CVE-2025-23265: 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, 14:05 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
- Immediate upgrade to NVIDIA Megatron-LM version 0.12.0 or later once available, as this version addresses the vulnerability.
- If you cannot patch yet, restrict access to systems running Megatron-LM to trusted users only, minimizing the risk of local exploitation.
- Implement strict file input validation and sanitization controls to prevent malicious files from being processed by the vulnerable Python component.
- Employ application-level sandboxing or containerization to isolate Megatron-LM processes, limiting the potential impact of code execution exploits.
- Monitor system logs and audit trails for unusual file access patterns or privilege escalations related to Megatron-LM usage.
- Enforce the principle of least privilege on accounts with access to Megatron-LM environments to reduce the attack surface.
- 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.
- Conduct regular security assessments and penetration testing focused on AI infrastructure to identify and remediate similar vulnerabilities proactively.
- 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.