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VulnerabilityPublished

CVE-2025-33248: NVIDIA Megatron LM · Deserialization of Untrusted Data

Affects NVIDIA Megatron LMSource CVE Database V5

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Not known to be exploited · CVSS 7.8 High : upgrade to 0.15.3 soon.

Data as of 29 Oct 2026, 19:04 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 11% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.15.3
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 Mar 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: 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 29 Oct 2026.

What to do

Upgrade megatron-lm to 0.15.3.

Affected software

ProductAffectedFixed in
NVIDIAMegatron LMAffected
  • =All versions prior to 0.15.3
Fixed in–
PyPImegatron-lmpkg:pypi/megatron-lmOther threatsAffected
  • <0.15.3
Fixed in
  • 0.15.3

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

  1. Upgrade NVIDIA Megatron-LM to version 0.15.3 or later immediately, as this version addresses the deserialization vulnerability.
  2. Implement strict input validation and sanitization on all files and data loaded by the hybrid conversion script to prevent malicious payloads.
  3. Restrict access to systems running Megatron-LM to trusted users only, minimizing the risk of local exploitation.
  4. Employ sandboxing or containerization techniques to isolate the execution environment of Megatron-LM, limiting the impact of potential code execution.
  5. Monitor system logs and behavior for unusual activity indicative of exploitation attempts, such as unexpected file loads or privilege escalations.
  6. Educate users about the risks of loading untrusted files and enforce policies that prohibit loading files from unverified sources.
  7. Conduct regular security audits and code reviews focusing on deserialization and input handling practices within AI frameworks.
  8. Consider using application whitelisting to prevent unauthorized binaries or scripts from executing on systems hosting Megatron-LM.

Description

NVIDIA Megatron-LM contains a vulnerability in the hybrid conversion script where an Attacker may cause an RCE by convincing a user to load a maliciously crafted file. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data tampering.

References

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