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VulnerabilityPublished

CVE-2025-23306: NVIDIA Megatron-LM · Code Injection

Affects NVIDIA Megatron-LMSource CVE Database V5

0

Not known to be exploited · CVSS 7.8 High : upgrade to 0.12.2 soon.

Data as of 26 Oct 2026, 12:55 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 12% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.12.2
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 13 Aug 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 NVIDIA Megatron-LM to 0.12.2.

Affected software

ProductAffectedFixed in
NVIDIA Megatron-LMAffected
  • <0.12.2
Fixed in
  • 0.12.2

AI-suggested

European organizations should prioritize upgrading NVIDIA Megatron-LM to version 0.12.2 or later as soon as it becomes available, as this is the definitive fix for the vulnerability. Until then, organizations should restrict access to systems running Megatron-LM to trusted users only, enforce strict access controls, and monitor for unusual activity indicative of exploitation attempts. Implementing application whitelisting and runtime application self-protection (RASP) can help detect and block unauthorized code execution. Conduct thorough input validation and sanitization on any user-supplied data interacting with the training arguments to reduce injection risk. Network segmentation should be employed to isolate AI training environments from broader enterprise networks, limiting lateral movement potential. Regularly audit and review logs for suspicious behavior related to Megatron-LM processes. Additionally, organizations should engage with NVIDIA support channels for any interim patches or mitigation guidance and consider deploying endpoint detection and response (EDR) solutions tailored to detect code injection patterns. Finally, incorporate this vulnerability into incident response plans to ensure rapid containment if exploitation is detected.

Description

NVIDIA Megatron-LM for all platforms contains a vulnerability in the megatron/training/ arguments.py component where an attacker could cause a code injection issue by providing a malicious input. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

References

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