What is the CVSS score of EUVD-2026-41044?

EUVD-2026-41044 has a CVSS 3.1 base score of 7.8 (High). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H. EPSS exploitation probability: 0.1%.

Which versions of NVIDIA Megatron Bridge are affected by EUVD-2026-41044?

NVIDIA Megatron Bridge on Linux contains a vulnerability enabling arbitrary code execution and privilege escalation when local users load malicious serialized data.

How to fix or mitigate EUVD-2026-41044?

Within 24 hours: Conduct inventory of all systems running NVIDIA Megatron Bridge and identify dependencies and user populations. Within 7 days: Restrict bridge execution to essential personnel only, disable on non-critical systems, and conduct security awareness training on risks of untrusted files. Within 30 days: Monitor NVIDIA security advisories for patch availability and establish expedited patching procedures for deployment upon release.

NVIDIA Megatron Bridge EUVD-2026-41044

| CVE-2026-24244 HIGH

Deserialization of Untrusted Data (CWE-502)

2026-07-01 nvidia

GHSA-wh7f-f43p-5q6v

Nvidia Deserialization Information Disclosure RCE Megatron Bridge

7.8

CVSS 3.1 · Vendor: nvidia

Severity by source

Vendor (nvidia) PRIMARY

7.8 HIGH

AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

vuln.today AI

7.8 HIGH

Local delivery of a malicious artifact with no auth (AV:L, PR:N) but a victim must load it (UI:R); deserialization yields full code execution, so C/I/A all High.

3.1 AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

4.0 AV:L/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Primary rating from Vendor (nvidia).

CVSS VectorVendor: nvidia

CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Attack Vector

Local

Attack Complexity

Low

Privileges Required

None

User Interaction

Required

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

Analysis Generated

Jul 01, 2026 - 15:56 vuln.today

CVE Published

Jul 01, 2026 - 14:53 cve.org

HIGH 7.8

DescriptionCVE.org

NVIDIA Megatron Bridge for Linux contains a vulnerability where an attacker could cause deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure.

Articles & Coverage 1

News 16 New Nvidia Vulnerabilities - 3 Critical, 13 High Severity Jul 02, 2026

AnalysisAI

Arbitrary code execution and privilege escalation in NVIDIA Megatron Bridge on Linux arises from unsafe deserialization of untrusted data, allowing a local attacker who convinces a user to load a malicious serialized object to run code, tamper with data, and disclose information. NVIDIA (the reporting vendor) rates it 7.8 (High); the CVSS vector requires local access and user interaction, so exploitation is not remote-unauthenticated. There is no public exploit identified at time of analysis and it is not listed in CISA KEV.

Technical ContextAI

Megatron Bridge is NVIDIA's open-source library used in large-language-model and deep-learning training pipelines to bridge and convert model checkpoints/state between frameworks (e.g., Megatron-Core and Hugging Face formats). The affected component is identified by CPE cpe:2.3:a:nvidia:megatron-bridge:*:*:*:*:*:*:*:*. The root cause is CWE-502 (Deserialization of Untrusted Data): the library reconstructs objects from serialized input (commonly Python pickle-based checkpoint or config formats in the ML ecosystem) without validating that the data is trustworthy, so a crafted payload can trigger object instantiation that executes attacker-controlled code during the load step.

RemediationAI

Upgrade NVIDIA Megatron Bridge to the fixed release identified in NVIDIA's advisory at https://github.com/NVIDIA/product-security/tree/main/2026/5841 - a patch is available per the vendor advisory, but the exact fixed version is not stated in the available data and must be read from that advisory. Until patched, treat all model checkpoints, configuration files, and serialized artifacts as untrusted: only load checkpoints from sources you control or have cryptographically verified, and avoid loading third-party or downloaded models on privileged training hosts (trade-off: blocks convenient reuse of public model artifacts). Where the workflow allows, restrict who can supply files to the load path and run conversion/loading jobs under a low-privilege, sandboxed account or container to contain the impact of a malicious deserialization (trade-off: added operational overhead), and prefer safe serialization formats (e.g., safetensors) over pickle-based formats where the tooling supports it.