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NVIDIA Megatron-LM EUVDEUVD-2026-98562

| CVE-2026-65110 HIGH
Deserialization of Untrusted Data (CWE-502)
2026-10-13 psirt@nvidia.com GHSA-4jq4-xvrh-7j5r
7.8
CVSS 3.1 · Vendor: nvidia
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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Craft malicious checkpoint file
  2. Exploit
    Convince user to load it in Megatron-LM
  3. Execution
    Trigger unsafe deserialization (CWE-502)
  4. Impact
    Execute arbitrary code as the loading user

Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A08:2025 - Software or Data Integrity Failures
ASVS 5.0
V1 - Encoding and Sanitization Safe deserialization, type allowlists
MITRE ATT&CK

2 techniques across 2 tactics: Execution, Initial Access

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Exploitation Conditions

Exploitation requires that a victim load an attacker-supplied, maliciously crafted Megatron-LM checkpoint file into the checkpoint-loading path, which deserializes untrusted data (CWE-502). The attack is local (AV:L) - the attacker does not reach the target over the network but must deliver the malicious artifact (e.g., via a model repository, shared filesystem, or social engineering) and induce the user to load it. Key limiting factors: the attack depends on victim action (loading the file), aligning with the description's 'convincing a user' even though the CVSS vector marks UI:N; it requires low-level privileges on the system (PR:L) and does not work against systems that never load untrusted checkpoints. Code executes with the privileges of the loading user/process, not inherently root.

Impact

This is a CWE-502 deserialization-to-RCE flaw in NVIDIA Megatron-LM checkpoint loading, scored CVSS 7.8 (High) by NVIDIA PSIRT. The signals point to meaningful-but-conditional risk rather than a drop-everything emergency. The CVSS vector is AV:L (local) and PR:L, meaning the attack is not a remote, network-facing vector - the malicious checkpoint must be loaded on the target system by a user with at least low privileges. No EPSS score, KEV listing, or public POC is present in the provided data, so there is no evidence of active or imminent mass exploitation; exploitation status is best described as no public exploit identified at time of analysis. There is a notable internal conflict: NVIDIA's vector sets UI:N (no user interaction), yet the description explicitly says the attack requires 'convincing a user to load a maliciously crafted file,' which is textbook user interaction - verify UI with the vendor, as this directly affects whether the flaw is treated as a social-engineering/supply-chain issue or an autonomous one. Overall this behaves like the broad class of 'malicious ML artifact' deserialization bugs (akin to unsafe pickle/torch.load loading): high technical impact (C:H/I:H/A:H, full code execution as the loading user) but gated behind the victim voluntarily loading an attacker-supplied file, which realistically means a targeted supply-chain or social-engineering delivery rather than opportunistic scanning.

Exploit ScenarioDerived

An attacker publishes or shares a trojanized model checkpoint (e.g., via a model hub, shared storage, or a compromised artifact) and convinces an ML engineer to load it into a Megatron-LM training or inference workflow. When the victim loads the checkpoint, malicious serialized objects are deserialized (CWE-502), executing attacker code with the victim's privileges. No public exploit code is identified in the provided data, but the AV:L/AC:L profile makes successful exploitation straightforward once the victim loads the file.

This Week Schedule remediation this week. Why: HIGH severity (CVSS 7.0-8.9).
No patch listed yet 0.2% exploitation probability (EPSS)

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AnalysisAIv1 – Oct 13, 2026

Deserialization of untrusted data in the NVIDIA Megatron-LM checkpoint-loading path allows code execution, privilege escalation, information disclosure, and data tampering when a user is convinced to load a maliciously crafted checkpoint file. NVIDIA PSIRT scores this CVSS 7.8 (High) and both the vendor and the independent assessment agree on full confidentiality, integrity, and availability impact; the assessments differ on prerequisites, with the vendor vector indicating low privileges and no user interaction (PR:L/UI:N) while the independent assessment models it as no privileges with required victim interaction (PR:N/UI:R). No public exploit code or CISA KEV entry was identified at time of analysis, and only environments that actually load untrusted checkpoints are exposed; code runs with the privileges of the loading user or process, not inherently as root.

