CVE-2025-23349: NVIDIA Megatron-LM · Code Injection
Affects NVIDIA Megatron-LMSource CVE Database V5
Not known to be exploited · CVSS 7.8 High : upgrade to 0.13.1 soon.
Data as of 25 Oct 2026, 19:02 UTC
- 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:HExploitation evidence
Disclosed: evidence found.
CVE published 24 Sep 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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 2026-10-25.
What to do
Upgrade NVIDIA Megatron-LM to 0.13.1.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| NVIDIA Megatron-LM | Affected
| Fixed in
|
AI-suggested
To mitigate this vulnerability, European organizations should prioritize upgrading NVIDIA Megatron-LM to versions 0.13.1 or 0.12.3 or later, where the vulnerability is patched. Until upgrades are applied, organizations should restrict access to systems running Megatron-LM to trusted users only, enforcing strict access controls and monitoring for unusual activity. Implementing application whitelisting and runtime application self-protection (RASP) can help detect and prevent unauthorized code execution. Conduct thorough code reviews and input validation audits for any custom modifications or integrations with Megatron-LM, especially around the vulnerable component. Network segmentation should isolate AI training environments from broader enterprise networks to limit lateral movement in case of compromise. Employ endpoint detection and response (EDR) tools with behavioral analytics to identify potential exploitation attempts. Additionally, organizations should establish incident response plans specific to AI infrastructure and conduct regular security awareness training for users with access to these systems. Finally, maintain up-to-date backups of AI models and training data to enable recovery from tampering or data loss.
Description
NVIDIA Megatron-LM for all platforms contains a vulnerability in the tasks/orqa/unsupervised/nq.py component, where an attacker may cause a code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.