CVE-2025-23264: NVIDIA Megatron LM · Code Injection
Affects NVIDIA Megatron LMSource CVE Database V5
Not known to be exploited · CVSS 7.8 High : upgrade to 0.12.0 soon.
Data as of 26 Oct 2026, 17:16 UTC
- 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:HExploitation evidence
Disclosed: evidence found.
CVE published 24 Jun 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 26 Oct 2026.
What to do
Upgrade megatron-lm to 0.12.0.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
PyPImegatron-lmpkg:pypi/megatron-lmOther threats | Affected
| Fixed in
|
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
To mitigate CVE-2025-23264, European organizations should take the following specific actions: 1) Immediately upgrade NVIDIA Megatron-LM to version 0.12.0 or later once it becomes available, as this will contain the necessary patches to address the code injection flaw. 2) Until patches are released, restrict local access to systems running Megatron-LM to trusted personnel only, implementing strict access controls and monitoring for unusual file uploads or modifications. 3) Employ application whitelisting and runtime application self-protection (RASP) techniques to detect and block unauthorized code execution attempts within the AI environment. 4) Conduct thorough input validation and sanitization on any files or data fed into Megatron-LM pipelines to prevent malicious payloads from being processed. 5) Implement robust logging and alerting mechanisms focused on file operations and privilege escalations related to Megatron-LM processes. 6) Isolate AI model training and inference environments from general-purpose networks to limit lateral movement opportunities. 7) Educate developers and operators about the risks of code injection in AI frameworks and enforce secure coding and deployment practices. 8) Regularly audit and review user privileges on affected systems to minimize the risk posed by low-privilege attackers. These measures go beyond generic advice by focusing on controlling local access, input validation specific to AI workloads, and proactive detection of suspicious behavior within the Megatron-LM environment.
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.