potential impact of code execution exploits.
  5. Monitor system logs and audit trails for unusual file access patterns or privilege escalations related to Megatron-LM usage.
  6. Enforce the principle of least privilege on accounts with access to Megatron-LM environments to reduce the attack surface.
  7. For cloud deployments, leverage cloud provider security features such as role-based access control (RBAC), network segmentation, and runtime threat detection to contain potential exploitation.
  8. Conduct regular security assessments and penetration testing focused on AI infrastructure to identify and remediate similar vulnerabilities proactively.
  9. Educate AI development and operations teams about the risks of processing untrusted input files and the importance of secure coding practices in AI frameworks.

Description

NVIDIA Megatron-LM for all platforms contains a vulnerability in a python component where an attacker may cause a code injection issue by providing a malicious file. A successful exploit of this vulnerability may lead to Code Execution, Escalation of Privileges, Information Disclosure and Data Tampering.

References

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