RemediationAI

No vendor-released patch was identified in the provided data; consult the NVIDIA product security advisory at https://github.com/NVIDIA/product-security/tree/main/2026/5881 (and the NVD/CVE records above) for the authoritative fix version and apply it as soon as NVIDIA publishes one. Until then, treat checkpoint loading as a trust boundary: load only checkpoints from trusted, integrity-verified sources and validate SHA-256 hashes or signatures obtained over a separate channel before loading; where supported, force safe deserialization such as torch.load(..., weights_only=True) or convert checkpoints to a non-executable tensor-only format like safetensors - noting the trade-off that these modes reject checkpoints containing arbitrary Python objects and may break legacy or custom optimizer/RNG state, so validate in a staging environment first. Because the vulnerable path is the load itself rather than a toggleable service, the most reliable compensating control is isolation: perform loading inside a container or VM running as an unprivileged user with read-only mounts, no network egress, and no access to credentials or shared storage, accepting the added operational overhead. Additionally restrict write access to model repositories and checkpoint directories, disable automatic loading of remote or user-supplied checkpoints in CI/CD and training job templates, and monitor for unexpected child processes or outbound connections spawned by checkpoint-loading jobs.

Affected VersionsENISA

Megatron-LM 0 to 17

Affects your stack

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Technical ContextAI · show details

The root cause is CWE-502 (Deserialization of Untrusted Data) in a PyTorch-based distributed training framework. Megatron-LM checkpoints bundle model weights, optimizer state, and training metadata, and the loading path reconstructs these objects by deserializing attacker-controllable data; Python pickle-style deserialization can invoke arbitrary callables during object reconstruction, which turns a seemingly benign model file into an execution primitive. The attack vector is local (AV:L) in the sense that the attacker does not reach the target over the network - the malicious artifact must instead be delivered through a model repository, shared filesystem, or social engineering and then loaded by the victim. The vendor vector and the independent assessment diverge on the exact prerequisites (PR:L/UI:N versus PR:N/UI:R), but both describe the same fundamental condition: a user or automated pipeline must invoke the vulnerable checkpoint-loading code on an untrusted file. Because execution inherits the permissions of the loading process, impact scales with how privileged the training or inference job runs.

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Severity by source

Vendor (nvidia) PRIMARY 7.8 HIGH
AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
vuln.today AI 7.8 HIGH

Local artifact load (AV:L) that the victim must actively perform (UI:R) with no attacker privileges (PR:N); deserialization yields full code execution so C:H/I:H/A:H, scope unchanged.

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:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Primary rating from Vendor (nvidia).

CVSS VectorVendor: nvidia

Attack Vector
Local
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

DescriptionCVE.org

NVIDIA Megatron-LM contains a vulnerability in checkpoint loading where, an attacker could cause an RCE by convincing a user to load a maliciously crafted file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

Executive ViewAI

Why It Matters

NVIDIA's Megatron-LM framework can execute attacker-supplied code when it loads a crafted checkpoint file, giving an intruder the same access and data reach as the user or service process performing the load. Organizations affected are those running large-model training or fine-tuning pipelines that ingest checkpoints from external, shared, or unverified sources - a common practice in collaborative machine-learning work. Because the flaw can compromise model integrity as well as the host, it is both a security and a model-supply-chain risk that warrants leadership attention even though no public exploit is known today.

Business Impact: High
Compensating Controls

No vendor-released patch identified at time of analysis. Interim mitigations: refuse to load checkpoints from unverified or external sources; restrict all externally sourced checkpoints to isolated, network-restricted sandboxes with no access to production data or credentials; run checkpoint-loading processes under least-privilege, non-root accounts and confined containers; verify checkpoint hashes and provenance against an internal registry before use; monitor loader processes for anomalous child processes, file writes, and network egress; and treat any host that has loaded an unverified checkpoint as potentially compromised pending review. Note: the CVSS assessment is disputed - the vendor vector indicates low privileges with no user interaction (PR:L/UI:N) while the independent assessment indicates no privileges with required victim interaction (PR:N/UI:R); no public exploit code was identified at time of analysis.

Threat Intelligence Overview

EPSS Score
0.2%
Top 87% exploitable
Prevalence
Medium
popular NVIDIA LLM-training framework, common in ML/research orgs but not everywhere
Data Quality
6 sources
Confidence: High
EUVD NVD CVE.org EPSS Vulnrichment GitHub VulDB

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity HIGH
CISA KEV No
Public POC No
EPSS 0.2%
Patch available No
Active threat No
Deciding rule
HIGH severity (CVSS 7.0-8.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision
Scheduled
Exploitation
None
System Exposure· derived
Small
Automatable
No
Human Impact
High
Technical Impact
Total

Priority Score

39
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +39
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Insecure deserialization occurs when an application converts serialized data (a stream of bytes representing an object's state) back into a living object without proper validation.

Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.

Remote Code Execution represents the critical moment when an attacker successfully runs arbitrary code on a target system without physical access.

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