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Vulnerability Database/CVE-2026-61760

CVE-2026-61760: Nvidia Nemo Megatron Bridge RCE Vulnerability

CVE-2026-61760 is a deserialization vulnerability in Nvidia Nemo Megatron Bridge that enables remote code execution, data tampering, and information disclosure. This article covers technical details, affected versions, and mitigation.

Published:

CVE-2026-61760 Overview

NVIDIA Megatron Bridge contains a deserialization of untrusted data vulnerability [CWE-502]. An attacker with local access and low privileges can supply crafted serialized input to trigger unsafe deserialization inside the Megatron Bridge component. Successful exploitation can lead to arbitrary code execution, data tampering, and information disclosure in the context of the affected process. The flaw affects NVIDIA NeMo Megatron Bridge and requires no user interaction. Because Megatron Bridge is used in AI training and model orchestration pipelines, exploitation can compromise model integrity and expose sensitive training data.

Critical Impact

Local attackers can achieve code execution, tamper with AI training data, and access sensitive information processed by NVIDIA NeMo Megatron Bridge.

Affected Products

  • NVIDIA NeMo Megatron Bridge (all versions prior to the fixed release)
  • Deployments using nvidia:nemo_megatron_bridge component
  • AI training pipelines integrating Megatron Bridge on Linux hosts

Discovery Timeline

  • 2026-09-01 - CVE-2026-61760 published to NVD
  • 2026-09-02 - Last updated in NVD database

Technical Details for CVE-2026-61760

Vulnerability Analysis

The vulnerability is an insecure deserialization flaw classified under [CWE-502]. Megatron Bridge accepts serialized objects from a source under attacker influence and reconstructs them without adequate validation. During object reconstruction, the deserializer instantiates classes and invokes methods defined in the serialized payload. An attacker who can place or influence a malicious serialized artifact reachable by the Bridge process can execute arbitrary code with the privileges of that process.

The attack vector is local, and successful exploitation impacts confidentiality, integrity, and availability. In machine learning environments, code execution inside the training host also enables tampering with checkpoints, gradients, and datasets processed by the Bridge.

Root Cause

The root cause is the use of an unsafe deserialization primitive on data whose origin is not cryptographically verified. Serialization formats such as Python pickle allow arbitrary object graphs, including callable references, to be reconstructed on load. When Megatron Bridge loads a serialized artifact, embedded reducers execute during deserialization, giving the attacker a direct code execution primitive.

Attack Vector

An authenticated local user places a crafted serialized file, checkpoint, or configuration artifact in a path consumed by Megatron Bridge. When the Bridge process loads the object, embedded logic executes in the training or orchestration context. No user interaction is required beyond the normal pipeline workflow. Detailed technical references are available in the NVIDIA product security advisory and the NVD entry for CVE-2026-61760.

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Detection Methods for CVE-2026-61760

Indicators of Compromise

  • Unexpected child processes spawned by Megatron Bridge Python workers, particularly shells or network utilities.
  • Access to .pkl, .pt, or checkpoint files from paths outside sanctioned training storage.
  • Outbound network connections from training hosts to non-approved destinations shortly after checkpoint loads.
  • Modifications to model artifacts, tokenizer files, or dataset shards that do not correlate with training jobs.

Detection Strategies

  • Hunt for Python processes invoking pickle.load, torch.load, or dill.load on files from user-writable directories.
  • Alert on Megatron Bridge processes executing os.system, subprocess.Popen, or opening sockets.
  • Correlate checkpoint file creation events with the identity of the writing user and expected job schedule.

Monitoring Recommendations

  • Enable audit logging on all directories serving as inputs to Megatron Bridge and forward events to a central log store.
  • Track file integrity on model checkpoints, configuration files, and training datasets.
  • Monitor GPU host process trees for deviations from baseline training workflows.

How to Mitigate CVE-2026-61760

Immediate Actions Required

  • Upgrade NVIDIA NeMo Megatron Bridge to the fixed version listed in the NVIDIA security bulletin.
  • Restrict local access to training hosts to trusted operators only, using strong authentication and least privilege.
  • Audit existing checkpoint and artifact directories for untrusted or unexpected files and quarantine anything without a verified origin.

Patch Information

NVIDIA has published details in its product security repository at NVIDIA/product-security 2026/5868. Apply the vendor-supplied update for nvidia:nemo_megatron_bridge and validate the deployed version across all training and inference hosts. Refer to the CVE.org record for CVE-2026-61760 for canonical metadata.

Workarounds

  • Load model checkpoints only from cryptographically signed sources and verify signatures before deserialization.
  • Run Megatron Bridge workers under dedicated low-privilege service accounts with no interactive login rights.
  • Isolate training pipelines in namespaces or containers with restricted filesystem and network egress.
  • Disable ingestion of serialized artifacts from shared or multi-tenant storage until patched.
bash
# Configuration example: restrict checkpoint directory to service account
sudo chown -R megatron-svc:megatron-svc /opt/megatron/checkpoints
sudo chmod 750 /opt/megatron/checkpoints
# Block world-writable inputs
find /opt/megatron -type f -perm -o+w -exec chmod o-w {} \;

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

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