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VulnerabilityPublished

CVE-2025-23348: NVIDIA Megatron-LM · Code Injection

Affects NVIDIA Megatron-LMSource CVE Database V5

0

Not known to be exploited · CVSS 7.8 High : upgrade to 0.13.1 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 15% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.13.1
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 Sep 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 0.13.1.

Affected software

ProductAffectedFixed in
NVIDIA Megatron-LMAffected
  • <0.13.1
Fixed in
  • 0.13.1

AI-suggested

1. 2. If you cannot patch yet, restrict access to the pretrain_gpt script and the environments where it runs, limiting execution to trusted users and processes only. 3. Implement strict input validation and sanitization on all data fed into the pretrain_gpt script, especially if sourced externally or from untrusted origins. 4. Employ runtime application self-protection (RASP) or behavior monitoring tools to detect anomalous script execution or code injection attempts. 5. Use containerization or sandboxing techniques to isolate the training environment, minimizing the impact of potential code execution. 6. Conduct regular audits and code reviews of AI training scripts and pipelines to identify and remediate insecure coding practices. 7. Enhance network segmentation to limit lateral movement if an attacker gains local access. 8. Monitor logs and system behavior for signs of privilege escalation or unauthorized code execution related to Megatron-LM processes. 9. Educate AI development teams about secure coding practices and the risks of code injection vulnerabilities in AI frameworks.

Description

NVIDIA Megatron-LM for all platforms contains a vulnerability in the pretrain_gpt script, where malicious data created by an attacker may cause a code injection issue. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

References

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